Local structural-functional coupling with counterfactual explanations for epilepsy prediction.

Network science has become an invaluable tool for neuroimaging analysis and has advanced our understanding of the brain's complex structural and functional topology. Network science, an interdisciplinary offshoot of graph theory, conceives of the brain as a system of nodes, representing brain regions or voxels, and edges, representing the structural or functional connection between these regions or voxels. Such a model is considered appealing in neuroscience as it describes a system with various interacting regions that produces complex behaviors. This conception of the brain has revealed that the brain exhibits “small-world” properties, namely that brain networks are systems with regional specialization and efficient global information transfer. More importantly, studies in brain networks have furthered our understanding of diseases and disorders affecting the brain. Assessing group differences in representative samples of brain networks has become increasingly important, yet appropriate methodological developments have remained relatively sparse. Additionally, there is increased interest in understanding dynamic changes, or how a network changes over time. Currently, most network analysis tools are designed to analyze a static representation, thus changes that occur over time are often overlooked or ignored. As the field continues to grow, it is becoming increasingly important to develop methods for understanding the complexity of these data. This research topic features selected contributions that explores group analysis and dynamics in brain network studies including methodological and practical applications employing various imaging modalities. This snapshot of the field represents the growing interest to expand and validate new methods for use in the field of brain network neuroscience. “Defining nodes in complex brain networks” is a review article discussing the methodological choices for defining nodes, particularly how the choice of atlas-based or voxel-based methods affects the computed network (Stanley et al., 2013). “Routing in the brain” is a commentary on network routing focusing on how current methods and models may not be adequate for brain networks. Further exploration of the packet switching network model may be more realistic and appropriate for the brain (Graham, 2014). “A multimodal approach for determining brain networks by jointly modeling functional and structural connectivity” is a methods article introducing a Bayesian model for estimating functional networks, from functional magnetic resonance imaging (fMRI) data, leveraging complementary structural diffusion tensor imaging (DTI) data (Xue et al., 2015). “A permutation testing framework to compare groups of brain networks” is a methods article describing a permutation testing framework to statistically compare groups of functional brain networks while incorporating topological features inherent in each individual network (Simpson et al., 2013). “Assessing dynamics, spatial scale, and uncertainty in task-related brain network analyses” is a methods article describing a statistical approach for computing uncertainty in static and dynamic functional networks and aggregating network measures in task-related electrocorticography (ECoG) data (Stephen et al., 2014). “Hierarchical vector auto-regressive models and their applications to multi-subject effective connectivity” is a methods article introducing a generalization of the vector auto-regressive (VAR) model for comparing effective connectivity between experimental conditions while accounting for between-subject heterogeneity (Gorrostieta et al., 2013). “Statistical network analysis for functional MRI: summary networks and group comparisons” is a review article discussing the construction of summary networks and how to test for topological differences in groups of networks when these groups also exhibit significant differences in density (Ginestet et al., 2014). “Detecting functional connectivity change points for single-subject fMRI data” is a methods article that extends Dynamic Connectivity Regression (DCR), a data-driven technique which detects change points in functional connectivity between brain regions to determine state changes over the course of an experimental task, by introducing a novel algorithm aimed at increasing the estimation of networks for individual subject data (Cribben et al., 2013). “The Laplacian spectrum of neural networks” is an original research article examining the use of the Laplacian spectrum in anatomic networks of the macaque, cat and Caenorhabditis elegans. This method describes network structure at a systems level, assessing systemic infrastructural properties of the entire network as opposed to properties of specific nodes or connections (De Lange et al., 2014). “Quantifying network properties in multi-electrode recordings: spatiotemporal characterization and inter-trial variation of evoked gamma oscillations in mouse somatosensory cortex in vitro” is an original research article on network dynamics in multi-electrode array data focusing on changes in the functional connectivity of evoked gamma oscillations in cortical circuits (Carmeli et al., 2013).

