Individual Brain Networks Research Articles

Concussion Prone Scenarios: A Multi-Dimensional Exploration in Impact Directions, Brain Morphology, and Network Architectures Using Computational Models.

While individual susceptibility to traumatic brain injury (TBI) has been speculated, past work does not provide an analysis considering how physical features of an individual's brain (e.g., brain size, shape), impact direction, and brain network features can holistically contribute to the risk of suffering a TBI from an impact. This work investigated each of these features simultaneously using computational modeling and analyses of simulated functional connectivity. Unlike the past studies that assess the severity of TBI based on the quantification of brain tissue damage (e.g., principal strain), we approached the brain as a complex network in which neuronal oscillations orchestrate to produce normal brain function (estimated by functional connectivity) and, to this end, both the anatomical damage location and its topological characteristics within the brain network contribute to the severity of brain function disruption and injury. To represent the variations in the population, we analyzed a publicly available database of brain imaging data and selected five distinct network architectures, seven different brain sizes, and three uniaxial head rotational conditions to study the consequences of 74 virtual impact scenarios. Results show impact direction produces the most significant change in connections across brain areas (structural connectome) and the functional coupling of activity across these brain areas (functional connectivity). Axial rotations were more injurious than those with sagittal and coronal rotations when the head kinematics were the same for each condition. When the impact direction was held constant, brain network architecture showed a significantly different vulnerability across axial and sagittal, but not coronal rotations. As expected, brain size significantly affected the expected change in structural and functional connectivity after impact. Together, these results provided groupings of predicted vulnerability to impact-a subgroup of male brain architectures exposed to axial impacts were most vulnerable, while a subgroup of female brain architectures was the most tolerant to the sagittal impacts studied. These findings lay essential groundwork for subject-specific analyses of concussion and provide invaluable guidance for designing personalized protection equipment.

Hierarchical Individual Naturalistic Functional Brain Networks with Group Consistency uncovered by a Two-Stage NAS-Volumetric Sparse DBN Framework.

The functional magnetic resonance imaging under naturalistic paradigm (NfMRI) showed great advantages in identifying complex and interactive functional brain networks (FBNs) because of its dynamics and multimodal information. In recent years, various deep learning models, such as deep convolutional autoencoder (DCAE), deep belief network (DBN), and volumetric sparse DBN (vsDBN), can obtain hierarchical FBNs and temporal features from fMRI data. Among them, the vsDBN model revealed a good capability in identifying hierarchical FBNs by modeling fMRI volume images. However, because of the high dimensionality of fMRI volumes and the diverse training parameters of deep learning methods, especially the network architecture that is the most critical parameter for uncovering the hierarchical organization of human brain function, researchers still face challenges in designing an appropriate deep learning framework with automatic network architecture optimization to model volumetric NfMRI. In addition, most of the existing deep learning models ignore the group-wise consistency and intersubject variation properties embedded in NfMRI volumes. To solve these problems, we proposed a two-stage neural architecture search (NAS) and vsDBN model (two-stage NAS-vsDBN model) to identify the hierarchical human brain spatiotemporal features possessing both group consistency and individual uniqueness under naturalistic condition. Moreover, our model defined reliable network structure for modeling volumetric NfMRI data via NAS framework, and the group-level and individual-level FBNs and associated temporal features exhibited great consistency. In general, our method well identified the hierarchical temporal and spatial features of the brain function and revealed the crucial properties of neural processes under natural viewing condition.

Altered pattern analysis and identification of subjective cognitive decline based on morphological brain network.

