Graph theory approaches to functional network organization in brain disorders: A critique for a brave new small-world

Systematic evaluation of fMRI data-processing pipelines for consistent functional connectomics

Functional interactions between brain regions can be viewed as a network, enabling neuroscientists to investigate brain function through network science. Here, we systematically evaluate 768 data-processing pipelines for network reconstruction from resting-state functional MRI, evaluating the effect of brain parcellation, connectivity definition, and global signal regression. Our criteria seek pipelines that minimise motion confounds and spurious test-retest discrepancies of network topology, while being sensitive to both inter-subject differences and experimental effects of interest. We reveal vast and systematic variability across pipelines' suitability for functional connectomics. Inappropriate choice of data-processing pipeline can produce results that are not only misleading, but systematically so, with the majority of pipelines failing at least one criterion. However, a set of optimal pipelines consistently satisfy all criteria across different datasets, spanning minutes, weeks, and months. We provide a full breakdown of each pipeline's performance across criteria and datasets, to inform future best practices in functional connectomics.

Dynamic temporal neural patterns based on multichannel LFPs Identify different brain states during anesthesia in pigeons: comparison of three anesthetics

Anesthetic-induced brain activity study is crucial in avian cognitive-, consciousness-, and sleep-related research. However, the neurobiological mechanisms underlying the generation of brain rhythms and specific connectivity of birds during anesthesia are poorly understood. Although different kinds of anesthetics can be used to induce an anesthesia state, a comparison study of these drugs focusing on the neural pattern evolution during anesthesia is lacking. Here, we recorded local field potentials (LFPs) using a multi-channel micro-electrode array inserted into the nidopallium caudolateral (NCL) of adult pigeons (Columba livia) anesthetized with chloral hydrate, pelltobarbitalum natricum or urethane. Power spectral density (PSD) and functional connectivity analyses were used to measure the dynamic temporal neural patterns in NCL during anesthesia. Neural decoding analysis was adopted to calculate the probability of the pigeon's brain state and the kind of injected anesthetic. In the NCL during anesthesia, we found elevated power activity and functional connectivity at low-frequency bands and depressed power activity and connectivity at high-frequency bands. Decoding results based on the spectral and functional connectivity features indicated that the pigeon's brain states during anesthesia and the injected anesthetics can be effectively decoded. These findings provide an important foundation for future investigations on how different anesthetics induce the generation of specific neural patterns.

Longitudinal Functional Connectome in Pediatric Concussion: An Advancing Concussion Assessment in Pediatrics Study

Advanced magnetic resonance imaging (MRI) techniques indicate that concussion (i.e., mild traumatic brain injury) disrupts brain structure and function in children. However, the functional connectivity of brain regions within global and local networks (i.e., functional connectome) is poorly understood in pediatric concussion. This prospective, longitudinal study addressed this gap using data from the largest neuroimaging study of pediatric concussion to date to study the functional connectome longitudinally after concussion as compared with mild orthopedic injury (OI). Children and adolescents (n = 967) 8-16.99 years with concussion or mild OI were recruited from pediatric emergency departments within 48 h post-injury. Pre-injury and 1-month post-injury symptom ratings were used to classify concussion with or without persistent symptoms based on reliable change. Subjects completed a post-acute (2-33 days) and chronic (3 or 6 months via random assignment) MRI scan. Graph theory metrics were derived from 918 resting-state functional MRI scans in 585 children (386 concussion/199 OI). Linear mixed-effects modeling was performed to assess group differences over time, correcting for multiple comparisons. Relative to OI, the global clustering coefficient was reduced at 3 months post-injury in older children with concussion and in females with concussion and persistent symptoms. Time post-injury and sex moderated group differences in local (regional) network metrics of several brain regions, including degree centrality, efficiency, and clustering coefficient of the angular gyrus, calcarine fissure, cuneus, and inferior occipital, lingual, middle occipital, post-central, and superior occipital gyrus. Relative to OI, degree centrality and nodal efficiency were reduced post-acutely, and nodal efficiency and clustering coefficient were reduced chronically after concussion (i.e., at 3 and 6 months post-injury in females; at 6 months post-injury in males). Functional network alterations were more robust and widespread chronically as opposed to post-acutely after concussion, and varied by sex, age, and symptom recovery at 1-month post-injury. Local network segregation reductions emerged globally (across the whole brain network) in older children and in females with poor recovery chronically after concussion. Reduced functioning between neighboring regions could negatively disrupt specialized information processing. Local network metric alterations were demonstrated in several posterior regions that are involved in vision and attention after concussion relative to OI. This indicates that functioning of superior parietal and occipital regions could be particularly susceptibile to the effects of concussion. Moreover, those regional alterations were especially apparent at later time periods post-injury, emerging after post-concussive symptoms resolved in most and persisted up to 6 months post-injury, and differed by biological sex. This indicates that neurobiological changes continue to occur up to 6 months after pediatric concussion, although changes emerge earlier in females than in males. Changes could reflect neural compensation mechanisms.