Adolescent MDD has become a significant public health issue, yet its underlying mechanisms remain unclear. Multimodal brain imaging techniques offer a powerful method for exploring complex mental disorders. However, evidence focusing on the multimodal brain networks and structural-functional coupling in adolescent depression is still limited. Participants with major depressive disorder (MDD) were Han Chinese individuals aged 13 to 18 who had been unmedicated for at least two weeks. We conducted multimodal MRI acquisitions, including structural MRI (sMRI), resting-state functional MRI (rsfMRI), and Diffusion Tensor Imaging (DTI). The cortex was parceled into 360 regions using the HCP-MMP atlas. Functional connectivity and deterministic structural connectivity matrices were constructed, and structural-functional coupling coefficients were calculated. Differences in connectivity and coupling coefficients between the MDD and healthy controls (HCs) groups were identified. A total of 25 adolescents with MDD (mean age: 15.68 years, standard deviation [SD]: 1.18; Female: 21 (84.00%)) and 27hCs (mean age: 14.30 years, standard deviation [SD]: 1.51; Female: 13 (48.15%)) were included in the analysis. There were 9 structural connections and 122 functional connections that differed between the two groups, involving multiple cortical regions. Additionally, we identified structural-functional coupling differences in three brain areas, specifically the posterior cingulate cortex and the ventral visual cortex. Adolescent MDD involves disruptions in brain structural networks, functional networks, and structural-functional coupling. These differing indicators may serve as potential biomarkers for adolescent MDD.

It has been widely reported that structural and functional connectivities are disturbed in cortical networks in schizophrenia (SZ). However, much less is known about the structural and functional connectivities between cortical and subcortical regions in SZ. Here, diffusion tensor imaging (DTI) data was used to identify consistent cortico-subcortical structural connection patterns across SZ patients and controls, and thus 13 common cortical Regions of Interest (ROIs) were determined. DTI and resting state fMRI (R-fMRI) datasets were used to assess the structural and functional connectivities between the 13 cortical ROIs and 12 subcortical regions in 8 SZ patients and 10 normal controls. It was found that there are significantly increased functional connectivities for 7 cortico-subcortical connections between the 13 cortical ROIs and 12 subcortical regions. Among most of these connections, the functional connectivity strength was doubled in SZ in comparison to controls. The cortical ROIs with functional hyper-connectivities to subcortical regions are localized in frontal and parietal lobes. However, no significant difference in the structural connectivity between these cortical and subcortical regions was found between SZ and controls. Additional analysis results showed 4 significantly increased and 2 significantly decreased cortico-cortical connections. Our study results suggest the functional hyper-connectivity between cortical and subcortical regions, adding further evidence to literature findings that SZ is a disorder of connectivity between components of large-scale brain networks. The result of either increased or decreased functional connectivities among cortical ROIs exhibits the complex pattern of disturbance of brain networks in SZ.

The human brain is an extremely complex network of structural and functional connections that operate at multiple spatial and temporal scales. Investigating the relationship between these multi-scale connections is critical to advancing our comprehension of brain function and disorders. However, accurately predicting structural connectivity from its functional counterpart remains a challenging pursuit. One of the major impediments is the lack of public repositories that integrate structural and functional networks at diverse resolutions, in conjunction with modular transcriptomic profiles, which are essential for comprehensive biological interpretation. To mitigate this limitation, our contribution encompasses the provision of an open-access dataset consisting of derivative matrices of functional and structural connectivity across multiple scales, accompanied by code that facilitates the investigation of their interrelations. We also provide additional resources focused on neuro-genetic associations of module-level network metrics, which present promising opportunities to further advance research in the field of network neuroscience, particularly concerning brain disorders.

ObjectiveTo investigate the changes in the global attributes of structural connectivity (SC) and functional connectivity (FC) graph theory, as well as the coupling topological structure of the whole brain SC-FC in patients with noise-induced hearing loss (NIHL).Methods57 NIHL patients and 55 health controls (HCs) were included; resting-state functional magnetic resonance imaging, diffusion tensor imaging, clinical data (scales, laboratory indicators) were collected. The graph theory network analysis of SC and FC, the whole-brain SC-FC coupling were performed, and a correlation analysis was employed to analyze the results in relation to the clinical data.ResultsFor FC, there was no significant difference in the global indices directly between groups (P > 0.05, FDR corrected). For SC, the normalized clustering coefficient (γ) and small-worldness (σ) of the NIHL were higher than those of the HCs (P < 0.05, FDR corrected). There was no significant difference in the SC-FC coupling strength of whole brain between two groups (P > 0.05). The graph attribute indices and coupling values of NIHL were correlated to varying degrees with the results of anxiety scale, coagulation, inflammation and biochemical indices (P < 0.05).ConclusionThe brain network topology structure of NIHL patients is abnormal, but the SC-FC coupling strength does not change significantly. This may provide a basis for understanding the theoretical mechanism of brain neural function remodeling and for future more detailed and diverse studies.

