Accurately distinguishing stages of Alzheimer's disease (AD) is crucial for diagnosis and treatment. In this paper, we introduce a stacking classifier method that combines six single classifiers into a stacking classifier. Using brain network models and network metrics, we employ t-tests to identify abnormal brain regions, from which we construct a subnetwork and extract its features to form the training dataset. Our method is then applied to the ADNI (Alzheimer's Disease Neuroimaging Initiative) datasets, categorizing the stages into four categories: Alzheimer's disease, mild cognitive impairment (MCI), mixed Alzheimer's mild cognitive impairment (ADMCI), and healthy controls (HCs). We investigate four classification groups: AD-HCs, AD-MCI, HCs-ADMCI, and HCs-MCI. Finally, we compare the classification accuracy between a single classifier and our stacking classifier, demonstrating superior accuracy with our stacking classifier from a subnetwork-based viewpoint.

Cortico-cortical paired associative stimulation (ccPAS) is a powerful transcranial magnetic stimulation (TMS) protocol thought to rely on Hebbian plasticity and known to strengthen effective connectivity, mainly within frontal lobe networks. Here, we expand on previous work by exploring the effects of ccPAS on the pathway linking the medial posterior parietal area hV6A with the primary motor cortex (M1), whose plasticity mechanisms remain largely unexplored. To assess the effective connectivity of the hV6A-M1 network, we measured motor-evoked potentials (MEPs) in 30 right-handed volunteers at rest during dual-site, paired-pulse TMS. Consistent with previous findings, we found that MEPs were inhibited when the conditioning stimulus over hV6A preceded the test stimulus over M1 by 12 ms, highlighting inhibitory hV6A-M1 causal interactions. We then manipulated the hV6A-M1 circuit via ccPAS using different inter-stimulus intervals (ISI) never tested before. Our results revealed a time-dependent modulation. Specifically, only when the conditioning stimulus preceded the test one by 12 ms did we find a gradual increase of MEP amplitude during ccPAS, and excitatory aftereffects. In contrast, when ccPAS was applied with an ISI of 4 ms or 500 ms, no corticospinal excitability changes were observed, suggesting that temporal specificity is a critical factor in modulating the hV6A-M1 network. These results suggest that ccPAS can induce time-dependent Hebbian plasticity in the dorsomedial parieto-frontal network at rest, offering novel insights into the network's plasticity and temporal dynamics.

The ketogenic diet (KD) is an effective alternative therapy for drug-resistant epilepsy (DRE). However, there are no established predictors for KD effectiveness. We aimed to investigate the impact of 12 months of KD therapy (KDT) on brain connectivity, as measured by functional magnetic resonance imaging (fMRI), and its correlation with seizure control, behavioral/mood alterations, and parental stress.

Children with DRE were enrolled in this single-center, prospective cohort study from February 2020 to October 2021. They were divided into a control group and a KDT group. The Child Behavior Checklist (CBCL) and Parental Stress Index (PSI) were administered to parents at the initiation of KDT (T0) and at 12 months (T1). Resting-state fMRI was performed at T0 and at 6 months of KDT. The primary outcome was the between-group difference in the change of CBCL/PSI scores, and brain connectivity metrics after KDT, and the secondary outcome involved measuring their correlation with seizure reduction rates.

Twenty-two patients with DRE were enrolled. We had 13 patients in the control group and 9 in the KDT group. Our data revealed that 12 months of KDT can reduce monthly seizure frequency. Several subscales of CBCL T-scores were higher at T0 compared with the control group, then becoming comparable at T1. The PSI scores from 'mothers' reports reduced after receiving KDT. The changes in node assortativity (ΔAssortativity) were positively correlated with behavioral problems and negatively with seizure reduction rates in the KD group.

Twelve months of KDT can reduce monthly seizure frequency and improve mood/behavioral disturbances in patients with DRE. Furthermore, KDT could relieve primary caregivers' stress. A lower ΔAssortativity value was associated with better behavioral outcomes and greater seizure reduction. The ΔAssortativity value in fMRI may be a crucial predictor for the effectiveness of KDT.

The brain is immensely complex, with diverse components and dynamic interactions building upon one another to orchestrate a wide range of behaviors. Understanding patterns of these complex interactions and how they are coordinated to support collective neural function is critical for parsing human and animal behavior, treating mental illness, and developing artificial intelligence. Rapid experimental advances in imaging, recording, and perturbing neural systems across various species now provide opportunities to distill underlying principles of brain organization and function. Here, we take stock of recent progress and review methods used in the statistical analysis of brain networks, drawing from fields of statistical physics, network theory, and information theory. Our discussion is organized by scale, starting with models of individual neurons and extending to large-scale networks mapped across brain regions. We then examine organizing principles and constraints that shape the biological structure and function of neural circuits. We conclude with an overview of several critical frontiers, including expanding current models, fostering tighter feedback between theory and experiment, and leveraging perturbative approaches to understand neural systems. Alongside these efforts, we highlight the importance of contextualizing their contributions by linking them to formal accounts of explanation and causation.

The hierarchical modular functional structure in the human brain has not been adequately depicted by conventional functional magnetic resonance imaging (fMRI) acquisition techniques and traditional functional connectivity reconstruction methods. Fortunately, rapid advancements in fMRI scanning techniques and deep learning methods open a novel frontier to map the spatial hierarchy within Brain Connectivity Networks (BCNs). The novel multiband multi-echo (MBME) fMRI technique has increased spatiotemporal resolution and peak functional sensitivity, while the advanced deep linear model (multilayer-stacked) named DEep Linear Matrix Approximate Reconstruction (DELMAR) enables the identification of hierarchical features without extensive hyperparameter tuning. We incorporate a multi-echo blood oxygenation level-dependent (BOLD) signal and DELMAR for denoising in its first layer, thereby eliminating the need for a separate multi-echo independent component analysis (ME-ICA) denoising step. Our results demonstrate that the DELMAR/Denoising/Mapping strategy produces more accurate and reproducible hierarchical BCNs than traditional ME-ICA denoising followed by DELMAR. Additionally, we showcase that MBME fMRI outperforms multiband (MB) fMRI in terms of hierarchical BCN mapping accuracy and precision. These reproducible spatial hierarchies in BCNs have significant potential for developing improved fMRI diagnostic and prognostic biomarkers of functional connectivity across a wide range of neurological and psychiatric disorders.

