The brainstem reticular formation is a small-world, not scale-free, network

Multimodal and hemispheric graph-theoretical brain network predictors of learning efficacy for frontal alpha asymmetry neurofeedback

EEG neurofeedback using frontal alpha asymmetry (FAA) has been widely used for emotion regulation, but its effectiveness is controversial. Studies indicated that individual differences in neurofeedback training can be traced to neuroanatomical and neurofunctional features. However, they only focused on regional brain structure or function and overlooked possible neural correlates of the brain network. Besides, no neuroimaging predictors for FAA neurofeedback protocol have been reported so far. We designed a single-blind pseudo-controlled FAA neurofeedback experiment and collected multimodal neuroimaging data from healthy participants before training. We assessed the learning performance for evoked EEG modulations during training (L1) and at rest (L2), and investigated performance-related predictors based on a combined analysis of multimodal brain networks and graph-theoretical features. The main findings of this study are described below. First, both real and sham groups could increase their FAA during training, but only the real group showed a significant increase in FAA at rest. Second, the predictors during training blocks and at rests were different: L1 was correlated with the graph-theoretical metrics (clustering coefficient and local efficiency) of the right hemispheric gray matter and functional networks, while L2 was correlated with the graph-theoretical metrics (local and global efficiency) of the whole-brain and left the hemispheric functional network. Therefore, the individual differences in FAA neurofeedback learning could be explained by individual variations in structural/functional architecture, and the correlated graph-theoretical metrics of learning performance indices showed different laterality of hemispheric networks. These results provided insight into the neural correlates of inter-individual differences in neurofeedback learning.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11571-023-09939-x.

Brain functional network changes in patients with juvenile myoclonic epilepsy: a study based on graph theory and Granger causality analysis

Many resting-state functional magnetic resonance imaging (rs-fMRI) studies have shown that the brain networks are disrupted in adolescent patients with juvenile myoclonic epilepsy (JME). However, previous studies have mainly focused on investigating brain connectivity disruptions from the perspective of static functional connections, overlooking the dynamic causal characteristics between brain network connections. In our study involving 37 JME patients and 35 Healthy Controls (HC), we utilized rs-fMRI to construct whole-brain functional connectivity network. By applying graph theory, we delved into the altered topological structures of the brain functional connectivity network in JME patients and identified abnormal regions as key regions of interest (ROIs). A novel aspect of our research was the application of a combined approach using the sliding window technique and Granger causality analysis (GCA). This method allowed us to delve into the dynamic causal relationships between these ROIs and uncover the intricate patterns of dynamic effective connectivity (DEC) that pervade various brain functional networks. Graph theory analysis revealed significant deviations in JME patients, characterized by abnormal increases or decreases in metrics such as nodal betweenness centrality, degree centrality, and efficiency. These findings underscore the presence of widespread disruptions in the topological features of the brain. Further, clustering analysis of the time series data from abnormal brain regions distinguished two distinct states indicative of DEC patterns: a state of strong connectivity at a lower frequency (State 1) and a state of weak connectivity at a higher frequency (State 2). Notably, both states were associated with connectivity abnormalities across different ROIs, suggesting the disruption of local properties within the brain functional connectivity network and the existence of widespread multi-functional brain functional networks damage in JME patients. Our findings elucidate significant disruptions in the local properties of whole-brain functional connectivity network in patients with JME, revealing causal impairments across multiple functional networks. These findings collectively suggest that JME is a generalized epilepsy with localized abnormalities. Such insights highlight the intricate network dysfunctions characteristic of JME, thereby enriching our understanding of its pathophysiological features.

MEA-NAP compares microscale functional connectivity, topology, and network dynamics in organoid or monolayer neuronal cultures

Microelectrode array (MEA) recordings are commonly used to compare firing and burst rates in neuronal cultures. MEA recordings can also reveal microscale functional connectivity, topology, and network dynamics-patterns seen in brain networks across spatial scales. Network topology is frequently characterized in neuroimaging with graph theoretical metrics. However, few computational tools exist for analyzing microscale functional brain networks from MEA recordings. Here, we present a MATLAB MEA network analysis pipeline (MEA-NAP) for raw voltage time-series acquired from single- or multi-well MEAs. Applications to 3D human cerebral organoids or 2D human-derived or murine cultures reveal differences in network development, including topology, node cartography, and dimensionality. MEA-NAP incorporates multi-unit template-based spike detection, probabilistic thresholding for determining significant functional connections, and normalization techniques for comparing networks. MEA-NAP can identify network-level effects of pharmacologic perturbation and/or disease-causing mutations and, thus, can provide a translational platform for revealing mechanistic insights and screening new therapeutic approaches.

Alteration of White Matter Connectivity for MR-Guided Focused Ultrasound in the Treatment of Essential Tremor

BACKGROUND

Magnetic resonance-guided focused ultrasound (MRgFUS) thalamotomy has been implemented as a therapeutic alternative for the treatment of drug-refractory essential tremor (ET). However, its impact on the brain structural network is still unclear.

PURPOSE

To investigate both global and local alterations of the white matter (WM) connectivity network in ET after MRgFUS thalamotomy.

STUDY TYPE

Retrospective.

SUBJECTS

Twenty-seven ET patients (61 ± 11 years, 19 males) with MRgFUS thalamotomy and 28 healthy controls (HC) (61 ± 11 years, 20 males) were recruited for comparison.

FIELD STRENGTH/SEQUENCE

A 3 T/single shell diffusion tensor imaging by using spin-echo-based echo-planar imaging, three-dimensional T1 weighted imaging by using gradient-echo-based sequence.

ASSESSMENT

Patients were undergoing MRgFUS thalamotomy and their clinical data were collected from pre-operation to 6-month post-operation. Network topological metrics, including rich-club organization, small-world, and efficiency properties were calculated. Correlation between the topological metrics and tremor scores in ET groups was also calculated to assess the role of neural remodeling in the brain.

STATISTICAL TESTS

Two-sample independent t-tests, chi-squared test, ANOVA, Bonferroni test, and Spearman's correlation. Statistical significance was set at P < 0.05.

RESULTS

For ET patients, the strength of rich-club connection and clustering coefficient significantly increased vs. characteristic path length decreased at 6-month post-operation compared with pre-operation. The distribution pattern of rich-club regions was different in ET groups. Specifically, the order of the rich-club regions was changed according to the network degree value after MRgFUS thalamotomy. Moreover, the altered nodal efficiency in the right temporal pole of the superior temporal gyrus (R = 0.434-0.596) and right putamen (R = 0.413-0.436) was positively correlated with different tremor improvement.

DATA CONCLUSION

These findings might improve understanding of treatment-induced modulation from a network perspective and may work as an objective marker in the assessment of ET tremor control with MRgFUS thalamotomy.

LEVEL OF EVIDENCE

3 TECHNICAL EFFICACY: Stage 4.

Global topology of human connectome is insensitive to early life environments - A prospective longitudinal study of the general population

The widely acknowledged detrimental impact of early adversity on child development has driven efforts to understand the underlying mechanisms that may mediate these effects within the developing brain. Recent efforts have begun to move beyond associating adversity with the morphology of individual brain regions towards determining if and how adversity might shape their interconnectivity. However, whether adversity effects a global shift in the organisation of whole-brain networks remains unclear. In this study, we assessed this possibility using parental questionnaire and diffusion imaging data from The Avon Longitudinal Study of Parents and Children (ALSPAC, N = 913), a prospective longitudinal study spanning more than 20 years. We tested whether a wide range of adversities-including experiences of abuse, domestic violence, physical and emotional cruelty, poverty, neglect, and parental separation-measured by questionnaire within the first seven years of life were significantly associated with the tractography-derived connectome in young adulthood. We tested this across multiple measures of organisation and using a computational model that simulated the wiring economy of the brain. We found no significant relationships between early exposure to any form of adversity and the global organisation of the structural connectome in young adulthood. We did detect local differences in the medial prefrontal cortex, as well as an association between weaker brain wiring constraints and greater externalising behaviour in adolescence. Our results indicate that further efforts are necessary to delimit the magnitude and functional implications of adversity-related differences in connectomic organization. RESEARCH HIGHLIGHTS: Diverse prospective measures of the early-life environment do not predict the organisation of the DTI tractography-derived connectome in young adulthood Wiring economy of the connectome is weakly associated with externalising in adolescence, but not internalising or cognitive ability Further work is needed to establish the scope and significance of global adversity-related differences in the structural connectome.