Subjective cognitive decline (SCD) is considered the first stage of Alzheimer’s disease (AD). Accurate diagnosis and the exploration of the pathological mechanism of SCD are extremely valuable for targeted AD prevention. However, there is little knowledge of the specific altered morphological network patterns in SCD individuals. In this present study, 36 SCD cases and 34 paired-matched normal controls (NCs) were recruited. The Jensen-Shannon distance-based similarity (JSS) method was implemented to construct and derive the attributes of multiple brain connectomes (i.e., morphological brain connections and global and nodal graph metrics) of individual morphological brain networks. A t-test was used to discriminate between the selected nodal graph metrics, while the leave-one-out cross-validation (LOOCV) was used to obtain consensus connections. Comparisons were performed to explore the altered patterns of connectome features. Further, the multiple kernel support vector machine (MK-SVM) was used for combining brain connectomes and differentiating SCD from NCs. We showed that the consensus connections and nodal graph metrics with the most discriminative ability were mostly found in the frontal, limbic, and parietal lobes, corresponding to the default mode network (DMN) and frontoparietal task control (FTC) network. Altered pattern analysis demonstrated that SCD cases had a tendency for modularity and local efficiency enhancement. Additionally, using the MK-SVM to combine the features of multiple brain connectomes was associated with optimal classification performance [area under the curve (AUC): 0.9510, sensitivity: 97.22%, specificity: 85.29%, and accuracy: 91.43%]. Therefore, our study highlighted the combination of multiple connectome attributes based on morphological brain networks and offered a valuable method for distinguishing SCD individuals from NCs. Moreover, the altered patterns of multidimensional connectome attributes provided a promising insight into the neuroimaging mechanism and early intervention in SCD subjects.

Role of the amygdala in disrupted integration and effective connectivity of cortico-subcortical networks in apathy.

Apathy is a quantitative reduction in motivation and goal-directed behaviors, not only observed in neuropsychiatric disorders, but also present in healthy populations. Although brain abnormalities associated with apathy in clinical disorders have been studied, the organization of brain networks in healthy individuals has yet to be identified. We examined properties of intrinsic brain networks in healthy individuals with varied levels of apathy. By using functional magnetic resonance imaging in combination with graph theory analysis and dynamic causal modeling analysis, we tested communications among nodes and modules as well as effective connectivity among brain networks. We found that the average participation coefficient of the subcortical network, especially the amygdala, was lower in individuals with high than low apathy. Importantly, we observed weaker effective connectivity fromthe hippocampus and parahippocampal gyrus to the amygdala, and from the amygdala to the parahippocampal gyrus and medial frontal cortex in individuals with apathy. These findings suggest that individuals with high apathy exhibit aberrant communication within the cortical-to-subcortical network, characterized by differences in amygdala-related effective connectivity. Our work sheds light on the neural basis of apathy in subclinical populations and may have implications for understanding the development of clinical conditions that feature apathy.

Masked features of task states found in individual brain networks.

Completing complex tasks requires that we flexibly integrate information across brain areas. While studies have shown how functional networks are altered during different tasks, this work has generally focused on a cross-subject approach, emphasizing features that are common across people. Here we used extended sampling "precision" fMRI data to test the extent to which task states generalize across people or are individually specific. We trained classifiers to decode state using functional network data in single-person datasets across 5 diverse task states. Classifiers were then tested on either independent data from the same person or new individuals. Individualized classifiers were able to generalize to new participants. However, classification performance was significantly higher within a person, a pattern consistent across model types, people, tasks, feature subsets, and even for decoding very similar task conditions. Notably, these findings also replicated in a new independent dataset. These results suggest that individual-focused approaches can uncover robust features of brain states, including features obscured in cross-subject analyses. Individual-focused approaches have the potential to deepen our understanding of brain interactions during complex cognition.

Characterizing the propagation pathway of neuropathological events of Alzheimer's disease using harmonic wavelet analysis.