The review explores the complex interplay between brain structures and their associated functions, presenting a diversity of hierarchical models that enhances our understanding of these relationships. Central to this approach are structure-function flow diagrams, which offer a visual representation of how specific neuroanatomical structures are linked to their functional roles. These diagrams are instrumental in mapping the intricate connections between different brain regions, providing a clearer understanding of how functions emerge from the underlying neural architecture. The study details innovative attempts to develop new functional hierarchies that integrate structural and functional data. These efforts leverage recent advancements in neuroimaging techniques such as fMRI, EEG, MEG, and PET, as well as computational models that simulate neural dynamics. By combining these approaches, the study seeks to create a more refined and dynamic hierarchy that can accommodate the brain's complexity, including its capacity for plasticity and adaptation. A significant focus is placed on the overlap of structures and functions within the brain. The manuscript acknowledges that many brain regions are multifunctional, contributing to different cognitive and behavioral processes depending on the context. This overlap highlights the need for a flexible, non-linear hierarchy that can capture the brain's intricate functional landscape. Moreover, the study examines the interdependence of these functions, emphasizing how the loss or impairment of one function can impact others. Another crucial aspect discussed is the brain's ability to compensate for functional deficits following neurological diseases or injuries. The investigation explores how the brain reorganizes itself, often through the recruitment of alternative neural pathways or the enhancement of existing ones, to maintain functionality despite structural damage. This compensatory mechanism underscores the brain's remarkable plasticity, demonstrating its ability to adapt and reconfigure itself in response to injury, thereby ensuring the continuation of essential functions. In conclusion, the study presents a system of brain functions that integrates structural, functional, and dynamic perspectives. It offers a robust framework for understanding how the brain's complex network of structures supports a wide range of cognitive and behavioral functions, with significant implications for both basic neuroscience and clinical applications.

The current study aimed to investigate brain network abnormalities in glioma-related epilepsy (gre) patients through high-density electroencephalography (eeg) data analysis.

The study included 35 patients with newly diagnosed frontal gliomas. All participants underwent 128-channel resting-state EEG recordings before surgery. Afterward, graph theory and microstate analyses were performed, and the resulting metrics were compared between patients with GRE and those without GRE.

The network topology analysis demonstrated that the GRE group had a higher clustering coefficient, global efficiency, and local efficiency; a lower characteristic path length; and a higher small-worldness coefficient than the non-GRE group (adjusted p < 0.05 for all). Additionally, the microstate analysis indicated that the GRE group had lower occurrence and global explained variance of microstate E and higher global explained variance of microstate D (adjusted p < 0.05 for all). Moreover, the occurrence of microstate D was significantly negatively correlated with the maximum tumor diameter in the non-GRE group (r = -0.542, p = 0.009).

The current study revealed specific brain network abnormalities in GRE patients based on graph theory and microstate analyses of resting-state high-density EEG data. These findings can enhance our comprehension of the mechanisms behind GRE and offer potential biomarkers for improving individualized management of glioma patients.

Translational network neuroscience aims to integrate advanced neuroimaging and data analysis techniques into clinical practice to better understand and treat neurological disorders. Despite the promise of technologies such as functional MRI and diffusion MRI combined with network analysis tools, the field faces several challenges that hinder its swift clinical translation. We have identified nine key roadblocks that impede this process: (a) theoretical and basic science foundations; (b) network construction, data interpretation, and validation; (c) MRI access, data variability, and protocol standardization; (d) data sharing; (e) computational resources and expertise; (f) interdisciplinary collaboration; (g) industry collaboration and commercialization; (h) operational efficiency, integration, and training; and (i) ethical and legal considerations. To address these challenges, we propose several possible solution strategies. By aligning scientific goals with clinical realities and establishing a sound ethical framework, translational network neuroscience can achieve meaningful advances in personalized medicine and ultimately improve patient care. We advocate for an interdisciplinary commitment to overcoming translational hurdles in network neuroscience and integrating advanced technologies into routine clinical practice.

Understanding the large-scale information processing that underlies complex human cognition is the central goal of cognitive neuroscience. While emerging activity flow models demonstrate that cognitive task information is transferred by interregional functional or structural connectivity, graph-theory-based models typically assume that neural communication occurs via the shortest path of brain networks. However, whether the shortest path is the optimal route for empirical cognitive information transmission remains unclear. Based on a large-scale activity flow mapping framework, we found that the performance of activity flow prediction with the shortest path was significantly lower than that with the direct path. The shortest path routing was superior to other network communication strategies, including search information, path ensembles, and navigation. Intriguingly, the shortest path outperformed the direct path in activity flow prediction when the physical distance constraint and asymmetric routing contribution were simultaneously considered. This study not only challenges the shortest path assumption through empirical network models but also suggests that cognitive task information routing is constrained by the spatial and functional embedding of the brain network.

We investigated the relationship of piriform cortex (PC) structural network centrality with drug resistance and epilepsy duration as markers of sustained epileptic activity.

PCs were manually delineated on retrospectively collected 3D-T1-MRI images of patients with temporal lobe epilepsy (TLE). Connectomes were computed from diffusion MRI scans, including the PC as network nodes. Betweenness centrality (BC) and node degree were computed and compared across drug-resistant versus drug-sensitive patients. Correlations of centrality metrics with the duration of epilepsy were calculated.