Age-related differences in affective behaviors in mice: possible role of prefrontal cortical-hippocampal functional connectivity and metabolomic profiles

Introduction

The differential expression of emotional reactivity from early to late adulthood may involve maturation of prefrontal cortical responses to negative valence stimuli. In mice, age-related changes in affective behaviors have been reported, but the functional neural circuitry warrants further investigation.

Methods

We assessed age variations in affective behaviors and functional connectivity in male and female C57BL6/J mice. Mice aged 10, 30 and 60 weeks (wo) were tested over 8 weeks for open field activity, sucrose preference, social interactions, fear conditioning, and functional neuroimaging. Prefrontal cortical and hippocampal tissues were excised for metabolomics.

Results

Our results indicate that young and old mice differ significantly in affective behavioral, functional connectome and prefrontal cortical-hippocampal metabolome. Young mice show a greater responsivity to novel environmental and social stimuli compared to older mice. Conversely, late middle-aged mice (60wo group) display variable patterns of fear conditioning and during re-testing in a modified context. Functional connectivity between a temporal cortical/auditory cortex network and subregions of the anterior cingulate cortex and ventral hippocampus, and a greater network modularity and assortative mixing of nodes was stronger in young versus older adult mice. Metabolome analyses identified differences in several essential amino acids between 10wo mice and the other age groups.

Discussion

The results support differential expression of 'emotionality' across distinct stages of the mouse lifespan involving greater prefrontal-hippocampal connectivity and neurochemistry.

Age-Related Differences in Affective Behaviors in Mice: Possible Role of Prefrontal Cortical-Hippocampal Functional Connectivity and Metabolomic Profiles

The differential expression of emotional reactivity from early to late adulthood may involve maturation of prefrontal cortical responses to negative valence stimuli. In mice, age-related changes in affective behaviors have been reported, but the functional neural circuitry warrants further investigation. We assessed age variations in affective behaviors and functional connectivity in male and female C57BL6/J mice. Mice aged 10, 30 and 60 weeks (wo) were tested over 8 weeks for open field activity, sucrose preference, social interactions, fear conditioning, and functional neuroimaging. Prefrontal cortical and hippocampal tissues were excised for metabolomics. Our results indicate that young and old mice differ significantly in affective behavioral, functional connectome and prefrontal cortical-hippocampal metabolome. Young mice show a greater responsivity to novel environmental and social stimuli compared to older mice. Conversely, late middle-aged mice (60wo group) display variable patterns of fear conditioning and with re-testing with a modified context. Functional connectivity between a temporal cortical/auditory cortex network and subregions of the anterior cingulate cortex and ventral hippocampus, and a greater network modularity and assortative mixing of nodes was stronger in young versus older adult mice. Metabolome analyses identified differences in several essential amino acids between 10wo mice and the other age groups. The results support differential expression of 'emotionality' across distinct stages of the mouse lifespan involving greater prefrontal-hippocampal connectivity and neurochemistry.

Altered cortical thickness-based structural covariance networks in type 2 diabetes mellitus

Cognitive impairment is a common complication of type 2 diabetes mellitus (T2DM), and early cognitive dysfunction may be associated with abnormal changes in the cerebral cortex. This retrospective study aimed to investigate the cortical thickness-based structural topological network changes in T2DM patients without mild cognitive impairment (MCI). Fifty-six T2DM patients and 59 healthy controls underwent neuropsychological assessments and sagittal 3-dimensional T1-weighted structural magnetic resonance imaging. Then, we combined cortical thickness-based assessments with graph theoretical analysis to explore the abnormalities in structural covariance networks in T2DM patients. Correlation analyses were performed to investigate the relationship between the altered topological parameters and cognitive/clinical variables. T2DM patients exhibited significantly lower clustering coefficient (C) and local efficiency (Elocal) values and showed nodal property disorders in the occipital cortical, inferior temporal, and inferior frontal regions, the precuneus, and the precentral and insular gyri. Moreover, the structural topological network changes in multiple nodes were correlated with the findings of neuropsychological tests in T2DM patients. Thus, while T2DM patients without MCI showed a relatively normal global network, the local topological organization of the structural network was disordered. Moreover, the impaired ventral visual pathway may be involved in the neural mechanism of visual cognitive impairment in T2DM patients. This study enriched the characteristics of gray matter structure changes in early cognitive dysfunction in T2DM patients.

Abnormal white matter structural network topological property in patients with temporal lobe epilepsy

BACKGROUND

Diffusion tensor imaging (DTI) studies have demonstrated white matter (WM) abnormalities in patients with temporal lobe epilepsy (TLE). However, alterations in the topological properties of the WM structural network in patients with TLE remain unclear. Graph theoretical analysis provides a new perspective for evaluating the connectivity of WM structural networks.

METHODS

DTI was used to map the structural networks of 18 patients with TLE (10 males and 8 females) and 29 (17 males and 12 females) age- and gender-matched normal controls (NC). Graph theory was used to analyze the whole-brain networks and their topological properties between the two groups. Finally, partial correlation analyses were performed on the weighted network properties and clinical characteristics, namely, duration of epilepsy, verbal intelligence quotient (IQ), and performance IQ.

RESULTS

Patients with TLE exhibited reduced global efficiency and increased characteristic path length. A total of 31 regions with nodal efficiency alterations were detected in the fractional anisotropy_ weighted network of the patients. Communication hubs, such as the middle temporal gyrus, right inferior temporal gyrus, left calcarine, and right superior parietal gyrus, were also differently distributed in the patients compared with the NC. Several node regions showed close relationships with duration of epilepsy, verbal IQ, and performance IQ.

CONCLUSIONS

Our results demonstrate the disruption of the WM structural network in TLE patients. This study may contribute to the further understanding of the pathological mechanism of TLE.

Aging effects on dual-route speech processing networks during speech perception in noise

Healthy aging leads to complex changes in the functional network of speech processing in a noisy environment. The dual-route neural architecture has been applied to the study of speech processing. Although evidence suggests that senescent increases activity in the brain regions across the dorsal and ventral stream regions to offset reduced periphery, the regulatory mechanism of dual-route functional networks underlying such compensation remains largely unknown. Here, by utilizing functional near-infrared spectroscopy (fNIRS), we investigated the compensatory mechanism of the dual-route functional connectivity, and its relationship with healthy aging by using a speech perception task at varying signal-to-noise ratios (SNR) in healthy individuals (young adults, middle-aged adults, and older adults). Results showed that the speech perception scores showed a significant age-related decrease with the reduction of the SNR. The analysis results of dual-route speech processing networks showed that the functional connection of Wernicke's area and homolog Wernicke's area were age-related increases. Further to clarify the age-related characteristics of the dual-route speech processing networks, graph-theoretical network analysis revealed an age-related increase in the efficiency of the networks, and the age-related differences in nodal characteristics were found both in Wernicke's area and homolog Wernicke's area under noise environment. Thus, Wernicke's area might be a key network hub to maintain efficient information transfer across the speech process network with healthy aging. Moreover, older adults would recruit more resources from the homologous Wernicke's area in a noisy environment. The recruitment of the homolog of Wernicke's area might provide a means of compensation for older adults for decoding speech in an adverse listening environment. Together, our results characterized dual-route speech processing networks at varying noise environments and provided new insight for the compensatory theories of how aging modulates the dual-route speech processing functional networks.

White matter-based brain network topological properties associated with individual impulsivity

Delay discounting (DD), a parameter derived from the intertemporal choice task, is a representative behavioral indicator of choice impulsivity. Previous research reported not only an association between DD and impulsive control disorders and negative health outcomes but also the neural correlates of DD. However, to date, there are few studies investigating the structural brain network topologies associated with individual differences in DD and whether self-reported measures (BIS-11) of impulsivity associated with DD share the same or distinct neural mechanisms is still unclear. To address these issues, here, we combined graph theoretical analysis with diffusion tensor imaging to investigate the associations between DD and the topological properties of the structural connectivity network and BIS-11 scores. Results revealed that people with a steep DD (greater impatience) had decreased small-worldness (a shift toward weaker small-worldnization) and increased degree centrality in the medial superior prefrontal cortex, associated with subjective value in the task. Though DD was associated with the BIS-11 motor impulsiveness subscale, this subscale was linked to topological properties different from DD; that is, high motor impulsiveness was associated with decreased local efficiency (less segregation) and decreased degree centrality in the precentral gyrus, involved in motor control. These findings provide insights into the systemic brain characteristics underlying individual differences in impulsivity and potential neural markers which could predict susceptibility to impulsive behaviors.