Empirical imaging biomarkers such as the level of the regional pathological burden are widely used to measure the risk of developing neurodegenerative diseases such as Alzheimer's disease (AD). However, ample evidence shows that the brain network (wirings of white matter fibers) plays a vital role in the progression of AD, where neuropathological burdens often propagate across the brain network in a prion-like manner. In this context, characterizing the spreading pathway of AD-related neuropathological events sheds new light on understanding the heterogeneity of pathophysiological mechanisms in AD. In this work, we propose a manifold-based harmonic network analysis approach to explore a novel imaging biomarker in the form of the AD propagation pattern, which eventually allows us to identify the AD-related spreading pathways of neuropathological events throughout the brain. The backbone of this new imaging biomarker is a set of region-adaptive harmonic wavelets that represent the common network topology across individuals. We conceptualize that the individual's brain network and its associated pathology pattern form a unique system, which vibrates as do all natural objects in the universe. Thus, we can computationally excite such a brain system using selected harmonic wavelets that match the system's resonance frequency, where the resulting oscillatory wave manifests the system-level propagation pattern of neuropathological events across the brain network. We evaluate the statistical power of our harmonic network analysis approach on large-scale neuroimaging data from ADNI. Compared with the other empirical biomarkers, our harmonic wavelets not only yield a new imaging biomarker to potentially predict the cognitive decline in the early stage but also offer a new window to capture the in-vivo spreading pathways of neuropathological burden with a rigorous mathematics insight.

The effect of long-term methadone maintenance treatment on coupling among three large-scale brain networks in male heroin-dependent individuals: A resting-state fMRI study

PurposeMethadone maintenance treatment (MMT) is considered as an effective and mainstream therapy for heroin dependence. However, whether long-term MMT would improve the coupling among the three core large-scale brain networks (salience, default mode, and executive control) and its relationship with the craving for heroin is unknown. MethodsForty-four male heroin-dependent individuals during long-term MMT, 27 male heroin-dependent individuals after short-term detoxification/abstinence (SA), and 26 demographically matched healthy controls (HC) underwent resting-state functional magnetic resonance imaging. We analyzed the difference in coupling among the salience, default mode, and executive control networks among the three groups and examined how the coupling among these large-scale networks was associated with craving before and after drug-cue exposure. ResultsCompared with the SA group, the MMT group showed lower craving before and after cue exposure and stronger connectivity between the dorsal anterior cingulate cortex (a key node of the salience network) and key regions of the bilateral executive control network, including the bilateral dorsolateral prefrontal cortex, posterior parietal cortex, and dorsomedial prefrontal cortex. Among the heroin-dependent individuals, the functional connectivity was negatively correlated with the craving before and after heroin-cue exposure. ConclusionOur findings suggest that long-term MMT could increase the coupling between the salience and bilateral executive control networks and decrease craving for heroin. These findings contribute to the understanding of the neural mechanism of MMT, from the perspective of large-scale brain networks.

Synchronization stability analysis of functional brain networks in boys with ADHD during facial emotions processing

The study of the synchronization phenomenon in the functional brain networks of individuals with Attention Deficit Hyperactivity Disorder (ADHD) has always been of interest to researchers. ADHD is a prevalent psychiatric disorder among children, which in addition to other problems, makes it difficult to recognize facial emotions correctly. However, the synchronization stability, which indicates the ability of a network to remain in a synchronous state, is still unknown in ADHD. This study investigates the phase synchronization stability and robustness of functional brain networks in boys with ADHD while observing the facial emotions. The primary brain networks of 22 boys with ADHD and 22 healthy ones are constructed using their electroencephalogram signals by the Phase Locking Value (PLV) method. Then, significant subnetworks (P-Value < 0.05) are extracted from the primary brain network by applying the Network-Based Statistic (NBS) method. Three measures, including the second smallest eigenvalue, largest eigenvalue, and eigenratio of these eigenvalues, are calculated from all brain subnetworks as network robustness and synchronization stability criteria. The statistical tests indicate no significant differences between the second smallest eigenvalues in brain networks of two ADHD and healthy groups, representing the same robustness of brain networks topological features to perturbations in both groups. The largest eigenvalues and the eigenratios extracted from the functional brain networks of the ADHD group are significantly (P-Value < 0.05) lower than the healthy one, which shows an increased synchronizability in the brain networks of the ADHD group. This alteration in the phase synchronization stability may be associated with a deficit in the emotional processing of the brain network in the ADHD group.