Sixty-two patients (36 females, 43/62 drug-resistant) were included in the main analysis. Greater centrality of the left PC was associated with drug resistance (degree: p = 0.00696, d = 0.85; BC: p = 0.00859, d = 0.59; alpha = 0.0125). Furthermore, left PC centrality was correlated with epilepsy duration (degree: rho = 0.39, p = 0.00181; BC: rho = 0.35, p = 0.0047; alpha = 0.0125). Results were robust to analysis of different parcellation schemes. Exploratory whole-network analysis yielded the largest effects in the left PC. Finer parcellations showed stronger effects for both analyses in the left olfactory cortex rostral to PC. In 28 subjects who had received epilepsy surgery, a trend of smaller centrality in patients with ILAE I outcome was observed in this area.

We demonstrated an increased centrality of the left PC in patients with drug-resistant TLE, which was also associated with the epilepsy duration. Recurring seizures over long periods may lead to changes of network properties of the PC. Large effects immediately rostral to our delineated PC region suggest a role of olfactory cortex anterior to the limen insulae in epileptogenic networks.

Identifying biomarkers for serious mental illnesses (SMI) has significant implications for prevention and early intervention. In the current study, changes in whole brain structural and functional connectomes were investigated in youth at transdiagnostic risk over a one-year period. Based on clinical assessments, participants were assigned to one of 5 groups: healthy controls (HC; n = 33), familial risk for serious mental illness (stage 0; n = 31), mild symptoms (stage 1a; n = 37), attenuated syndromes (stage 1b; n = 61), or discrete disorder (transition; n = 9). Constrained spherical deconvolution was used to generate whole brain tractography maps, which were then used to calculate connectivity matrices for graph theory analysis. Graph theory was also used to analyze correlations of functional magnetic resonance imaging (fMRI) signal between pairs of brain regions. Linear mixed models revealed structural and functional abnormalities in global metrics of small world lambda, and resting state networks involving the fronto-parietal, default mode, and deep grey matter networks, along with the visual and dorsal attention networks. Machine learning analysis additionally identified changes in nodal metrics of betweenness centrality in the angular gyrus and bilateral temporal gyri as potential features which can discriminate between the groups. Our findings further support the view that abnormalities in large scale networks (particularly those involving fronto-parietal, temporal, default mode, and deep grey matter networks) may underlie transdiagnostic risk for SMIs.

Functional connectivity holds promise as a biomarker of schizophrenia. Yet, the high dimensionality of predictive models trained on functional connectomes, combined with small sample sizes in clinical research, increases the risk of overfitting. Recently, low-dimensional representations of the connectome such as macroscale cortical gradients and gradient dispersion have been proposed, with studies noting consistent gradient and dispersion differences in psychiatric conditions. However, it is unknown which of these derived measures has the highest predictive capacity and how they compare to raw functional connectivity specifically in the case of schizophrenia. Our study evaluates which connectome features derived from resting state functional MRI - functional connectivity, gradients, or gradient dispersion - best identify schizophrenia. To this end, we leveraged data of 936 individuals from three large open-access datasets: COBRE, LA5c, and SRPBS-1600. We developed a pipeline which allows us to aggregate over a million different features and assess their predictive potential in a single, computationally efficient experiment. We selected top 1% of features with the largest permutation feature importance and trained 13 classifiers on them using 10-fold cross-validation. Our findings indicate that functional connectivity outperforms its low-dimensional derivatives such as cortical gradients and gradient dispersion in identifying schizophrenia (Mann-Whitney test conducted on test accuracy: connectivity vs. 1st gradient: U = 142, p < 0.003; connectivity vs. neighborhood dispersion: U = 141, p = 0.004). Additionally, we demonstrated that the edges which contribute the most to classification performance are the ones connecting primary sensory regions. Functional connectivity within the primary sensory regions showed the highest discrimination capabilities between subjects with schizophrenia and neurotypical controls. These findings along with the feature selection pipeline proposed here will facilitate future inquiries into the prediction of schizophrenia subtypes and transdiagnostic phenomena.

Scientific discovery in connectomics relies on network null models. The prominence of network features is conventionally evaluated against null distributions estimated using randomized networks. Modern imaging technologies provide an increasingly rich array of biologically meaningful edge weights. Despite the prevalence of weighted graph analysis in connectomics, randomization models that only preserve binary node degree remain most widely used. Here we propose a simulated annealing procedure for generating randomized networks that preserve weighted degree (strength) sequences. We show that the procedure outperforms other rewiring algorithms and generalizes to multiple network formats, including directed and signed networks, as well as diverse real-world networks. Throughout, we use morphospace representation to assess the sampling behavior of the algorithm and the variability of the resulting ensemble. Finally, we show that accurate strength preservation yields different inferences about brain network organization. Collectively, this work provides a simple but powerful method to analyze richly detailed next-generation connectomics datasets.

This study aimed to investigate the topological properties of brain functional networks in patients with tinnitus of varying durations. A total of 51 tinnitus patients (divided into recent-onset tinnitus (ROT) and persistent tinnitus (PT) groups) and 27 healthy controls (HC) were recruited. All participants underwent resting-state functional MRI and audiological assessments. Graph theory was used to examine brain network topology. The results showed that the ROT group exhibited lower clustering coefficient, gamma, sigma and local efficiency compared to both the HC and PT groups (all P < 0.05). Significant reductions in nodal clustering coefficient and local efficiency were found in the left caudate nucleus and left olfactory cortex, while increased nodal centralities were observed in the left orbital middle frontal gyrus and left postcentral gyrus in ROT patients (all P < 0.05). Furthermore, the ROT group had decreased nodal clustering in the right lenticular putamen and reduced nodal efficiency in the left olfactory cortex compared to both PT patients and HCs (all P < 0.05). Additionally, PT patients showed weaker functional connectivity between the subcortical and occipital lobe modules, as well as between the prefrontal and intra-frontal modules, compared to ROT patients. However, intra-module connectivity in the subcortical module was stronger in PT patients than in HCs. These findings suggest that recent-onset tinnitus is associated with alterations in brain network topology, but many of these changes are restored with the persistence of tinnitus.