Intrinsic dynamics of randomly clustered networks generate place fields and preplay of novel environments

During both sleep and awake immobility, hippocampal place cells reactivate time-compressed versions of sequences representing recently experienced trajectories in a phenomenon known as replay. Intriguingly, spontaneous sequences can also correspond to forthcoming trajectories in novel environments experienced later, in a phenomenon known as preplay. Here, we present a model showing that sequences of spikes correlated with the place fields underlying spatial trajectories in both previously experienced and future novel environments can arise spontaneously in neural circuits with random, clustered connectivity rather than pre-configured spatial maps. Moreover, the realistic place fields themselves arise in the circuit from minimal, landmark-based inputs. We find that preplay quality depends on the network's balance of cluster isolation and overlap, with optimal preplay occurring in small-world regimes of high clustering yet short path lengths. We validate the results of our model by applying the same place field and preplay analyses to previously published rat hippocampal place cell data. Our results show that clustered recurrent connectivity can generate spontaneous preplay and immediate replay of novel environments. These findings support a framework whereby novel sensory experiences become associated with preexisting "pluripotent" internal neural activity patterns.

Resolving inter-regional communication capacity in the human connectome

Applications of graph theory to the connectome have inspired several models of how neural signaling unfolds atop its structure. Analytic measures derived from these communication models have mainly been used to extract global characteristics of brain networks, obscuring potentially informative inter-regional relationships. Here we develop a simple standardization method to investigate polysynaptic communication pathways between pairs of cortical regions. This procedure allows us to determine which pairs of nodes are topologically closer and which are further than expected on the basis of their degree. We find that communication pathways delineate canonical functional systems. Relating nodal communication capacity to meta-analytic probabilistic patterns of functional specialization, we also show that areas that are most closely integrated within the network are associated with higher order cognitive functions. We find that these regions' proclivity towards functional integration could naturally arise from the brain's anatomical configuration through evenly distributed connections among multiple specialized communities. Throughout, we consider two increasingly constrained null models to disentangle the effects of the network's topology from those passively endowed by spatial embedding. Altogether, the present findings uncover relationships between polysynaptic communication pathways and the brain's functional organization across multiple topological levels of analysis and demonstrate that network integration facilitates cognitive integration.

Early adversity changes the economic conditions of mouse structural brain network organization

Early adversity can change educational, cognitive, and mental health outcomes. However, the neural processes through which early adversity exerts these effects remain largely unknown. We used generative network modeling of the mouse connectome to test whether unpredictable postnatal stress shifts the constraints that govern the organization of the structural connectome. A model that trades off the wiring cost of long-distance connections with topological homophily (i.e., links between regions with shared neighbors) generated simulations that successfully replicate the rodent connectome. The imposition of early life adversity shifted the best-performing parameter combinations toward zero, heightening the stochastic nature of the generative process. Put simply, unpredictable postnatal stress changes the economic constraints that reproduce rodent connectome organization, introducing greater randomness into the development of the simulations. While this change may constrain the development of cognitive abilities, it could also reflect an adaptive mechanism that facilitates effective responses to future challenges.

Different topological patterns in structural covariance networks between high and low delay discounters

Introduction

People prefer immediate over future rewards because they discount the latter's value (a phenomenon termed "delay discounting," used as an index of impulsivity). However, little is known about how the preferences are implemented in brain in terms of the coordinated pattern of large-scale structural brain networks.

Methods

To examine this question, we classified high discounting group (HDG) and low discounting group (LDG) in young adults by assessing their propensity for intertemporal choice. We compared global and regional topological properties in gray matter volume-based structural covariance networks between two groups using graph theoretical analysis.

Results

HDG had less clustering coefficient and characteristic path length over the wide sparsity range than LDG, indicating low network segregation and high integration. In addition, the degree of small-worldness was more significant in HDG. Locally, HDG showed less betweenness centrality (BC) in the parahippocampal gyrus and amygdala than LDG.

Discussion

These findings suggest the involvement of structural covariance network topology on impulsive choice, measured by delay discounting, and extend our understanding of how impulsive choice is associated with brain morphological features.

Parkinson's disease speech production network as determined by graph-theoretical network analysis

Parkinson's disease (PD) can affect speech as well as emotion processing. We employ whole-brain graph-theoretical network analysis to determine how the speech-processing network (SPN) changes in PD, and assess its susceptibility to emotional distraction. Functional magnetic resonance images of 14 patients (aged 59.6 ± 10.1 years, 5 female) and 23 healthy controls (aged 64.1 ± 6.5 years, 12 female) were obtained during a picture-naming task. Pictures were supraliminally primed by face pictures showing either a neutral or an emotional expression. PD network metrics were significantly decreased (mean nodal degree, p < 0.0001; mean nodal strength, p < 0.0001; global network efficiency, p < 0.002; mean clustering coefficient, p < 0.0001), indicating an impairment of network integration and segregation. There was an absence of connector hubs in PD. Controls exhibited key network hubs located in the associative cortices, of which most were insusceptible to emotional distraction. The PD SPN had more key network hubs, which were more disorganized and shifted into auditory, sensory, and motor cortices after emotional distraction. The whole-brain SPN in PD undergoes changes that result in (a) decreased network integration and segregation, (b) a modularization of information flow within the network, and (c) the inclusion of primary and secondary cortical areas after emotional distraction.

Topological abnormality of structural covariance network in MRI-negative frontal lobe epilepsy

Background

Frontal lobe epilepsy (FLE) is the second most common type of focal epilepsy, however, imaging studies of FLE have been far less than Temporal lobe epilepsy (TLE) and the structural findings were not consistent in previous literature.

Object

Investigate the changes in cortical thickness in patients with FLE and the alteration of the structural covariance networks (SCNs) of cortical thickness with graph-theory.

Method

Thirty patients with FLE (18 males/12 females; 28.33 ± 11.81 years) and 27 demographically matched controls (15 males/12 females; 29.22 ± 9.73 years) were included in this study with high-resolution structural brain MRI scans. The cortical thickness was calculated, and structural covariance network (SCN) of cortical thickness were reconstructed using 68 × 68 matrix and analyzed with graph-theory approach.

Result

Cortical thickness was not significantly different between two groups, but path length and node betweenness were significantly increased in patients with FLE, and the regional network alterations were significantly changed in right precentral gyrus and right temporal pole (FDR corrected, p < 0.05). Comparing to HC group, network hubs were decreased and shifted away from frontal lobe.

Conclusion

The topological properties of cortical thickness covariance network were significantly altered in patients with FLE, even without obvious surface-based morphological damage. Graph-theory based SCN analysis may provide sensitive neuroanatomical biomarkers for FLE.

How can graph theory inform the dual-stream model of speech processing? a resting-state fMRI study of post-stroke aphasia

The dual-stream model of speech processing has been proposed to represent the cortical networks involved in speech comprehension and production. Although it is arguably the prominent neuroanatomical model of speech processing, it is not yet known if the dual-stream model represents actual intrinsic functional brain networks. Furthermore, it is unclear how disruptions after a stroke to the functional connectivity of the dual-stream model's regions are related to specific types of speech production and comprehension impairments seen in aphasia. To address these questions, in the present study, we examined two independent resting-state fMRI datasets: (1) 28 neurotypical matched controls and (2) 28 chronic left-hemisphere stroke survivors with aphasia collected at another site. Structural MRI, as well as language and cognitive behavioral assessments, were collected. Using standard functional connectivity measures, we successfully identified an intrinsic resting-state network amongst the dual-stream model's regions in the control group. We then used both standard functional connectivity analyses and graph theory approaches to determine how the functional connectivity of the dual-stream network differs in individuals with post-stroke aphasia, and how this connectivity may predict performance on clinical aphasia assessments. Our findings provide strong evidence that the dual-stream model is an intrinsic network as measured via resting-state MRI, and that weaker functional connectivity of the hub nodes of the dual-stream network defined by graph theory methods, but not overall average network connectivity, is weaker in the stroke group than in the control participants. Also, the functional connectivity of the hub nodes predicted specific types of impairments on clinical assessments. In particular, the relative strength of connectivity of the right hemisphere's homologues of the left dorsal stream hubs to the left dorsal hubs versus right ventral stream hubs is a particularly strong predictor of post-stroke aphasia severity and symptomology.