Individual Brain Morphological Connectome Indicator Based on Jensen-Shannon Divergence Similarity Estimation for Autism Spectrum Disorder Identification.

BackgroundStructural magnetic resonance imaging (sMRI) reveals abnormalities in patients with autism spectrum syndrome (ASD). Previous connectome studies of ASD have failed to identify the individual neuroanatomical details in preschool-age individuals. This paper aims to establish an individual morphological connectome method to characterize the connectivity patterns and topological alterations of the individual-level brain connectome and their diagnostic value in patients with ASD.MethodsBrain sMRI data from 24 patients with ASD and 17 normal controls (NCs) were collected; participants in both groups were aged 24–47 months. By using the Jensen–Shannon Divergence Similarity Estimation (JSSE) method, all participants’s morphological brain network were ascertained. Student’s t-tests were used to extract the most significant features in morphological connection values, global graph measurement, and node graph measurement.ResultsThe results of global metrics’ analysis showed no statistical significance in the difference between two groups. Brain regions with meaningful properties for consensus connections and nodal metric features are mostly distributed in are predominantly distributed in the basal ganglia, thalamus, and cortical regions spanning the frontal, temporal, and parietal lobes. Consensus connectivity results showed an increase in most of the consensus connections in the frontal, parietal, and thalamic regions of patients with ASD, while there was a decrease in consensus connectivity in the occipital, prefrontal lobe, temporal lobe, and pale regions. The model that combined morphological connectivity, global metrics, and node metric features had optimal performance in identifying patients with ASD, with an accuracy rate of 94.59%.ConclusionThe individual brain network indicator based on the JSSE method is an effective indicator for identifying individual-level brain network abnormalities in patients with ASD. The proposed classification method can contribute to the early clinical diagnosis of ASD.

Topologically Disrupted Gray Matter Networks in Drug-Naïve Essential Tremor Patients With Poor Sleep Quality

BackgroundSleep disturbances are widespread among patients with essential tremor (ET) and may have adverse effects on patients' quality of life. However, the pathophysiology underlying poor quality of sleep (QoS) in patients with ET remains unclear. Our study aimed to identify gray matter (GM) network alterations in the topological properties of structural MRI related to QoS in patients with ET.MethodWe enrolled 45 ET patients with poor QoS (SleET), 59 ET patients with normal QoS (NorET), and 66 healthy controls (HC), and they all underwent a three-dimensional T1-weighted MRI scan. We used a graph-theoretical approach to investigate the topological organization of GM morphological networks, and individual morphological brain networks were constructed according to the interregional similarity of GM volume distributions. Furthermore, we performed network-based statistics, and partial correlation analyses between topographic features and clinical characteristics were conducted.ResultsGlobal network organization was disrupted in patients with ET. Compared with the NorET group, the SleET group exhibited disrupted topological GM network organization with a shift toward randomization. Moreover, they showed altered nodal centralities in mainly the frontal, temporal, parietal, and cerebellar lobes. Morphological connection alterations within the default mode network (DMN), salience, and basal ganglia networks were observed in the SleET group and were generally more extensive than those in the NorET and HC groups. Alterations within the cerebello-thalamo-(cortical) network were only detected in the SleET group. The nodal degree of the left thalamus was negatively correlated with the Fahn-Tolosa-Marin Tremor Rating Scale score (r = −0.354, p =0.027).ConclusionOur findings suggest that potential complex interactions underlie tremor and sleep disruptions in patients with ET. Disruptions within the DMN and the cerebello-thalamo-(cortical) network may have a broader impact on sleep quality in patients with ET. Our results offer valuable insight into the neural mechanisms underlying poor QoS in patients with ET.

The Social Connectome - Moving Toward Complexity in the Study of Brain Networks and Their Interactions in Social Cognitive and Affective Neuroscience.