This article considers the problem of classifying individuals in a dataset of diverse psychosis spectrum conditions, including persons with subsyndromal psychotic-like experiences (PLEs) and healthy controls. This task is more challenging than the traditional problem of distinguishing patients with a diagnosed disorder from controls using brain network features, since the neurobiological differences between PLE individuals and healthy persons are less pronounced. Further, examining a transdiagnostic sample compared to controls is concordant with contemporary approaches to understanding the full spectrum of neurobiology of psychoses. We consider both support vector machines (SVMs) and graph convolutional networks (GCNs) for classification, with a variety of edge selection methods for processing the inputs. We also employ the MultiVERSE algorithm to generate network embeddings of the functional and structural networks for each subject, which are used as inputs for the SVMs. The best models among SVMs and GCNs yielded accuracies >63%. Investigation of network connectivity between persons with PLE and controls identified a region within the right inferior parietal cortex, called the PGi, as a central region for communication among modules (network hub). Class activation mapping revealed that the PLE group had salient regions in the dorsolateral prefrontal, orbital and polar frontal cortices, and the lateral temporal cortex, whereas the controls did not. Our study demonstrates the potential usefulness of deep learning methods to distinguish persons with subclinical psychosis and diagnosable disorders from controls. In the long term, this could help improve accuracy and reliability of clinical diagnoses, provide neurobiological bases for making diagnoses, and initiate early intervention strategies.

This study explored how the human cortical folding pattern composed of convex gyri and concave sulci affected single-subject morphological brain networks, which are becoming an important method for studying the human brain connectome. We found that gyri-gyri networks exhibited higher morphological similarity, lower small-world parameters, and lower long-term test-retest reliability than sulci-sulci networks for cortical thickness- and gyrification index-based networks, while opposite patterns were observed for fractal dimension-based networks. Further behavioral association analysis revealed that gyri-gyri networks and connections between gyral and sulcal regions significantly explained inter-individual variance in Cognition and Motor domains for fractal dimension- and sulcal depth-based networks. Finally, the clinical application showed that only sulci-sulci networks exhibited morphological similarity reductions in major depressive disorder for cortical thickness-, fractal dimension-, and gyrification index-based networks. Taken together, these findings provide novel insights into the constraint of the cortical folding pattern to the network organization of the human brain.

The brain's diverse intrinsic timescales enable us to perceive stimuli with varying temporal persistency. This study aimed to uncover the cortical organizational schemes underlying these variations, revealing the neural architecture for processing a wide range of sensory experiences. We collected resting-state fMRI, task-fMRI, and diffusion-weighted imaging data from 47 individuals. Based on this data, we extracted six organizational schemes: (1) the structural Rich Club (RC) architecture, shown to synchronize the connectome; (2) the structural Diverse Club architecture, as an alternative to the RC based on the network's module structure; (3) the functional uni-to-multimodal gradient, reflected in a wide range of structural and functional features; and (4) the spatial posterior/lateral-to-anterior/medial gradient, established for hierarchical levels of cognitive control. Also, we explored the effects of (5) structural graph theoretical measures of centrality and (6) cytoarchitectural differences. Using Bayesian model comparison, we contrasted the impact of these organizational schemes on (1) intrinsic resting-state timescales and (2) inter-subject correlation (ISC) from a task involving hierarchically nested digit sequences. As expected, resting-state timescales were slower in structural network hubs, hierarchically higher areas defined by the functional and spatial gradients, and thicker cortical regions. ISC analysis demonstrated hints for the engagement of higher cortical areas with more temporally persistent stimuli. Finally, the model comparison identified the uni-to-multimodal gradient as the best organizational scheme for explaining the chronotopy in both task and rest. Future research should explore the microarchitectural features that shape this gradient, elucidating how our brain adapts and evolves across different modes of processing.

Network control theory (NCT) is a simple and powerful tool for studying how network topology informs and constrains the dynamics of a system. Compared to other structure-function coupling approaches, the strength of NCT lies in its capacity to predict the patterns of external control signals that may alter the dynamics of a system in a desired way. An interesting development for NCT in the neuroscience field is its application to study behavior and mental health symptoms. To date, NCT has been validated to study different aspects of the human structural connectome. NCT outputs can be monitored throughout developmental stages to study the effects of connectome topology on neural dynamics and, separately, to test the coherence of empirical datasets with brain function and stimulation. Here, we provide a comprehensive pipeline for applying NCT to structural connectomes by following two procedures. The main procedure focuses on computing the control energy associated with the transitions between specific neural activity states. The second procedure focuses on computing average controllability, which indexes nodes' general capacity to control the dynamics of the system. We provide recommendations for comparing NCT outputs against null network models, and we further support this approach with a Python-based software package called 'network control theory for python'. The procedures in this protocol are appropriate for users with a background in network neuroscience and experience in dynamical systems theory.

Bridging the gap between cortical morphometric remodeling and gene expression can help to clarify the effects of the selective brain accumulation of Amyloid-β (Aβ) and tau proteins occurring in the Alzheimer's disease (AD). To this aim, we derived morphometric similarity (MS) networks from 126 Aβ- and tau-positive (Aβ+/tau+) and 172 Aβ-/tau- subjects, and we investigated the association between group-wise regional MS differences and transcriptional correlates thanks to an imaging transcriptomics approach grounded in the Allen Human Brain Atlas (AHBA). The expressed gene with the highest correlation with MS alterations was BCHE, a gene related to Aβ homeostasis. In addition, notably, among the most promising results derived from the enrichment analysis, we found the immune response to be a biological process and astrocytes, microglia, and oligodendrocyte precursors for the cell types. In summary, by relating cortical MS and AHBA-derived transcriptomics, we were able to retrieve findings suggesting the biological mechanisms underlying the Aβ- and tau- induced cortical MS alterations in the AD continuum.