Epileptic prediction using spatiotemporal information combined with optimal features strategy on EEG

Objective

Epilepsy is the second most common brain neurological disease after stroke, which has the characteristics of sudden and recurrence. Seizure prediction is seriously important for improving the quality of patients' lives.

Methods

From the perspective of multiple dimensions including time-frequency, entropy and brain network, this paper proposed a novel approach by constructing the optimal spatiotemporal feature set to predict seizures. Based on strong independence and large information capabilities, the two-dimensional feature screening algorithm is performed to eliminate unnecessary redundant features. In order to verify the effectiveness of the optimal feature set, support vector machine (SVM) was used to classify the preictal and interictal states on both the Kaggle intracranial EEG and CHB-MIT scalp EEG dataset.

Results

This model achieved an average accuracy of 98.01%, AUC of 0.96, F-Score of 98.3% and FPR of 0.0383/h on the Kaggle dataset; On the CHB-MIT dataset, the average accuracy, AUC, F-score and FPR were 95.93%, 0.92, 94.97% and 0.0473/h, respectively. Further ablation experiments have confirmed that the temporal and spatial features fusion has better performance than the individual temporal or spatial features.

Conclusion

Compared to the state-of-the-art methods, our approach outperforms most of these existing techniques. The results show that our approach can effectively extract the spatiotemporal information of epileptic EEG signals to predict epileptic seizures with high performance.

Central nodes of canine functional brain networks are concentrated in the cingulate gyrus

Compared to the field of human fMRI, knowledge about functional networks in dogs is scarce. In this paper, we present the first anatomically-defined ROI (region of interest) based functional network map of the companion dog brain. We scanned 33 awake dogs in a "task-free condition". Our trained subjects, similarly to humans, remain willingly motionless during scanning. Our goal is to provide a reference map with a current best estimate for the organisation of the cerebral cortex as measured by functional connectivity. The findings extend a previous spatial ICA (independent component analysis) study (Szabo et al. in Sci Rep 9(1):1.25. https://doi.org/10.1038/s41598-019-51752-2 , 2019), with the current study including (1) more subjects and (2) improved scanning protocol to avoid asymmetric lateral distortions. In dogs, similarly to humans (Sacca et al. in J Neurosci Methods. https://doi.org/10.1016/j.jneumeth.2021.109084 , 2021), ageing resulted in increasing framewise displacement (i.e. head motion) in the scanner. Despite the inherently different approaches between model-free ICA and model-based ROI, the resulting functional networks show a remarkable similarity. However, in the present study, we did not detect a designated auditory network. Instead, we identified two highly connected, lateralised multi-region networks extending to non-homotropic regions (Sylvian L, Sylvian R), including the respective auditory regions, together with the associative and sensorimotor cortices and the insular cortex. The attention and control networks were not split into two fully separated, dedicated networks. Overall, in dogs, fronto-parietal networks and hubs were less dominant than in humans, with the cingulate gyrus playing a central role. The current manuscript provides the first attempt to map whole-brain functional networks in dogs via a model-based approach.

Brain plasticity of structural connectivity networks and topological properties in baseball players with different levels of expertise

Brain plasticity in structural connectivity networks along the development of expertise has remained largely unknown. To address this, we recruited individuals with three different levels of baseball-playing experience: skilled batters (SB), intermediate batters (IB), and healthy controls (HC). We constructed their structural connectivity networks using diffusion tractography and compared their region-to-region structural connections and the topological characteristics of the constructed networks using graph-theoretical analysis. The group differences were detected in 35 connections predominantly involving sensorimotor and visual systems; the intergroup changes could be depicted either in a stepwise (HC < / = IB < / = SB) or a U-/inverted U-shaped manner as experience increased (IB < SB and/or HC, or opposite). All groups showed small-world topology in their constructed networks, but SB had increased global and local network efficiency than IB and/or HC. Furthermore, although the number and cortical regions identified as hubs of the networks in the three groups were highly similar, SB exhibited higher nodal global efficiency in both the dorsolateral and medial parts of the bilateral superior frontal gyri than IB. Our findings add new evidence of topological reorganization in brain networks associated with sensorimotor experience in sports. Interestingly, these changes do not necessarily increase as a function of experience as previously suggested in literature.

Altered White Matter Network Topology in Panic Disorder

Panic disorder (PD) is an anxiety disorder that impairs life quality and social function and is associated with distributed brain regions. However, the alteration of the structural network remains unclear in PD patients. This study explored the specific characteristics of the structural brain network in patients with PD by graph theory analysis of diffusion tensor images (DTI). A total of 81 PD patients and 48 matched healthy controls were recruited for this study. The structural networks were constructed, and the network topological properties for individuals were estimated. At the global level, the network efficiency was higher, while the shortest path length and clustering coefficient were lower in the PD group compared to the healthy control (HC) group. At the nodal level, the PD group showed a widespread higher nodal efficiency and lower average shortest path length in the prefrontal, sensorimotor, limbic, insula, and cerebellum regions. Overall, the current results showed that the alteration of information processing in the fear network might play a role in the pathophysiology of PD.

Abnormal Properties of Cortical Functional Brain Network in Major Depressive Disorder: Graph Theory Analysis Based on Electroencephalography-Source Estimates

Studies of scalp electroencephalography (EEG) had shown altered topological organization of functional brain networks in patients with major depressive disorder (MDD). However, most previous EEG-based network analyses were performed at sensor level, while the interpretation of obtained results was not straightforward due to volume conduction effect. To reduce the impact of this defect, the whole cortical functional brain networks of MDD patients were studied during resting state based on EEG-source estimates in this paper. First, scalp EEG signals were recorded from 19 patients with MDD and 20 normal controls under resting eyes-closed state, and cortical neural signals were estimated by using sLORETA method. Then, the correntropy coefficient of wavelet packet coefficients was performed to calculate functional connectivity (FC) matrices in four different frequency bands: δ, θ, α, β, respectively. Afterwards, topological properties of brain networks were analyzed by graph theory approaches. The results showed that the global FC strength of MDD patients was significantly higher than that of healthy subjects in α band. Also, it was found that MDD patients have abnormally increased clustering coefficient and local efficiency in both α and β bands compared to normal people. Furthermore, patients with MDD exhibited increased nodal clustering coefficients in the left lingual gryus and left precuneus in α band. In addition, β band global clustering coefficient was positively correlated with the scores of depression severity. Therefore, the findings indicated the cortical functional brain networks in MDD patients were disruptions, which suggested it would be one of potential causes of depression.

Intrinsic Organization of Occipital Hubs Predicts Depression: A Resting-State fNIRS Study

Dysfunctional brain networks have been found in patients with major depressive disorder (MDD). In this study, to verify this in a more straightforward way, we investigated the intrinsic organization of brain networks in MDD by leveraging the resting-state functional near-infrared spectroscopy (rs-fNIRS). Thirty-four MDD patients (24 females, 38.41 ± 13.14 years old) and thirty healthy controls (22 females, 34.43 ± 5.03 years old) underwent a 10 min rest while their brain activity was recorded via fNIRS. The results showed that MDD patients and healthy controls exhibited similar resting-state functional connectivity. Moreover, the depression group showed lower small-world Lambda (1.12 ± 0.04 vs. 1.16 ± 0.10, p = 0.04) but higher global efficiency (0.51 ± 0.03 vs. 0.48 ± 0.05, p = 0.03) than the control group. Importantly, MDD patients, as opposed to healthy controls, showed a significantly lower nodal local efficiency at the left middle occipital gyrus (0.56 ± 0.36 vs. 0.81 ± 0.20, pFDR < 0.05), which predicted the level of depression in MDD (r = 0.45, p = 0.01, R2 = 0.15). In sum, we found a more integrated brain network in MDD patients with a lower nodal local efficiency at the occipital hub, which could predict depressive symptoms.