Previous invasive studies indicate that human neocortical graymatter contains cytoarchitectonically distinct layers, with notable differences in their structural connectivity with the rest of the brain. Given recent improvements in the spatial resolution of anatomical and functional magnetic resonance imaging (fMRI), we hypothesize that resting state functional connectivity (FC) derived from fMRI is sensitive to layer-specific thalamo-cortical and cortico-cortical microcircuits. Using sub-millimeter resting state fMRI data obtained at 7 T, we found that: (1) FC between the entire thalamus and cortical layers I and VI was significantly stronger than between the thalamus and other layers. Furthermore, FC between somatosensory thalamus (ventral posterolateral nucleus, VPL) and layers IV, VI of the primary somatosensory cortex were stronger than with other layers; (2) Inter-hemispheric cortico-cortical FC between homologous regions in superficial layers (layers I–III) was stronger compared to deep layers (layers V–VI). These findings are in agreement with structural connections inferred from previous invasive studies that showed that: (i) M-type neurons in the entire thalamus project to layer-I; (ii) Pyramidal neurons in layer-VI target all thalamic nuclei, (iii) C-type neurons in the VPL project to layer-IV and receive inputs from layer-VI of the primary somatosensory cortex, and (iv) 80% of collosal projecting neurons between homologous cortical regions connect superficial layers. Our results demonstrate for the first time that resting state fMRI is sensitive to structural connections between cortical layers (previously inferred through invasive studies), specifically in thalamo-cortical and cortico-cortical networks.

Extensive efforts are devoted to understand the functional (FC) and structural connections (SC) of the brain. FC is usually measured by functional magnetic resonance imaging (fMRI), and conceptualized as degree of synchronicity in brain activity between different regions. SC is typically indexed by measures of white matter (WM) properties, e.g. by diffusion weighted imaging (DWI). FC and SC are intrinsically related, in that coordination of activity across regions ultimately depends on fast and efficient transfer of information made possible by structural connections. Convergence between FC and SC has been shown for specific networks, especially the default mode network (DMN). However, it is not known to what degree FC is constrained by major WM tracts and whether FC and SC change together over time. Here, 120 participants (20-85 years) were tested at two time points, separated by 3.3 years. Resting-state fMRI was used to measure FC, and DWI to measure WM microstructure as an index of SC. TRACULA, part of FreeSurfer, was used for automated tractography of 18 major WM tracts. Cortical regions with tight structural couplings defined by tractography were only weakly related at the functional level. Certain regions of the DMN showed a modest relationship between change in FC and SC, but for the most part, the two measures changed independently. The main conclusions are that anatomical alignment of SC and FC seems restricted to specific networks and tracts, and that changes in SC and FC are not necessarily strongly correlated.

The human brain is a complex network characterized by structural and functional connections within and between regions. It is proven that in some brain diseases (such as schizophrenia), these connections are disrupted. Graph theory is a suitable tool to analyze brain network disruptions. The data analyzed in this study include the functional and structural connections of 27 schizophrenic patients and 27 healthy controls. By means of Independent Component Analysis (ICA) and joint ICA (jICA) algorithms, the brain network is divided into 10 sub-networks, in which structural connections play an important role. We look for graph parameters that distinguish patients from healthy people. We observed that the local parameters of joint subnetwork #3 such as clustering efficiency, strength, and local efficiency can be used to distinguish patients from controls. The global parameters in joint subnetwork #1, such as characteristic path length, radius, diameter, and max modularity have the same effect. To investigate nervous system disorders such as schizophrenia it is better to analyze the joint sub-networks of the brain than the whole brain.

Synathesia is a condition in which stimulation of a sensory modality triggers another sensation in the alike or an unalike sensory modality. Currently, synaesthesia is deemed a neurological condition that engages unwanted transfer of signals between brain regions from one sense to another “crosstalk activation”. The probability that undiagnosed synaesthesia may impact the results of structural magnetic resonance imaging (MRI), Diffusion Tensor imaging (DTI), functional magnetic resonance imaging (fMRI) and resting state connectivity studies is high, given the multiple anatomical and functional connections within the brain. In this paper, the currently available literature to mark which sensations adjured by synaesthesia and how could this impact MRI different modalities. Our study found that synaesthesia can have an opaque impact on fMRI studies of sensory, memory and cognitive functions, and there is testimony to suggest structural connections in the brain are also mutated DTI measurements especially, it shows enhanced structural connectivity for synesthetes between brain regions, higher Fractional anisotropy (FA), as well as increased in the white matter integrity between some regions.. Given the low dispersal of synaesthesia, the likelihood of synaesthesia being a perplexing factor in DTI, fMRI studies of patient groups is small; however, determining the existence of synaesthesia is paramount for investigating individual patients especially Shizoherenia, and autistic patients.