Over the past 150 years of neuroscientific research, the field has undergone a tremendous evolution. Starting out with lesion-based inference of brain function, functional neuroimaging, introduced in the late 1980s, and increasingly fine-grained and sophisticated methods and analyses now allow us to study the live neural correlates of complex behaviors in individuals and multiple agents simultaneously. Classically, brain-behavior coupling has been studied as an association of a specific area in the brain and a certain behavioral outcome. This has been a crucial first step in understanding brain organization. Social cognitive processes, as well as their neural correlates, have typically been regarded and studied as isolated functions and blobs of neural activation. However, as our understanding of the social brain as an inherently dynamic organ grows, research in the field of social neuroscience is slowly undergoing the necessary evolution from studying individual elements to how these elements interact and their embedding within the overall brain architecture. In this article, we review recent studies that investigate the neural representation of social cognition as interacting, complex, and flexible networks. We discuss studies that identify individual brain networks associated with social affect and cognition, interaction of these networks, and their relevance for disorders of social affect and cognition. This perspective on social cognitive neuroscience can highlight how a more fine-grained understanding of complex network (re-)configurations could improve our understanding of social cognitive deficits in mental disorders such as autism spectrum disorder and schizophrenia, thereby providing new impulses for methods of interventions.

Experience with opioids does not modify the brain network involved in expectations of placebo analgesia.

Placebo analgesia (PA) is defined as a psychobiological phenomenon triggered by the information surrounding an analgesic drug instead of its inherent pharmacological properties. PA is hypothesized to be formed through either verbal suggestions or conditioning. The present study aims at disentangling the neural correlates of expectations effects with or without conditioning through prior experience using the model of PA.We addressed this question by recruiting two groups of individuals holding comparable verbally‐induced expectations regarding morphine analgesia but either (i) with or (ii) without prior experience with opioids. We then contrasted the two groups' neurocognitive response to acute heat‐pain induction following the injection of sham morphine using electroencephalography (EEG). Topographic ERP analyses of the N2 and P2 pain evoked potential components allowed to test the hypothesis that PA involves distinct neural networks when induced by expectations with or without prior experience.First, we confirmed that the two groups showed corresponding expectations of morphine analgesia (Hedges' g s < .4 positive control criteria, g s = .37 observed difference), and that our intervention induced a medium‐sized PA (Hedges' g av ≥ .5 positive control, g av = .6 observed PA). We then tested our hypothesis on the recruitment of different PA‐associated brain networks in individuals with versus without prior experience with opioids and found no evidence for a topographic N2 and P2 ERP components difference between the two groups. Our results thus suggest that in the presence of verbally‐induced expectations, modifications in the PA‐associated brain activity by conditioning are either absent or very small.

Epidemic models characterize seizure propagation and the effects of epilepsy surgery in individualized brain networks based on MEG and invasive EEG recordings

Epilepsy surgery is the treatment of choice for drug-resistant epilepsy patients. However, seizure-freedom is currently achieved in only 2/3 of the patients after surgery. In this study we have developed an individualized computational model based on MEG brain networks to explore seizure propagation and the efficacy of different virtual resections. Eventually, the goal is to obtain individualized models to optimize resection strategy and outcome. We have modelled seizure propagation as an epidemic process using the susceptible-infected (SI) model on individual brain networks derived from presurgical MEG. We included 10 patients who had received epilepsy surgery and for whom the surgery outcome at least one year after surgery was known. The model parameters were tuned in in order to reproduce the patient-specific seizure propagation patterns as recorded with invasive EEG. We defined a personalized search algorithm that combined structural and dynamical information to find resections that maximally decreased seizure propagation for a given resection size. The optimal resection for each patient was defined as the smallest resection leading to at least a 90% reduction in seizure propagation. The individualized model reproduced the basic aspects of seizure propagation for 9 out of 10 patients when using the resection area as the origin of epidemic spreading, and for 10 out of 10 patients with an alternative definition of the seed region. We found that, for 7 patients, the optimal resection was smaller than the resection area, and for 4 patients we also found that a resection smaller than the resection area could lead to a 100% decrease in propagation. Moreover, for two cases these alternative resections included nodes outside the resection area. Epidemic spreading models fitted with patient specific data can capture the fundamental aspects of clinically observed seizure propagation, and can be used to test virtual resections in silico. Combined with optimization algorithms, smaller or alternative resection strategies, that are individually targeted for each patient, can be determined with the ultimate goal to improve surgery outcome. MEG-based networks can provide a good approximation of structural connectivity for computational models of seizure propagation, and facilitate their clinical use.