Identifying relationships between structural and functional networks is crucial for understanding the large-scale organization of the human brain. The potential contribution of emerging techniques like functional near-infrared spectroscopy to investigate the structure-functional relationship has yet to be explored. In our study, using simultaneous Electroencephalography (EEG) and Functional near-infrared spectroscopy (fNIRS) recordings from 18 subjects, we characterize global and local structure-function coupling using source-reconstructed EEG and fNIRS signals in both resting state and motor imagery tasks, as this relationship during task periods remains underexplored. Employing the mathematical framework of graph signal processing, we investigate how this relationship varies across electrical and hemodynamic networks and different brain states. Results show that fNIRS structure-function coupling resembles slower-frequency EEG coupling at rest, with variations across brain states and oscillations. Locally, the relationship is heterogeneous, with greater coupling in the sensory cortex and increased decoupling in the association cortex, following the unimodal to transmodal gradient. Discrepancies between EEG and fNIRS are noted, particularly in the frontoparietal network. Cross-band representations of neural activity revealed lower correspondence between electrical and hemodynamic activity in the transmodal cortex, irrespective of brain state while showing specificity for the somatomotor network during a motor imagery task. Overall, these findings initiate a multimodal comprehension of structure-function relationship and brain organization when using affordable functional brain imaging.

Altered structural brain development has been identified in fetuses with congenital heart disease (CHD), suggesting that the neurodevelopmental impairment observed later in life might originate in utero. There are many interacting factors that may perturb neurodevelopment during the fetal period and manifest as structural brain alterations, such as altered cerebral substrate delivery and aberrant fetal hemodynamics.

We extracted structural covariance networks from the log Jacobian determinants of 435 in utero T2 weighted image magnetic resonance imaging scans, (n=67 controls, 368 with CHD) acquired during the third trimester. We fit general linear models to test whether age, sex, expected cerebral substrate delivery, and CHD diagnosis were significant predictors of structural covariance. We identified significant effects of age, sex, cerebral substrate delivery, and specific CHD diagnosis across a variety of structural covariance networks, including primary motor and sensory cortices, cerebellar regions, frontal cortex, extra-axial cerebrospinal fluid, thalamus, brainstem, and insula, consistent with widespread coordinated aberrant maturation of specific brain regions over the third trimester.

Structural covariance networks offer a sensitive, data-driven approach to explore whole-brain structural changes without anatomical priors. We used them to stratify a heterogenous patient cohort with CHD, highlighting similarities and differences between diagnoses during fetal neurodevelopment. Although there was a clear effect of abnormal fetal hemodynamics on structural brain maturation, our results suggest that this alone does not explain all the variation in brain development between individuals with CHD.

The human brain undergoes rapid changes from the fetal stage to two years postnatally, during which proper structural and functional maturation lays the foundation for later cognitive and behavioral development. Multimodal magnetic resonance imaging (MRI) techniques, especially structural MRI (sMRI), diffusion MRI (dMRI), functional MRI (fMRI), and perfusion MRI (pMRI), provide unprecedented opportunities to non-invasively quantify these early brain changes at whole brain and regional levels. Each modality offers unique insights into the complex processes of both typical neurodevelopment and the pathological mechanisms underlying psychiatric and neurological disorders. Compared to a single modality, multimodal MRI enhances discriminative power and provides more comprehensive insights for understanding and improving neurodevelopmental and mental health outcomes, particularly in high-risk populations. Machine learning- and deep learning-based methods have demonstrated significant potential for predicting future outcomes using multimodal brain MRI acquired during early childhood. Here, we review the unique characteristics of various MRI techniques for imaging early brain development and describe the common approaches to analyze these modalities. We then discuss machine learning approaches in predicting future neurodevelopmental and clinical outcomes using multimodal MRI information during early childhood, highlighting the potential of identifying biomarkers for early detection and personalized interventions in atypical development.

It is known that abnormal functional connectivity (FC) in schizophrenia (SZ) is closely related to structural connectivity (SC). We speculate that indirect SC also have an impact on FC in SZ patients. Conventional single-layer network has limitations for studying the relationship between indirect SC and FC. Thus, this study constructed a multiplex network based on structural connectivity and functional connectivity (SC-FC). The SC-FC bandwidth and SC-FC cost are used to analyze the impact of indirect SC on FC. Moreover, this paper proposed mediation ability, mediation cost, mediated strength and mediated cost to quantify the effects of mediator nodes and mediated nodes on indirect SC. The results show that SZ patients exhibit lower SC-FC bandwidth and SC-FC cost compared to healthy controls (HC), which could be caused by the limbic and subcortical network (LSN), default mode network (DMN) and visual network (VN). The mediator and mediated nodes in indirect SC of SZ patients also showed diminished effects. These findings suggest that functional communication ability and cost in SZ patients are influenced by indirect SC. This study provides new perspectives for understanding the relationship between indirect SC and FC, and provides strong evidence for interpreting the physiological mechanisms of SZ patients.

Prior studies have shown strong genetic effects on cortical thickness (CT), structural covariance, and neurodevelopmental trajectories in childhood and adolescence. However, the importance of genetic factors on the induction of spatiotemporal variation during neurodevelopment remains poorly understood. Here, we explore the genetics of maturational coupling by examining 308 MRI-derived regional CT measures in a longitudinal sample of 677 twins and family members. We find dynamic inter-regional genetic covariation in youth, with the emergence of regional subnetworks in late childhood and early adolescence. Three critical neurodevelopmental epochs in genetically-mediated maturational coupling were identified, with dramatic network strengthening near eleven years of age. These changes are associated with statistically-significant (empirical p-value <0.0001) increases in network strength as measured by average clustering coefficient and assortativity. We then identify genes from the Allen Human Brain Atlas with similar co-expression patterns to genetically-mediated structural covariation in children. This set was enriched for genes involved in potassium transport and dendrite formation. Genetically-mediated CT-CT covariance was also strongly correlated with expression patterns for genes located in cells of neuronal origin.