The thresholding problem and variability in the EEG graph network parameters

Graph thresholding is a frequently used practice of eliminating the weak connections in brain functional connectivity graphs. The main aim of the procedure is to delete the spurious connections in the data. However, the choice of the threshold is arbitrary, and the effect of the threshold choice is not fully understood. Here we present the description of the changes in the global measures of a functional connectivity graph depending on the different proportional thresholds based on the 146 resting-state EEG recordings. The dynamics is presented in five different synchronization measures (wPLI, ImCoh, Coherence, ciPLV, PPC) in sensors and source spaces. The analysis shows significant changes in the graph's global connectivity measures as a function of the chosen threshold which may influence the outcome of the study. The choice of the threshold could lead to different study conclusions; thus it is necessary to improve the reasoning behind the choice of the different analytic options and consider the adoption of different analytic approaches. We also proposed some ways of improving the procedure of thresholding in functional connectivity research.

Disrupted topological properties of the structural brain network in patients with cerebellar infarction on different sides are associated with cognitive impairment

Purpose

To explore changes in the brain structural network in patients with cerebellar infarction on different sides and their correlations with changes in cognitive function.

Methods

Nineteen patients with acute left posterior cerebellar infarction and 18 patients with acute right posterior cerebellar infarction seen from July 2016 to September 2019 in the Department of Neurology, Affiliated Brain Hospital of Nanjing Medical University, were selected. A total of 27 healthy controls matched for sex, age, and years of education were recruited. The subjects underwent head diffusion magnetic resonance imaging examination and neuropsychological cognitive scale evaluation, and we analyzed changes in brain structural network properties in patients with cerebellar infarction and their correlation with changes in patients' cognitive function.

Results

The Mini-Mental Status Examination (MMSE), Montreal Cognitive Assessment (MOCA) and the Rey auditory verbal learning test (RAVLT) scores in the left and right cerebellar infarction groups were significantly lower than those in the healthy control group (p < 0.05). In addition, the digit span test (DST) scores were lower in the left cerebellar infarction group (p < 0.05); the trail-making test (TMT) times in the right cerebellar infarction group were significantly higher than those in the left cerebellar infarction group (p < 0.05). Meanwhile, the left and right cerebellar infarction groups had abnormal brain topological properties, including clustering coefficient, shortest path length, global efficiency, local efficiency and nodal efficiency. After unilateral cerebellar infarction, bilateral cerebral nodal efficiency was abnormal. Correlation analysis showed that there was a close correlation between decreased processing speed in patients with left cerebellar infarction and decreased efficiency of right cerebral nodes (p < 0.05), and there was a close relationship between executive dysfunction and decreased efficiency of left cerebral nodes in patients with right cerebellar infarction (p < 0.05).

Conclusion

Patients with cerebellar infarction have cognitive impairment. Unilateral cerebellar infarction can reduce the network efficiency of key regions in the bilateral cerebral hemispheres, and these abnormal changes are closely related to patient cognitive impairment. The results of this study provide evidence for understanding the underlying neural mechanisms of cerebellar cognitive impairment and suggest that brain topological network properties may be markers of cerebellar cognitive impairment.

A new model for freedom of movement using connectomic analysis

The problem of whether we can execute free acts or not is central in philosophical thought, and it has been studied by numerous scholars throughout the centuries. Recently, neurosciences have entered this topic contributing new data and insights into the neuroanatomical basis of cognitive processes. With the advent of connectomics, a more refined landscape of brain connectivity can be analysed at an unprecedented level of detail. Here, we identify the connectivity network involved in the movement process from a connectomics point of view, from its motivation through its execution until the sense of agency develops. We constructed a "volitional network" using data derived from the Brainnetome Atlas database considering areas involved in volitional processes as known in the literature. We divided this process into eight processes and used Graph Theory to measure several structural properties of the network. Our results show that the volitional network is small-world and that it contains four communities. Nodes of the right hemisphere are contained in three of these communities whereas nodes of the left hemisphere only in two. Centrality measures indicate the nucleus accumbens is one of the most connected nodes in the network. Extensive connectivity is observed in all processes except in Decision (to move) and modulation of Agency, which might correlate with a mismatch mechanism for perception of Agency.

Percolation may explain efficiency, robustness, and economy of the brain

The brain consists of billions of neurons connected by ultra-dense synapses, showing remarkable efficiency, robust flexibility, and economy in information processing. It is generally believed that these advantageous properties are rooted in brain connectivity; however, direct evidence remains absent owing to technical limitations or theoretical vacancy. This research explores the origins of these properties in the largest yet brain connectome of the fruit fly. We reveal that functional connectivity formation in the brain can be explained by a percolation process controlled by synaptic excitation-inhibition (E/I) balance. By increasing the E/I balance gradually, we discover the emergence of these properties as byproducts of percolation transition when the E/I balance arrives at 3:7. As the E/I balance keeps increase, an optimal E/I balance 1:1 is unveiled to ensure these three properties simultaneously, consistent with previous in vitro experimental predictions. Once the E/I balance reaches over 3:2, an intrinsic limitation of these properties determined by static (anatomical) brain connectivity can be observed. Our work demonstrates that percolation, a universal characterization of critical phenomena and phase transitions, may serve as a window toward understanding the emergence of various brain properties.

Reorganization of prefrontal network in stroke patients with dyskinesias: evidence from resting-state functional near-infrared spectroscopy

Stroke usually causes multiple functional disability. To develop novel rehabilitation strategies, it is quite necessary to improve the understanding of post-stroke brain plasticity. Here, we use functional near-infrared spectroscopy to investigate the prefrontal cortex (PFC) network reorganization in stroke patients with dyskinesias. The PFC hemodynamic signals in the resting state from 16 stroke patients and 10 healthy subjects are collected and analyzed with the graph theory. The PFC networks for both groups show small-world attributes. The stroke patients have larger clustering coefficient and transitivity and smaller global efficiency and small-worldness than healthy subjects. Based on the selected network features, the established support vector machine model classifies the two groups of subjects with an accuracy rate of 88.5%. Besides, the clustering coefficient and local efficiency negatively correlate with patients' motor function. This study suggests that the PFC of stroke patients with dyskinesias undergoes specific network reorganization.

Characterization of Cognitive Function in Survivors of Diffuse Gliomas Using Morphometric Correlation Networks

This pilot study investigates structural alterations and their relationships with cognitive function in survivors of diffuse gliomas. Twenty-four survivors of diffuse gliomas (mean age 44.5 ± 11.5), from whom high-resolution T1-weighted images, neuropsychological tests, and self-report questionnaires were obtained, were analyzed. Patients were grouped by degree of cognitive impairment, and interregional correlations of cortical thickness were computed to generate morphometric correlation networks (MCNs). The results show that the cortical thickness of the right insula (R² = 0.3025, p = 0.0054) was negatively associated with time since the last treatment, and the cortical thickness of the left superior temporal gyrus (R² = 0.2839, p = 0.0107) was positively associated with cognitive performance. Multiple cortical regions in the default mode, salience, and language networks were identified as predominant nodes in the MCNs of survivors of diffuse gliomas. Compared to cognitively impaired patients, cognitively non-impaired patients tended to have higher network stability in network nodes removal analysis, especially when the fraction of removed nodes (among 66 nodes in total) exceeded 55%. These findings suggest that structural networks are altered in survivors of diffuse gliomas and that their cortical structures may also be adapting to support cognitive function during survivorship.