The Effect of Aging on Brain Glucose Metabolic Connectivity Revealed by [18F]FDG PET-MR and Individual Brain Networks.

Contrary to group-based brain connectivity analyses, the aim of this study was to construct individual brain metabolic networks to determine age-related effects on brain metabolic connectivity. Static 40–60 min [18F]FDG positron emission tomography (PET) images of 67 healthy subjects between 20 and 82 years were acquired with an integrated PET-MR system. Network nodes were defined by brain parcellation using the Schaefer atlas, while connectivity strength between two nodes was determined by comparing the distribution of PET uptake values within each node using a Kullback–Leibler divergence similarity estimation (KLSE). After constructing individual brain networks, a linear and quadratic regression analysis of metabolic connectivity strengths within- and between-networks was performed to model age-dependency. In addition, the age dependency of metrics for network integration (characteristic path length), segregation (clustering coefficient and local efficiency), and centrality (number of hubs) was assessed within the whole brain and within predefined functional subnetworks. Overall, a decrease of metabolic connectivity strength with healthy aging was found within the whole-brain network and several subnetworks except within the somatomotor, limbic, and visual network. The same decrease of metabolic connectivity was found between several networks across the whole-brain network and the functional subnetworks. In terms of network topology, a less integrated and less segregated network was observed with aging, while the distribution and the number of hubs did not change with aging, suggesting that brain metabolic networks are not reorganized during the adult lifespan. In conclusion, using an individual brain metabolic network approach, a decrease in metabolic connectivity strength was observed with healthy aging, both within the whole brain and within several predefined networks. These findings can be used in a diagnostic setting to differentiate between age-related changes in brain metabolic connectivity strength and changes caused by early development of neurodegeneration.

Examining the Effects of Normal Ageing on Cortical Connectivity of Older Adults.

With the recent advancement in computer technology, we can extract the picture of the brain as a network. The aim of this study is to constructs large scale individual anatomical brain networks using regional gray matter cortical thickness from individual subject's magnetic resonance imaging (MRI) data, as well as to investigate changes with normal aging in global network organization. The dataset includes 183 healthy subjects sMRI data with an age range from 50 to 80 plus. For all brain networks, we calculated the global network measures and nodal network measures by using network analysis toolkit GRETNA. From global network measurements we calculated small-world measurements and network efficiency measurements, from nodal measurements we calculated node clustering coefficient (CC) and node efficiency at a wide-range of threshold values. All small world measurements showed more clustering at all the given threshold values than random networks and a alike least path length, indicative of that they were "small world". To analyze the effect normal ageing on networks organization, the networks of subjects were categorized into three age groups (50s, 60s, and 70 over). The global and nodal network measurements of each group were statistically analyzed to investigate the significant difference in network organization with in age groups. Results shows that the age has no significance effect in global measurements of brain network. However, by analysis the nodal measures of brain network between age group, network nodes from brain frontal lobe and temporal lobe showed age related significant difference. The results obtained from the proposed study suggest that this network method can deliver a concise network-level picture of brain organization and be used from the outlook of composite networks to investigate inter-individual variability in brain morphology.

Repetitive transcranial magnetic stimulation modulates coupling among large-scale brain networks in heroin-dependent individuals: A randomized resting-state functional magnetic resonance imaging study.