Hypertension is a risk factor for cognitive impairment in older age. However, evidence of the neural basis of the relationship between the deterioration of cognitive function and elevated blood pressure is sparse. Based on previous research, we speculate that variations in brain connectivity are closely related to elevated blood pressure even before the onset of clinical conditions and apparent cognitive decline in individuals over 60 years of age. Forty cognitively healthy adults were recruited. Each received a blood pressure test before and after the cognitive assessment in various domains. Diffusion tensor imaging (DTI) and resting-state functional magnetic resonance imaging (rsfMRI) data were collected. Our findings confirm that elevated blood pressure is associated with brain connectivity variations in cognitively healthy individuals. The integrity of the splenium of the corpus callosum is closely related to individual differences in systolic blood pressure. In particular, elevated systolic blood pressure is related to resting-state ventral attention network (VAN) and information processing speed. Serial mediation analyses have further revealed that lower integrity of the splenium statistically predicts elevated systolic blood pressure, which in turn predicts weakened functional connectivity (FC) within the VAN and eventually poorer processing speed. The current study sheds light on how neural correlates are involved in the impact of elevated blood pressure on cognitive functioning.

Resting state functional magnetic resonance imaging (fMRI) has been commonly used to measure functional connectivity between cortical regions, while diffusion tensor imaging (DTI) can be used to characterize structural connectivity of white matter tracts. In principle combining resting state fMRI and DTI data could allow characterization of structure-function relations of distributed neural networks. However, due to differences in the biophysical origins of their signals and in the tissues to which they apply, there has been no direct integration of these techniques to date. We demonstrate that MRI signal variations and power spectra in a resting state are largely comparable between gray matter and white matter, that there are temporal correlations of fMRI signals that persist over long distances within distinct white matter structures, and that neighboring intervoxel correlations of low frequency resting state signals showed distinct anisotropy in many regions. These observations suggest that MRI signal variations from within white matter in a resting state may convey similar information as their corresponding fluctuations of MRI signals in gray matter. We thus derive a local spatio-temporal correlation tensor which captures directional variations of resting-state correlations and which reveals distinct structures in both white and gray matter. This novel concept is illustrated with in vivo experiments in a resting state, which demonstrate the potential of the technique for mapping the functional structure of neural networks and for direct integration of structure-function relations in the human brain.

ObjectiveDeep brain stimulation (DBS) of the centromedian thalamic nucleus (CM) can be an alternative treatment option for intractable epilepsy patients. Since CM may be involved in widespread cortico-subcortical networks, identification of the cortical sub-networks specific to the target stimuli may provide further understanding on the underlying mechanisms of CM DBS. Several brain structures have distinguishing brain connections that may be related to the pivotal propagation and subsequent clinical effect of DBS.MethodsTo explore core structures and their connections relevant to CM DBS, we applied electroencephalogram (EEG) and diffusion tensor imaging (DTI) to 10 medically intractable patients – three generalized epilepsy (GE) and seven multifocal epilepsy (MFE) patients unsuitable for resective surgery. Spatiotemporal activation pattern was mapped from scalp EEG by delivering low-frequency stimuli (5 Hz). Structural connections between the CM and the cortical activation spots were assessed using DTI.ResultsWe confirmed an average 72% seizure reduction after CM DBS and its clinical efficiency remained consistent during the observation period (mean 21 months). EEG data revealed sequential source propagation from the anterior cingulate, followed by the frontotemporal regions bilaterally. In addition, maximal activation was found in the left cingulate gyrus and the right medial frontal cortex during the right and left CM stimulation, respectively. From DTI data, we confirmed concrete structural connections between CM and those maximal activation spots identified from EEG data.ConclusionThese results suggest that the anterior cingulate can be a core cortical structure for the bilateral propagation of CM stimulation. Our DTI findings also indicate that the propagation of CM stimulation may rely upon integrity of structural connections between CM and these key cortical regions. Structures and their connections found in this study may be relevant in the interpretation of the clinical outcomes of CM DBS.