Aberrant topological organization and age-related differences in the human connectome in subjective cognitive decline by using regional morphology from magnetic resonance imaging

Subjective cognitive decline (SCD) is characterized by self-experienced deficits in cognitive capacity with normal performance in objective cognitive tests. Previous structural covariance studies showed specific insights into understanding the structural alterations of the brain in neurodegenerative diseases. Moreover, in subjects with neurodegenerative diseases, accelerated brain degeneration with aging was shown. However, the age-related variations in coordinated topological patterns of morphological networks in individuals with SCD remain poorly understood. In this study, 77 individual morphological networks were constructed, including 42 normal controls (NCs) and 35 SCD individuals, from structural magnetic resonance imaging (sMRI). A stepwise linear regression model and partial correlation analysis were constructed to evaluate the differences in age-related alterations of the network properties in individuals with SCD compared with NCs. Compared with NC, the properties of integration and segregation in individuals with SCD were lower, and the aberrant metrics were negatively correlated with age in SCD. The rich-club connections persevered, but the paralimbic system connections were disrupted in individuals with SCD compared with NCs. In addition, age-related differences in nodal global efficiency are distributed mainly in prefrontal cortex regions. In conclusion, the age-related disruption of topological organizations in individuals with SCD may indicate that the degeneration of brain efficiency with aging was accelerated in individuals with SCD.

Abnormal innate and learned behavior induced by neuron-microglia miscommunication is related to CA3 reconfiguration

Neuron-microglia communication through the Cx3cr1-Cx3cl1 axis is essential for the development and refinement of neural circuits, which determine their function into adulthood. In the present work we set out to extend the behavioral characterization of Cx3cr1^-/- mice evaluating innate behaviors and spatial navigation, both dependent on hippocampal function. Our results show that Cx3cr1-deficient mice, which show some changes in microglial and synaptic terminals morphology and density, exhibit alterations in activities of daily living and in the rapid encoding of novel spatial information that, nonetheless, improves with training. A neural substrate for these cognitive deficiencies was found in the form of synaptic dysfunction in the CA3 region of the hippocampus, with a marked impact on the mossy fiber (MF) pathway. A network analysis of the CA3 microcircuit reveals the effect of these synaptic alterations on the functional connectivity among CA3 neurons with diminished strength and topological reorganization in Cx3cr1-deficient mice. Neonatal population activity of the CA3 region in Cx3cr1-deficient mice shows a marked reorganization around the giant depolarizing potentials, the first form of network-driven activity of the hippocampus, suggesting that alterations found in adult subjects arise early on in postnatal development, a critical period of microglia-dependent neural circuit refinement. Our results show that interruption of the Cx3cr1-Cx3cl1/neuron-microglia axis leads to changes in CA3 configuration that affect innate and learned behaviors.

Resting-State Functional Network Topology Alterations of the Occipital Lobe Associated With Attention Impairment in Isolated Rapid Eye Movement Behavior Disorder

Purpose

This study investigates the topological properties of brain functional networks in patients with isolated rapid eye movement sleep behavior disorder (iRBD).

Participants and Methods

A total of 21 patients with iRBD (iRBD group) and 22 healthy controls (HCs) were evaluated using resting-state functional MRI (rs-fMRI) and neuropsychological measures in cognitive and motor function. Data from rs-fMRI were analyzed using graph theory, which included small-world properties, network efficiency, network local efficiency, nodal shortest path, node efficiency, and network connectivity, as well as the relationship between behavioral characteristics and altered brain topological features.

Results

Rey-Osterrieth complex figure test (ROCFT-copy), symbol digital modalities test (SDMT), auditory verbal learning test (AVLT)-N1, AVLT-N2, AVLT-N3, and AVLT-N1-3 scores were significantly lower in patients with iRBD than in HC (P &lt; 0.05), while trail making test A (TMT-A), TMT-B, and Unified Parkinson’s Disease Rating Scale Part-III (UPDRS-III) scores were higher in patients with iRBD (P &lt; 0.05). Compared with the HCs, patients with iRBD had no difference in the small-world attributes (P &gt; 0.05). However, there was a significant decrease in network global efficiency (P = 0.0052) and network local efficiency (P = 0.0146), while an increase in characteristic path length (P = 0.0071). There was lower nodal efficiency in occipital gyrus and nodal shortest path in frontal, parietal, temporal lobe, and cingulate gyrus. Functional connectivities were decreased between the nodes of occipital with the regions where they had declined nodal shortest path. There was a positive correlation between TMT-A scores and the nodal efficiency of the right middle occipital gyrus (R = 0.602, P = 0.014).

Conclusion

These results suggest that abnormal behaviors may be associated with disrupted brain network topology and functional connectivity in patients with iRBD and also provide novel insights to understand pathophysiological mechanisms in iRBD.

Dynamic balance deficit and the neural network in Alzheimer's disease and mild cognitive impairment

BACKGROUND

Patients with Alzheimer's disease (AD) and mild cognitive impairment (MCI) exhibit balance deficits. Although only a few studies have evaluated the relationship between the brain images and balance indices. In this study, we measured balance indices, including the index of postural stability (IPS) and assessed the relationship between the brain images and their clinical motor and cognitive functional features.

METHODS

The study included patients with MCI (N = 14) and patients with AD (N = 19). The primary outcome was IPS under a visual block condition and/or a proprioception block condition. In addition, 9 MCI and 8 AD patients underwent a 1.5-Tesla (1.5-T) Magnetic Resonance Imaging (MRI) scan, and the relationships between the MRI parameters and the balance indices were evaluated.

RESULTS

The IPS score was significantly lower in the AD group than the MCI group, but only under the closed eyes/hard surface condition. In terms of MRI, there was a significant positive correlation between the IPS and the regional betweenness centrality in the left hippocampal region.

CONCLUSIONS

The finding of a significantly lower IPS score under the closed eyes/hard surface condition in AD than in MCI cases suggests that the vestibular and/or proprioceptive systems were more severely impaired in AD than MCI cases. The results suggest that a dynamic balance disturbance due to deficits of the vestibular hippocampal pathway may be a useful marker for the diagnosis of MCI and detection of disease progression from MCI to AD.

Functional Network Changes After High-Frequency rTMS Over the Most Activated Speech-Related Area Combined With Speech Therapy in Chronic Stroke With Non-fluent Aphasia

OBJECTIVE

High-frequency repetitive transcranial magnetic stimulation (HF-rTMS) to the lesional hemisphere requires prudence in selecting the appropriate stimulation spot. Functional near-IR spectroscopy (fNIRS) can be used in both selecting the stimulation spot and assessing the changes of the brain network. This study aimed to evaluate the effect of HF-rTMS on the most activated spot identified with fNIRS and assess the changes of brain functional network in the patients with poststroke aphasia.

METHODS

A total of five patients received HF-rTMS to the most activated area on the lesional hemisphere, followed by 30 min of speech therapy for 10 days. The Korean version of the Western aphasia battery (K-WAB) and fNIRS evaluation were done 1 day before the treatment, 1 day and 1 month after the last treatment session. Changes of K-WAB and paired cortical interaction and brain network analysis using graph theory were assessed.

RESULTS

Aphasia quotient in K-WAB significantly increased after the treatment (P = 0.043). The correlation analysis of cortical interactions showed increased connectivity between language production and processing areas. Clustering coefficients of the left hemisphere were increased over a sparsity range between 0.45 and 0.58 (0.015 < p < 0.031), whereas the clustering coefficients of the right hemisphere, decreased over a sparsity range 0.15-0.87 (0.063 < p < 0.095). The global efficiency became lower over a network sparsity range between 0.47 and 0.75 (0.015 < p < 0.063).

CONCLUSION

Improvement of language function and changes of corticocortical interaction between language-related cortical areas were observed after HF-rTMS on the most activated area identified by fNIRS with combined speech therapy in the patients with poststroke aphasia.

Functional Brain Networks in Preschool Children With Autism Spectrum Disorders

OBJECTIVE

The present study aims to investigate the functional brain network characteristics of preschool children with autism spectrum disorder (ASD) through functional connectivity (FC) calculations using resting-state functional MRI (rs-fMRI) and graph theory analysis to better understand the pathogenesis of ASD and provide imaging evidence for the early assessment of this condition.

METHODS

A prospective study of preschool children including 32 with ASD (ASD group) and 22 healthy controls (HC)group was conducted in which all subjects underwent rs-fMRI scans, and then the differences in FC between the two groups was calculated, followed by graph-theoretic analysis to obtain the FC properties of the network.