The abnormal interactions of three key large-scale brain networks (default mode [DMN], salience and executive control [ECN]) were showed underlie dysfunctions in heroin addiction. Repetitive transcranial magnetic stimulation (rTMS) targeting the left dorsolateral prefrontal cortex (DLPFC) is a potential treatment for heroin addiction. It is unclear whether impaired coupling among the large-scale brain networks would be improved by rTMS in treated heroin-dependent individuals. Thirty-five heroin-dependent individuals were included in this sham-controlled, randomized study. The patients received either active or sham rTMS for 1week. The craving for heroin and resting-state functional magnetic resonance imaging data were collected before and after 1-week rTMS. Twenty-two healthy subjects were included as controls not receiving rTMS. After 1-week rTMS, only the active rTMS group showed a significant decrease in spontaneous and heroin cue-induced craving. The coupling between left DLPFC (a key node of left ECN) and left parahippocampal gyrus (PHG, included in DMN) significantly increased for the active group with a tendency towards that of controls. The coupling between the right precentral gyrus and three key regions included in DMN (posterior cingulate cortex/precuneus and bilateral inferior parietal cortex) significantly decreased for the active group with a tendency towards that of healthy controls. For the active rTMS individuals, the left DLPFC-PHG coupling negatively correlated with the spontaneous craving and the drug cue-induced craving. It suggested that the rTMS could reduce heroin craving, which might be related to the modulation of ECN-DMN coupling. This finding might shed light on the mechanism of rTMS for heroin addiction treatment.

Optimization of epilepsy surgery through virtual resections on individual structural brain networks

The success of epilepsy surgery in patients with refractory epilepsy depends upon correct identification of the epileptogenic zone (EZ) and an optimal choice of the resection area. In this study we developed individualized computational models based upon structural brain networks to explore the impact of different virtual resections on the propagation of seizures. The propagation of seizures was modelled as an epidemic process [susceptible-infected-recovered (SIR) model] on individual structural networks derived from presurgical diffusion tensor imaging in 19 patients. The candidate connections for the virtual resection were all connections from the clinically hypothesized EZ, from which the seizures were modelled to start, to other brain areas. As a computationally feasible surrogate for the SIR model, we also removed the connections that maximally reduced the eigenvector centrality (EC) (large values indicate network hubs) of the hypothesized EZ, with a large reduction meaning a large effect. The optimal combination of connections to be removed for a maximal effect were found using simulated annealing. For comparison, the same number of connections were removed randomly, or based on measures that quantify the importance of a node or connection within the network. We found that 90% of the effect (defined as reduction of EC of the hypothesized EZ) could already be obtained by removing substantially less than 90% of the connections. Thus, a smaller, optimized, virtual resection achieved almost the same effect as the actual surgery yet at a considerably smaller cost, sparing on average 27.49% (standard deviation: 4.65%) of the connections. Furthermore, the maximally effective connections linked the hypothesized EZ to hubs. Finally, the optimized resection was equally or more effective than removal based on structural network characteristics both regarding reducing the EC of the hypothesized EZ and seizure spreading. The approach of using reduced EC as a surrogate for simulating seizure propagation can suggest more restrictive resection strategies, whilst obtaining an almost optimal effect on reducing seizure propagation, by taking into account the unique topology of individual structural brain networks of patients.

Hierarchical modelling of functional brain networks in population and individuals from big fMRI data

A major goal of large-scale brain imaging datasets is to provide resources for investigating heterogeneous populations. Characterisation of functional brain networks for individual subjects from these datasets will have an enormous potential for prediction of cognitive or clinical traits. We propose for the first time a technique, Stochastic Probabilistic Functional Modes (sPROFUMO), that is scalable to UK Biobank (UKB) with expected 100,000 participants, and hierarchically estimates functional brain networks in individuals and the population, while allowing for bidirectional flow of information between the two. Using simulations, we show the model's utility, especially in scenarios that involve significant cross-subject variability, or require delineation of fine-grained differences between the networks. Subsequently, by applying the model to resting-state fMRI from 4999 UKB subjects, we mapped resting state networks (RSNs) in single subjects with greater detail than has been possible previously in UKB (>100 RSNs), and demonstrate that these RSNs can predict a range of sensorimotor and higher-level cognitive functions. Furthermore, we demonstrate several advantages of the model over independent component analysis combined with dual-regression (ICA-DR), particularly with respect to the estimation of the spatial configuration of the RSNs and the predictive power for cognitive traits. The proposed model and results can open a new door for future investigations into individualised profiles of brain function from big data.