Despite much research on the function of the insular cortex, few studies have investigated functional subdivisions of the insula in humans. The present study used resting-state functional connectivity magnetic resonance imaging (MRI) to parcellate the human insular lobe based on clustering of functional connectivity patterns. Connectivity maps were computed for each voxel in the insula based on resting-state functional MRI (fMRI) data and segregated using cluster analysis. We identified 3 insular subregions with distinct patterns of connectivity: a posterior region, functionally connected with primary and secondary somatomotor cortices; a dorsal anterior to middle region, connected with dorsal anterior cingulate cortex, along with other regions of a previously described control network; and a ventral anterior region, primarily connected with pregenual anterior cingulate cortex. Applying these regions to a separate task data set, we found that dorsal and ventral anterior insula responded selectively to disgusting images, while posterior insula did not. These results demonstrate that clustering of connectivity patterns can be used to subdivide cerebral cortex into anatomically and functionally meaningful subregions; the insular regions identified here should be useful in future investigations on the function of the insula.

The aim of our study was to investigate differences in whole brain connectivity at different levels between drug-naïve individuals with early Parkinson’s disease (PD) and healthy controls (HCs). Resting-state functional magnetic resonance imaging data were collected from 47 patients with early-stage, drug-naïve PD and 50 HCs. Functional brain connectivity was analyzed at the integrity, network, and edge levels; UPDRS-III, MMSE, MOCA, HAMA, and HAMD scores, reflecting the symptoms of PD, were collected for further regression analysis. Compared with age-matched HCs, reduced functional connectivity were mainly observed in the visual (VSN), somatomotor (SMN), limbic (LBN), and deep gray matter networks (DGN) at integrity level [p < 0.05, false discovery rate (FDR) corrected]. Intra-network analysis indicated decreased functional connectivity in DGN, SMN, LBN, and ventral attention networks (VAN). Inter-network analysis indicated reduced functional connectivity in nine pairs of resting-state networks. At the edge level, the LBN was the center of abnormal functional connectivity (p < 0.05, FDR corrected). MOCA score was associated with the intra-network functional connectivity strength (FC) of the DGN, and inter-network FC of the DGN-VAN. HAMA and HAMD scores were associated with the FC of the SMN and DGN, and either the LBN or VAN, respectively. We demonstrated variations in whole brain connections of drug-naïve patients with early PD. Major changes involved the SMN, DGN, LBN, and VSN, which may be relevant to symptoms of early PD. Additionally, our results support PD as a disconnection syndrome.

Functional connectomics is a popular approach to investigate the neural underpinnings of developmental disorders of which attention deficit hyperactivity disorder (ADHD) is one of the most prevalent. Nonetheless, neuronal mechanisms driving the aberrant functional connectivity resulting in ADHD symptoms remain largely unclear. Whereas resting state activity reflecting intrinsic tonic background activity is only vaguely connected to behavioral effects, naturalistic neuroscience has provided means to measure phasic brain dynamics associated with overt manifestation of the symptoms. Here we collected functional magnetic resonance imaging (fMRI) data in three experimental conditions, an active virtual reality (VR) task where the participants execute goal-directed behaviors, a passive naturalistic Video Viewing task, and a standard Resting State condition. Thirty-nine children with ADHD and thirty-seven typically developing (TD) children participated in this preregistered study. Functional connectivity was examined with network-based statistics (NBS) and graph theoretical metrics. During the naturalistic VR task, the ADHD group showed weaker task performance and stronger functional connectivity than the TD group. Group differences in functional connectivity were observed in widespread brain networks: particularly subcortical areas showed hyperconnectivity in ADHD. More restricted group differences in functional connectivity were observed during the Video Viewing, and there were no group differences in functional connectivity in the Resting State condition. These observations were consistent across NBS and graph theoretical analyses, although NBS revealed more pronounced group differences. Furthermore, during the VR task and Video Viewing, functional connectivity in TD controls was associated with task performance during the measurement, while Resting State activity in TD controls was correlated with ADHD symptoms rated over six months. We conclude that overt expression of the symptoms is correlated with aberrant brain connectivity in ADHD. Furthermore, naturalistic paradigms where clinical markers can be coupled with simultaneously occurring brain activity may further increase the interpretability of psychiatric neuroimaging findings.