Attention-deficit/hyperactivity disorder (ADHD) is among the most prevalent, inheritable, and heterogeneous childhood-onset neurodevelopmental disorders. Children with a hereditary background of ADHD have heightened risk of having ADHD and persistent impairment symptoms into adulthood. These facts suggest distinct familial-specific neuropathological substrates in ADHD that may exist in anatomical components subserving attention and cognitive control processing pathways during development. The objective of this study is to investigate the topological properties of the gray matter (GM) structural brain networks in children with familial ADHD (ADHD-F), non-familial ADHD (ADHD-NF), as well as matched controls. A total of 452 participants were involved, including 132, 165 and 155 in groups of ADHD-F, ADHD-NF and typically developed children, respectively. The GM structural brain network was constructed for each group using graph theoretical techniques with cortical and subcortical structures as nodes and correlations between volume of each pair of the nodes within each group as edges, while controlled for confounding factors using regression analysis. Relative to controls, children in both ADHD-F and ADHD-NF groups showed significantly higher nodal global and nodal local efficiencies in the left caudal middle frontal gyrus. Compared to controls and ADHD-NF, children with ADHD-F showed distinct structural network topological patterns associated with right precuneus (significantly higher nodal global efficiency and significantly higher nodal strength), left paracentral gyrus (significantly higher nodal strength and trend toward significantly higher nodal local efficiency) and left putamen (significantly higher nodal global efficiency and trend toward significantly higher nodal local efficiency). Our results for the first time in the field provide evidence of familial-specific structural brain network alterations in ADHD, that may contribute to distinct clinical/behavioral symptomology and developmental trajectories in children with ADHD-F.

Schizophrenia is a complex and serious brain disorder. Neuroscientists have become increasingly interested in using magnetic resonance-based brain imaging-derived phenotypes (IDPs) to investigate the etiology of psychiatric disorders. IDPs capture valuable clinical advantages and hold biological significance in identifying brain abnormalities. In this review, we aim to discuss current and prospective approaches to identify potential biomarkers for schizophrenia using clinical multimodal neuroimaging and imaging genetics. We first described IDPs through their phenotypic classification and neuroimaging genomics. Secondly, we discussed the applications of multimodal neuroimaging by clinical evidence in observational studies and randomized controlled trials. Thirdly, considering the genetic evidence of IDPs, we discussed how can utilize neuroimaging data as an intermediate phenotype to make association inferences by polygenic risk scores and Mendelian randomization. Finally, we discussed machine learning as an optimum approach for validating biomarkers. Together, future research efforts focused on neuroimaging biomarkers aim to enhance our understanding of schizophrenia.

Several studies have aimed at identifying biomarkers in the initial phases of Alzheimer's disease (AD). Conversely, texture features, such as those from gray-level co-occurrence matrices (GLCMs), have highlighted important information from several types of medical images. More recently, texture-based brain networks have been shown to provide useful information in characterizing healthy individuals. However, no studies have yet explored the use of this type of network in the context of AD. This work aimed to employ texture brain networks to investigate the distinction between groups of patients with amnestic mild cognitive impairment (aMCI) and mild dementia due to AD, and a group of healthy subjects. Magnetic resonance (MR) images from the three groups acquired at two instances were used. Images were segmented and GLCM texture parameters were calculated for each region. Structural brain networks were generated using regions as nodes and the similarity among texture parameters as links, and graph theory was used to compute five network measures. An ANCOVA was performed for each network measure to assess statistical differences between groups. The thalamus showed significant differences between aMCI and AD patients for four network measures for the right hemisphere and one network measure for the left hemisphere. There were also significant differences between controls and AD patients for the left hippocampus, right superior parietal lobule, and right thalamus-one network measure each. These findings represent changes in the texture of these regions which can be associated with the cortical volume and thickness atrophies reported in the literature for AD. The texture networks showed potential to differentiate between aMCI and AD patients, as well as between controls and AD patients, offering a new tool to help understand these conditions and eventually aid early intervention and personalized treatment, thereby improving patient outcomes and advancing AD research.

Networks involved in information processing often have their nodes arranged hierarchically, with the majority of connections occurring in adjacent levels. However, despite being an intuitively appealing concept, the hierarchical organization of large networks, such as those in the brain, is difficult to identify, especially in absence of additional information beyond that provided by the connectome. In this paper, we propose a framework to uncover the hierarchical structure of a given network, that identifies the nodes occupying each level as well as the sequential order of the levels. It involves optimizing a metric that we use to quantify the extent of hierarchy present in a network. Applying this measure to various brain networks, ranging from the nervous system of the nematode Caenorhabditis elegans to the human connectome, we unexpectedly find that they exhibit a common network architectural motif intertwining hierarchy and modularity. This suggests that brain networks may have evolved to simultaneously exploit the functional advantages of these two types of organizations, viz., relatively independent modules performing distributed processing in parallel and a hierarchical structure that allows sequential pooling of these multiple processing streams. An intriguing possibility is that this property we report may be common to information processing networks in general.

Parkinson's disease (PD) is characterized by loss of selectively vulnerable neurons within the basal ganglia circuit and progressive atrophy in subcortical and cortical regions. However, the impact of neurodegenerative pathology on the topological organization of cortical morphological networks has not been explored. The aims of this study were to investigate altered network patterns of covariance in cortical thickness and complexity, and to evaluate how morphological network integrity in PD is related to motor impairment.

Individual morphological networks were constructed for 50 PD patients and 46 healthy controls (HCs) by estimating interregional similarity distributions in surface-based indices. We performed graph theoretical analysis and network-based statistics to detect PD-related alterations and further examined the correlation of network metrics with clinical scores. Furthermore, support vector regression based on topological characteristics was applied to predict the severity of motor impairment in PD.