RESULTS

In the calculation of FC, compared with the children in the HC group, significant increases or decreases in subnetwork connectivity was found in the ASD group. There were 25 groups of subnetworks with enhanced FC, of which the medial prefrontal and posterior cingulate gyrus and angular gyrus were all important components of the default mode network (DMN). There were 11 groups of subnetworks with weakened FC, including the hippocampus, parahippocampal gyrus, superior frontal gyrus, inferior temporal gyrus, precuneus, amygdala, and perirhinal cortex, with the hippocampus and parahippocampal gyrus predominating. In the network properties determined by graph theory, the clustering coefficient and local efficiency of the functional network was increased in the ASD group; specifically, compared with those in the HC group, nodes in the left subinsular frontal gyrus and the right middle temporal gyrus had increased efficiency, and nodes in the left perisylvian cortex, the left lingual gyrus, and the right hippocampus had decreased efficiency.

CONCLUSION

Alterations in functional brain networks are evident in preschool children with ASD and can be detected with sleep rs-fMRI, which is important for understanding the pathogenesis of ASD and assessing this condition early.

Topological Characteristics Associated with Intraoperative Stimulation Related Epilepsy of Glioma Patients: A DTI Network Study

Background: Awake craniotomy with intraoperative stimulation has been utilized in glioma surgical resection to preserve the quality of life. Epilepsy may occur in 5–20% of cases, leading to severe consequences. This study aimed to discuss the mechanism of intraoperative stimulation-related epilepsy (ISE) using DTI-based graph theoretical analysis. Methods: Twenty patients with motor-area glioma were enrolled and divided into two groups (Ep and nEp) according to the presence of ISE. Additionally, a group of 10 healthy participants matched by age, sex, and years of education was also included. All participants underwent T1, T2, and DTI examinations. Graph theoretical analysis was applied to reveal the topological characteristics of white matter networks. Results: Three connections were found to be significantly lower in at least one weighting in the Ep group. These connections were between A1/2/3truL and A4ulL, A1/2/3truR and A4tR, and A6mL and A6mR. Global efficiency was significantly decreased, while the shortest path length increased in the Ep group in at least one weighting. Ten nodes exhibited significant differences in nodal efficiency and degree centrality analyses. The nodes A6mL and A6mR showed a marked decrease in total four weightings in the Ep group. Conclusions: The hub nodes A6mL and A6mR are disconnected in patients with ISE, causing subsequent lower efficiency of global and regional networks. These findings provide a basis for presurgical assessment of ISE, for which caution should be taken when it involves hub nodes during intraoperative electrical stimulation.

Functional Neural Networks in Writer's Cramp as Determined by Graph-Theoretical Analysis

Dystonia, a debilitating neurological movement disorder, is characterized by involuntary muscle contractions and develops from a complex pathophysiology. Graph theoretical analysis approaches have been employed to investigate functional network changes in patients with different forms of dystonia. In this study, we aimed to characterize the abnormal brain connectivity underlying writer's cramp, a focal hand dystonia. To this end, we examined functional magnetic resonance scans of 20 writer's cramp patients (11 females/nine males) and 26 healthy controls (10 females/16 males) performing a sequential finger tapping task with their non-dominant (and for patients non-dystonic) hand. Functional connectivity matrices were used to determine group averaged brain networks. Our data suggest that in their neuronal network writer's cramp patients recruited fewer regions that were functionally more segregated. However, this did not impair the network's efficiency for information transfer. A hub analysis revealed alterations in communication patterns of the primary motor cortex, the thalamus and the cerebellum. As we did not observe any differences in motor outcome between groups, we assume that these network changes constitute compensatory rerouting within the patient network. In a secondary analysis, we compared patients with simple writer's cramp (only affecting the hand while writing) and those with complex writer's cramp (affecting the hand also during other fine motor tasks). We found abnormal cerebellar connectivity in the simple writer's cramp group, which was less prominent in complex writer's cramp. Our preliminary findings suggest that longitudinal research concerning cerebellar connectivity during WC progression could provide insight on early compensatory mechanisms in WC.

Intrinsic brainstem circuits comprised of Chx10-expressing neurons contribute to reticulospinal output in mice

Glutamatergic reticulospinal neurons in the gigantocellular reticular nucleus (GRN) of the medullary reticular formation can function as command neurons, transmitting motor commands to spinal cord circuits to instruct movement. Recent advances in our understanding of this neuron-dense region have been facilitated by the discovery of expression of the transcriptional regulator, Chx10, in excitatory reticulospinal neurons. Here, we address the capacity of local circuitry in the GRN to contribute to reticulospinal output. We define two subpopulations of Chx10-expressing neurons in this region, based on distinct electrophysiological properties and soma size (small and large), and show that these populations correspond to local interneurons and reticulospinal neurons, respectively. Using focal release of caged glutamate combined with patch clamp recordings, we demonstrated that Chx10 neurons form microcircuits in which the Chx10 local interneurons project to and facilitate the firing of Chx10 reticulospinal neurons. We discuss the implications of these microcircuits in terms of movement selection.

NEW & NOTEWORTHY Reticulospinal neurons in the medullary reticular formation integrate inputs from higher regions to effectively instruct spinal motor circuits. Using photoactivation of neurons in brainstem slices, we studied connectivity of reticular formation neurons that express the transcriptional regulator, Chx10. We show that a subpopulation of these neurons functions as local interneurons that affect descending commands. The results shed light on the internal organization and microcircuit formation of reticular formation neurons.

Analyses of Contact Networks of Community Dogs on a University Campus in Nakhon Pathom, Thailand

Free-roaming dogs have been identified as an important reservoir of rabies in many countries including Thailand. There is a need for novel insights to improve current rabies control strategies in these countries. Network analysis is commonly used to study the interactions between individuals or organizations and has been applied in preventive veterinary medicine. However, contact networks of domestic free-roaming dogs are mostly unexplored. The objective of this study was to explore the contact network of free-roaming dogs residing on a university campus. Three one-mode networks were created using co-appearances of dogs as edges. A two-mode network was created by associating the dog with the pre-defined area it was seen in. The average number of contacts a dog had was 6.74. The normalized degree for the weekend network was significantly higher compared to the weekday network. All one-mode networks displayed small-world network characteristics. Most dogs were observed in only one area. The average number of dogs which shared an area was 8.67. In this study, we demonstrated the potential of observational methods to create networks of contacts. The network information acquired can be further used in network modeling and designing targeted disease control programs.

Bridging the Functional and Wiring Properties of V1 Neurons Through Sparse Coding

The functional properties of neurons in the primary visual cortex (V1) are thought to be closely related to the structural properties of this network, but the specific relationships remain unclear. Previous theoretical studies have suggested that sparse coding, an energy-efficient coding method, might underlie the orientation selectivity of V1 neurons. We thus aimed to delineate how the neurons are wired to produce this feature. We constructed a model and endowed it with a simple Hebbian learning rule to encode images of natural scenes. The excitatory neurons fired sparsely in response to images and developed strong orientation selectivity. After learning, the connectivity between excitatory neuron pairs, inhibitory neuron pairs, and excitatory-inhibitory neuron pairs depended on firing pattern and receptive field similarity between the neurons. The receptive fields (RFs) of excitatory neurons and inhibitory neurons were well predicted by the RFs of presynaptic excitatory neurons and inhibitory neurons, respectively. The excitatory neurons formed a small-world network, in which certain local connection patterns were significantly overrepresented. Bidirectionally manipulating the firing rates of inhibitory neurons caused linear transformations of the firing rates of excitatory neurons, and vice versa. These wiring properties and modulatory effects were congruent with a wide variety of data measured in V1, suggesting that the sparse coding principle might underlie both the functional and wiring properties of V1 neurons.

Diffusion tensor imaging brain structural clustering patterns in major depressive disorder

Using magnetic resonance diffusion tensor imaging data from 45 patients with major depressive disorder (MDD) and 41 healthy controls (HCs), network indices based on a 246-region Brainnetcome Atlas were investigated in the two groups, and in the MDD subgroups that were subgrouped based on their duration of the disease. Correlation between the network indices and the duration of illness was also examined. Differences were observed between the MDDS subgroup (short disease duration) and the HC group, but not between the MDD and HC groups. Compared with the HCs, the clustering coefficient (CC) values of MDDS were higher in precentral gyrus, and caudal lingual gyrus; the CC of MDDL subgroup (long disease duration) was higher in postcentral gyrus and dorsal granular insula in the right hemisphere. Network resilience analyses showed that the MDDS group was higher than the HC group, representing relatively more randomized networks in the diseased brains. The correlation analyses showed that the caudal lingual gyrus in the right hemisphere and the rostral lingual gyrus in the left hemisphere were particularly correlated with disease duration. The analyses showed that duration of the illness appears to have an impact on the networking patterns. Networking abnormalities in MDD patients could be blurred or hidden by the heterogeneity of the MDD clinical subgroups. Brain plasticity may introduce a recovery effect to the abnormal network patterns seen in patients with a relative short term of the illness, as the abnormalities may disappear in MDDL .