Topologically convergent and divergent morphological gray matter networks in early-stage Parkinson's disease with and without mild cognitive impairment.

Patients with Parkinson's disease with mild cognitive impairment (PD‐M) progress to dementia more frequently than those with normal cognition (PD‐N), but the underlying neurobiology remains unclear. This study aimed to define the specific morphological brain network alterations in PD‐M, and explore their potential diagnostic value. Twenty‐four PD‐M patients, 17 PD‐N patients, and 29 healthy controls (HC) underwent a structural MRI scan. Similarity between interregional gray matter volume distributions was used to construct individual morphological brain networks. These were analyzed using graph theory and network‐based statistics (NBS), and their relationship to neuropsychological tests was assessed. Support vector machine (SVM) was used to perform individual classification. Globally, compared with HC, PD‐M showed increased local efficiency (p = .001) in their morphological networks, while PD‐N showed decreased normalized path length (p = .008). Locally, similar nodal deficits were found in the rectus and lingual gyrus, and cerebellum of both PD groups relative to HC; additionally in PD‐M nodal deficits involved several frontal and parietal regions, correlated with cognitive scores. NBS found that similar connections were involved in the default mode and cerebellar networks of both PD groups (to a greater extent in PD‐M), while PD‐M, but not PD‐N, showed altered connections involving the frontoparietal network. Using connections identified by NBS, SVM allowed discrimination with high accuracy between PD‐N and HC (90%), PD‐M and HC (85%), and between the two PD groups (65%). These results suggest that default mode and cerebellar disruption characterizes PD, more so in PD‐M, whereas frontoparietal disruption has diagnostic potential.

GAT-LI: a graph attention network based learning and interpreting method for functional brain network classification

BackgroundAutism spectrum disorders (ASD) imply a spectrum of symptoms rather than a single phenotype. ASD could affect brain connectivity at different degree based on the severity of the symptom. Given their excellent learning capability, graph neural networks (GNN) methods have recently been used to uncover functional connectivity patterns and biological mechanisms in neuropsychiatric disorders, such as ASD. However, there remain challenges to develop an accurate GNN learning model and understand how specific decisions of these graph models are made in brain network analysis.ResultsIn this paper, we propose a graph attention network based learning and interpreting method, namely GAT-LI, which learns to classify functional brain networks of ASD individuals versus healthy controls (HC), and interprets the learned graph model with feature importance. Specifically, GAT-LI includes a graph learning stage and an interpreting stage. First, in the graph learning stage, a new graph attention network model, namely GAT2, uses graph attention layers to learn the node representation, and a novel attention pooling layer to obtain the graph representation for functional brain network classification. We experimentally compared GAT2 model’s performance on the ABIDE I database from 1035 subjects against the classification performances of other well-known models, and the results showed that the GAT2 model achieved the best classification performance. We experimentally compared the influence of different construction methods of brain networks in GAT2 model. We also used a larger synthetic graph dataset with 4000 samples to validate the utility and power of GAT2 model. Second, in the interpreting stage, we used GNNExplainer to interpret learned GAT2 model with feature importance. We experimentally compared GNNExplainer with two well-known interpretation methods including Saliency Map and DeepLIFT to interpret the learned model, and the results showed GNNExplainer achieved the best interpretation performance. We further used the interpretation method to identify the features that contributed most in classifying ASD versus HC.ConclusionWe propose a two-stage learning and interpreting method GAT-LI to classify functional brain networks and interpret the feature importance in the graph model. The method should also be useful in the classification and interpretation tasks for graph data from other biomedical scenarios.