Compared with HCs, PD patients showed lower local efficiency (p = 0.004), normalized characteristic path length (p = 0.022), and clustering coefficient (p = 0.005) for gyrification index-based morphological brain networks. Nodal topological abnormalities were mainly in the frontal, parietal and temporal regions, and impaired morphological connectivity was involved in the sensorimotor and default mode networks. The support vector regression model using network-based features allowed prediction of motor symptom severity with a correlation coefficient of 0.606.

This study identified a disrupted topological organization of cortical morphological networks that could substantially advance our understanding of the network degeneration mechanism of PD and might offer indicators for monitoring disease progression.

Current techniques in brain stimulation are still largely based on a phrenologic approach that a single brain target can treat a brain disorder. Nevertheless, meta-analyses of brain implants indicate an overall success rate of 50% improvement in 50% of patients, irrespective of the brain-related disorder. Thus, there is still a large margin for improvement. The goal of this manuscript is to 1) develop a general theoretical framework of brain functioning that is amenable to surgical neuromodulation, and 2) describe the engineering requirements of the next generation of implantable brain stimulators that follow from this theoretic model.

A neuroscience and engineering literature review was performed to develop a universal theoretical model of brain functioning and dysfunctioning amenable to surgical neuromodulation.

Even though a single target can modulate an entire network, research in network science reveals that many brain disorders are the consequence of maladaptive interactions among multiple networks rather than a single network. Consequently, targeting the main connector hubs of those multiple interacting networks involved in a brain disorder is theoretically more beneficial. We, thus, envision next-generation network implants that will rely on distributed, multisite neuromodulation targeting correlated and anticorrelated interacting brain networks, juxtaposing alternative implant configurations, and finally providing solid recommendations for the realization of such implants. In doing so, this study pinpoints the potential shortcomings of other similar efforts in the field, which somehow fall short of the requirements.

The concept of network stimulation holds great promise as a universal approach for treating neurologic and psychiatric disorders.

This paper introduces two novel scores for detecting local perturbations in networks. For this, we consider a non-Euclidean representation of networks, namely, their embedding onto the Poincaré disk model of hyperbolic geometry. We numerically evaluate the performances of these scores for the detection and localization of perturbations on homogeneous and heterogeneous network models. To illustrate our approach, we study latent geometric representations of real brain networks to identify and quantify the impact of epilepsy surgery on brain regions. Results suggest that our approach can provide a powerful tool for representing and analyzing changes in brain networks following surgical intervention, marking the first application of geometric network embedding in epilepsy research.

Although several previous studies have used resting-state functional magnetic resonance imaging and diffusion tensor imaging to report topological changes in the brain in epilepsy, it remains unclear whether the individual structural covariance network (SCN) changes in epilepsy, especially in pediatric epilepsy with visual cortex resection but with normal functions. Herein, individual SCNs were mapped and analyzed for seven pediatric patients with epilepsy after surgery and 15 age-matched healthy controls. A whole-brain individual SCN was constructed based on an automated anatomical labeling template, and global and nodal network metrics were calculated for statistical analyses. Small-world properties were exhibited by pediatric patients after brain surgery and by healthy controls. After brain surgery, pediatric patients with epilepsy exhibited a higher shortest path length, lower global efficiency, and higher nodal efficiency in the cuneus than those in healthy controls. These results revealed that pediatric epilepsy after brain surgery, even with normal functions, showed altered topological organization of the individual SCNs, which revealed residual network topological abnormalities and may provide initial evidence for the underlying functional impairments in the brain of pediatric patients with epilepsy after surgery that can occur in the future.

The amygdala is divided into functional subnuclei which have been challenging to investigate due to functional magnetic resonance imaging (MRI) limitations in mapping small neural structures. Hence their role in the neurobiology of posttraumatic stress disorder (PTSD) remains poorly understood. Examination of covariance of structural MRI measures could be an alternate approach to circumvent this issue. T1-weighted anatomical scans from a 3 T scanner from non-trauma-exposed controls (NEC; n = 71, 75 % female) and PTSD participants (n = 67, 69 % female) were parcellated into 105 brain regions. Pearson's r partial correlations were computed for three and nine bilateral amygdala subnuclei and every other brain region, corrected for age, sex, and total brain volume. Pairwise correlation comparisons were performed to examine subnuclei covariance profiles between-groups. Graph theory was employed to investigate subnuclei network topology. Volumetric measures were compared to investigate structural changes. We found differences between amygdala subnuclei in covariance with the hippocampus for both groups, and additionally with temporal brain regions for the PTSD group. Network topology demonstrated the importance of the right basal nucleus in facilitating network communication only in PTSD. There were no between-group differences for any of the three structural metrics. These findings are in line with previous work that has failed to find structural differences for amygdala subnuclei between PTSD and controls. However, differences between amygdala subnuclei covariance profiles observed in our study highlight the need to investigate amygdala subnuclei functional connectivity in PTSD using higher field strength fMRI for better spatial resolution.

Aberrant cognitive network activity and cognitive deficits are established features of chronic pain. However, the nature of cognitive network alterations associated with chronic pain and their underlying mechanisms require elucidation. Here, we report that the claustrum, a subcortical nucleus implicated in cognitive network modulation, is activated by acute painful stimulation and pain-predictive cues in healthy participants. Moreover, we discover pathological activity of the claustrum and a region near the posterior inferior frontal sulcus of the right dorsolateral prefrontal cortex (piDLPFC) in migraine patients during acute pain and cognitive task performance. Dynamic causal modeling suggests a directional influence of the claustrum on activity in this piDLPFC region, and diffusion weighted imaging verifies their structural connectivity. These findings advance understanding of claustrum function during acute pain and provide evidence of a possible circuit mechanism driving cognitive impairments in chronic pain.