Changes in Brain Functional Network Connectivity in Adult Moyamoya Diseases

Moyamoya disease (MMD) is a cerebrovascular disease that is characterized by progressive stenosis or occlusion of the internal carotid arteries and its main branches, which leads to the formation of abnormal small collateral vessels. However, little is known about how these special vascular structures affect cortical network connectivity and brain function. By applying EEG analysis and graphic network analyses undergoing EEG recording of subjects with eyes-closed (EC) and eyes-open (EO) resting states, and working memory (WM) tasks, we examined the brain network features of hemorrhagic (H_MMD) and ischemic MMD (I_MMD) brains. For the first time, we observed that I_MMD had the much lower alpha-blocking rate during EO state than healthy controls while H_MMD exhibited the relatively low EEG activity rate across all the behavior states. Further, I_MMD showed strong network connections in the alpha-wave band in frontal and parietal regions during EO and WM states. EEG frequency and network topological maps during both resting and WM states indicated that the left frontal lobe and left parietal lobe in H_MMD patients and the right parietal lobe and temporal lobe in I_MMD patients have clear differences compared with controls, which provides a new insight to understand distinct electrophysiological features of MMD. However, due to the small sample size of recruited patient subjects, the result conclusion may be limited.

Supplementary information

The online version contains supplementary material available at (10.1007/s11571-021-09666-1).

Neuronal avalanches in Watts-Strogatz networks of stochastic spiking neurons

Networks of stochastic leaky integrate-and-fire neurons, both at the mean-field level and in square lattices, present a continuous absorbing phase transition with power-law neuronal avalanches at the critical point. Here we complement these results showing that small-world Watts-Strogatz networks have mean-field critical exponents for any rewiring probability p>0. For the ring (p=0), the exponents are the same from the dimension d=1 of the directed-percolation class. In the model, firings are stochastic and occur in discrete time steps, based on a sigmoidal firing probability function. Each neuron has a membrane potential that integrates the signals received from its neighbors. The membrane potentials are subject to a leakage parameter. We study topologies with a varied number of neuron connections and different values of the leakage parameter. Results indicate that the dynamic range is larger for p=0. We also study a homeostatic synaptic depression mechanism to self-organize the network towards the critical region. These stochastic oscillations are characteristic of the so-called self-organized quasicriticality.

Disruption of human brain connectivity networks in patients with cervical spondylotic myelopathy

Background

Brain functional plasticity and reorganization in patients with cervical spondylotic myelopathy (CSM) is increasingly being explored and validated. However, specific topological alterations in functional networks and their role in CSM brain functional reorganization remain unclear. This study investigates the topological architecture of intrinsic brain functional networks in CSM patients using graph theory.

Methods

Functional MRI was conducted on 67 CSM patients and 60 healthy controls (HCs). The topological organization of the whole-brain functional network was then calculated using theoretical graph analysis. The difference in categorical variables between groups was compared using a chi-squared test, while that between continuous variables was evaluated using a two-sample t-test. Nonparametric permutation tests were used to compare network measures between the two groups.

Results

Small-world architecture in functional brain networks were identified in both CSM patients and HCs. Compared with HCs, CSM patients showed a decreased area under the curve (AUC) of the characteristic path length (FDR q=0.040), clustering coefficient (FDR q=0.037), and normalized characteristic path length (FDR q=0.038) of the network. In contrast, there was an increased AUC of normalized clustering coefficient (FDR q=0.014), small-worldness (FDR q=0.009), and global network efficiency (FDR q=0.027) of the network. In local brain regions, nodal topological properties revealed group differences which were predominantly in the default-mode network (DMN), left postcentral gyrus, bilateral putamen, lingual gyrus, and posterior cingulate gyrus.

Conclusions

This study reported altered functional topological organization in CSM patients. Decreased nodal centralities in the visual cortex and sensory-motor regions may indicate sensory-motor dysfunction and blurred vision. Furthermore, increased nodal centralities in the cerebellum may be compensatory for sensory-motor dysfunction in CSM, while the increased DMN may indicate increased psychological processing in CSM patients.

Organization of areal connectivity in the monkey frontoparietal network

Activity observed in biological neural networks is determined by anatomical connectivity between cortical areas. The monkey frontoparietal network facilitates cognitive functions, but the organization of its connectivity is unknown. Here, a new connectivity matrix is proposed which shows that the network utilizes a small-world architecture and the 3-node M9 motif. Its areas exhibit relatively homogeneous connectivity with no suggestion of the hubs seen in scale-free networks. Crucially, its M9 dynamical relay motif is optimally arranged for near-zero and non-zero phase synchrony to arise in support of cognition, serving as a candidate topological mechanism for previously reported findings. These results can serve as a benchmark to be used in the treatment of neurological disorders where the types of cognition the frontoparietal network supports are impaired.

Metabolic Brain Network Analysis With 18F-FDG PET in a Rat Model of Neuropathic Pain

Neuropathic pain has been found to be related to profound reorganization in the function and structure of the brain. We previously demonstrated changes in local brain activity and functional/metabolic connectivity among selected brain regions by using neuroimaging methods. The present study further investigated large-scale metabolic brain network changes in 32 Sprague–Dawley rats with right brachial plexus avulsion injury (BPAI). Graph theory was applied in the analysis of 2-deoxy-2-[18F] fluoro-D-glucose (¹⁸F-FDG) PET images. Inter-subject metabolic networks were constructed by calculating correlation coefficients. Global and nodal network properties were calculated and comparisons between pre- and post-BPAI (7 days) status were conducted. The global network properties (including global efficiency, local efficiency and small-world index) and nodal betweenness centrality did not significantly change for all selected sparsity thresholds following BPAI (p > 0.05). As for nodal network properties, both nodal degree and nodal efficiency measures significantly increased in the left caudate putamen, left medial prefrontal cortex, and right caudate putamen (p < 0.001). The right entorhinal cortex showed a different nodal degree (p < 0.05) but not nodal efficiency. These four regions were selected for seed-based metabolic connectivity analysis. Strengthened connectivity was found among these seeds and distributed brain regions including sensorimotor area, cognitive area, and limbic system, etc. (p < 0.05). Our results indicated that the brain had the resilience to compensate for BPAI-induced neuropathic pain. However, the importance of bilateral caudate putamen, left medial prefrontal cortex, and right entorhinal cortex in the network was strengthened, as well as most of their connections with distributed brain regions.

Structural Brain Network Correlated with Reading Impairment in Alzheimer's Disease

AIM

Alzheimer's disease (AD) is the most common age-related neurodegenerative disease and leads to dementia. AD is characterized by progressive declines in memory and, as the disease progresses, language dysfunction. Although it has been reported that AD patients show progressive aphasia, no study has examined the relationship between language functions estimated by the Standard Language Test for Aphasia (SLTA) and brain network connectivity in Japanese AD patients. If present, such a relationship would be of particular interest because Japanese speakers are accustomed to mingling ideography and phonography.

METHODS

22 Japanese patients with AD who underwent 1.5-tesla MRI scan and SLTA, the scale for speech and reading impairment, participated in this study. We computed brain network connectivity metrics such as degree, betweenness centrality, and clustering coefficient, and estimated their relationships with the subscores of SLTA.

RESULTS

There was a significant negative correlation between the score for "reading aloud Kanji words" and the clustering coefficient in the left inferior temporal region, bilateral hippocampal regions, and right parietotemporal region. We also found a significant negative correlation between the score for "auditory comprehension of words" and the clustering coefficient in the left prefrontal region. No significant relationship was found between the other SLTA scores and the network metrics.

CONCLUSIONS

Our data suggest relationships between reading impairments and regional brain network connectivity in Japanese patients with AD. The brain connectome may provide adjunct biological information that could improve our understanding of reading impairment.