Mild cognitive impairment (MCI) is recognized as a precursor to Alzheimer's disease (AD), a progressive and irreversible neurodegenerative disorder of the brain. The neurodegeneration of brain connectivity networks plays a pivotal role in the development and progression of MCI. Traditionally, brain networks are generated using coarse-grained brain regions, where the regions serve as nodes and their functional or structural connections are used as edges. Recently, a novel finer scale brain folding patterns named 3-hinge gyrus (3HG) was identified, which is defined as the conjunctions coming from three directions on gyral crests. 3HGs have been shown playing an important role in brain network and can serve as hubs. In this study, our objective is to construct a novel 3HG-based finer-scale brain connectome and comprehensively compare its performance with traditional region-based connectome in predicting MCI against Normal Controls (NC). The results of extensive experiments demonstrate the superior performance of 3HG-based brain connectome, shedding light on the potential of 3HG-based connectomes in capturing intricate neurodegenerative patterns associated with MCI and AD.

Alzheimer's disease is characterized by large-scale structural changes in a specific pattern. Recent studies developed morphological similarity networks constructed by brain regions similar in structural features to represent brain structural organization. However, few studies have used local morphological properties to explore inter-regional structural similarity in Alzheimer's disease.

Here, we sourced T1-weighted MRI images of 342 cognitively normal participants and 276 individuals with Alzheimer's disease from the Alzheimer's Disease Neuroimaging Initiative database. The relationships of grey matter intensity between adjacent voxels were defined and converted to the structural pattern indices. We conducted the information-based similarity method to evaluate the structural similarity of structural pattern organization between brain regions. Besides, we examined the structural randomness on brain regions. Finally, the relationship between the structural randomness and cognitive performance of individuals with Alzheimer's disease was assessed by stepwise regression.

Compared to cognitively normal participants, individuals with Alzheimer's disease showed significant structural pattern changes in the bilateral posterior cingulate gyrus, hippocampus, and olfactory cortex. Additionally, individuals with Alzheimer's disease showed that the bilateral insula had decreased inter-regional structural similarity with frontal regions, while the bilateral hippocampus had increased inter-regional structural similarity with temporal and subcortical regions. For the structural randomness, we found significant decreases in the temporal and subcortical areas and significant increases in the occipital and frontal regions. The regression analysis showed that the structural randomness of five brain regions was correlated with the Mini-Mental State Examination scores of individuals with Alzheimer's disease.

Our study suggested that individuals with Alzheimer's disease alter micro-structural patterns and morphological similarity with the insula and hippocampus. Structural randomness of individuals with Alzheimer's disease changed in temporal, frontal, and occipital brain regions. Morphological similarity and randomness provide valuable insight into brain structural organization in Alzheimer's disease.

Here we aimed to explore the differences in individual gray matter (GM) networks at baseline in mild cognitive impairment patients who converted to Alzheimer's disease (AD) within 3 years (MCI-C) and nonconverters (MCI-NC).

Data from 461 MCI patients (180 MCI-C and 281 MCI-NC) were obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI). For each subject, a GM network was constructed using 3D-T1 imaging and the Kullback-Leibler divergence method. Gradient and topological analyses of individual GM networks were performed, and partial correlations were calculated to evaluate relationships among network properties, cognitive function, and apolipoprotein E (APOE) €4 alleles. Subsequently, a support vector machine (SVM) model was constructed to discriminate the MCI-C and MCI-NC patients at baseline.

The gradient analysis revealed that the principal gradient score distribution was more compressed in the MCI-C group than in the MCI-NC group, with scores for the left lingual gyrus, right fusiform gyrus and left middle temporal gyrus being increased in the MCI-C group (p < 0.05, FDR corrected). The topological analysis showed significant differences in nodal efficiency in four nodes between the two groups. Furthermore, the regional gradient scores or nodal efficiency were found to be significantly related to the neuropsychological test scores, and the left middle temporal gyrus gradient scores were positively associated with the number of APOE €4 alleles (r = 0.192, p = 0.002). Ultimately, the SVM model achieved a balanced accuracy of 79.4% in classifying MCI-C and MCI-NC patients (p < 0.001).

The whole-brain GM network hierarchy in the MCI-C group was more compressed than that in the MCI-NC group, suggesting more serious cognitive impairments in the MCI-C group. The left middle temporal gyrus gradient scores were related to both cognitive function and APOE €4 alleles, thus serving as potential biomarkers distinguishing MCI-C from MCI-NC at baseline.

Functional and structural magnetic resonance imaging (fMRI and sMRI) are complementary approaches that can be used to study longitudinal brain changes in adolescents. Each individual modality offers distinct insights into the brain. Each individual modality may overlook crucial aspects of brain analysis. By combining them, we can uncover hidden brain connections and gain a more comprehensive understanding. In previous work, we identified multivariate patterns of change in whole-brain function during adolescence. In this work, we focus on linking functional change patterns (FCPs) to brain structure. We introduce two approaches and applied them to data from the Adolescent Brain and Cognitive Development (ABCD) dataset. First, we evaluate voxelwise sMRI-FCP coupling to identify structural patterns linked to our previously identified FCPs. Our approach revealed multiple interesting patterns in functional network connectivity (FNC) and gray matter volume (GMV) data that were linked to subject level variation. FCP components 2 and 4 exhibit extensive associations between their loadings and voxel-wise GMV data. Secondly, we leveraged a symmetric multimodal fusion technique called multiset canonical correlation analysis (mCCA) + joint independent component analysis (jICA). Using this approach, we identify structured FCPs such as one showing increased connectivity between visual and sensorimotor domains and decreased connectivity between sensorimotor and cognitive control domains, linked to structural change patterns (SCPs) including alterations in the bilateral sensorimotor cortex. Interestingly, females exhibit stronger coupling between brain functional and structural changes than males, highlighting sex-related differences. The combined results from both asymmetric and symmetric multimodal fusion methods underscore the intricate sex-specific nuances in neural dynamics. By utilizing two complementary multimodal approaches, our study enhances our understanding of the dynamic nature of brain connectivity and structure during the adolescent period, shedding light on the nuanced processes underlying adolescent brain development.