Influence of Transcutaneous Vagus Nerve Stimulation on Motor Planning: A Resting-State and Task-State EEG Study

Transcutaneous vagus nerve stimulation (tVNS) shows a potential regulatory role for motor planning. Still, existing research mainly focuses on behavioral studies, and the neural modulation mechanism needs to be clarified. Therefore, we designed a multi-condition (active or sham, pre or under, difficult or easy, left-hand or right-hand) motor planning experiment to explore the effect of online tVNS (i.e., tVNS and tasks synchronized). Twenty-eight subjects were recruited and randomly assigned to active and sham groups. Both groups performed the same tasks in the experiment and separately collected task-state EEG and 5-min eye-open resting-state EEG. The results showed that the changes in event-related potential (ERP) and movement-related cortical potential (MRCP) amplitudes were more significant for the left-hand difficult task (LD) under active-tVNS. According to the power spectrum results, active-tVNS significantly modulated the activities of the contralateral motor cortex at beta and gamma bands in the resting state. The functional connectivity based on partial directed coherence (PDC) showed significant changes in the parietal lobe after active-tVNS. These findings suggest that tVNS is a promising way to improve motor planning ability.

Optimal approaches to analyzing functional MRI data in glioma patients

BACKGROUND

Resting-state fMRI is increasingly used to study the effects of gliomas on the functional organization of the brain. A variety of preprocessing techniques and functional connectivity analyses are represented in the literature. However, there so far has been no systematic comparison of how alternative methods impact observed results.

NEW METHOD

We first surveyed current literature and identified alternative analytical approaches commonly used in the field. Following, we systematically compared alternative approaches to atlas registration, parcellation scheme, and choice of graph-theoretical measure as regards differentiating glioma patients (N = 59) from age-matched reference subjects (N = 163).

RESULTS

Our results suggest that non-linear, as opposed to affine registration, improves structural match to an atlas, as well as measures of functional connectivity. Functionally- as opposed to anatomically-derived parcellation schemes maximized the contrast between glioma patients and reference subjects. We also demonstrate that graph-theoretic measures strongly depend on parcellation granularity, parcellation scheme, and graph density.

COMPARISON WITH EXISTING METHODS AND CONCLUSIONS

Our current work primarily focuses on technical optimization of rs-fMRI analysis in glioma patients and, therefore, is fundamentally different from the bulk of papers discussing glioma-induced functional network changes. We report that the evaluation of glioma-induced alterations in the functional connectome strongly depends on analytical approaches including atlas registration, choice of parcellation scheme, and graph-theoretical measures.

Analysis of Resting-State Functional Magnetic Resonance Imaging in Alzheimer's Disease

Alzheimer's disease (AD) has been characterized by widespread network disconnection among brain regions, widely overlapping with the hallmarks of the disease. Functional connectivity has been studied with an upward trend in the last two decades, predominantly in AD among other neuropsychiatric disorders, and presents a potential biomarker with various features that might provide unique contributions to foster our understanding of neural mechanisms of AD. The resting-state functional MRI (rs-fMRI) is usually used to measure the blood-oxygen-level-dependent signals that reflect the brain's functional connectivity. Nevertheless, the rs-fMRI is still underutilized, which might be due to the fairly complex acquisition and analytic methodology. In this chapter, we presented the common methods that have been applied in rs-fMRI literature, focusing on the studies on individuals in the continuum of AD. The key methodological aspects will be addressed that comprise acquiring, processing, and interpreting rs-fMRI data. More, we discussed the current and potential implications of rs-fMRI in AD.

Altered cerebellar volumes and intrinsic cerebellar networks in patients with transient global amnesia

This study aimed to investigate the differences in cerebellar volumes and intrinsic cerebellar networks between patients with transient global amnesia (TGA) and healthy controls. We retrospectively enrolled patients with TGA and age- and sex-matched healthy controls. We used three-dimensional T1-weighted imaging at the time of TGA diagnosis to obtain cerebellar volumes, and the intrinsic cerebellar network was calculated by applying graph theory based on cerebellar volumes. The nodes were defined as individual cerebellar volumes, and edges as partial correlations, controlling for the effects of age and sex. The cerebellar volumes and intrinsic cerebellar networks were compared between the two groups. We enrolled 44 patients with TGA and 47 healthy controls. The volume of the left cerebellar white matter in patients with TGA was significantly lower than that in healthy controls (1.0328 vs. 1.0753%, p = 0.0094). In addition, there were significant differences in intrinsic cerebellar networks between the two groups. The small-worldness index in patients with TGA was higher than that in the healthy controls (0.951 vs. 0.880, p = 0.038). In the correlation analysis, the volumes of the right cerebellar cortex and lobules VIIIB were significantly correlated with age in patients with TGA (r = -0.323, p = 0.033; r = -0.313, p = 0.038, respectively). Patients with TGA exhibit alterations in cerebellar volumes and intrinsic cerebellar networks compared with healthy controls. These findings may contribute to a better understanding of the pathophysiology of the TGA.

CAMBA framework: Unveiling the brain asymmetry alterations and longitudinal changes after stroke using resting-state EEG

Hemispheric asymmetry or lateralization is a fundamental principle of brain organization. However, it is poorly understood to what extent the brain asymmetries across different levels of functional organizations are evident in health or altered in brain diseases. Here, we propose a framework that integrates three degrees of brain interactions (isolated nodes, node-node, and edge-edge) into a unified analysis pipeline to capture the sliding window-based asymmetry dynamics at both the node and hemisphere levels. We apply this framework to resting-state EEG in healthy and stroke populations and investigate the stroke-induced abnormal alterations in brain asymmetries and longitudinal asymmetry changes during poststroke rehabilitation. We observe that the mean asymmetry in patients was abnormally enhanced across different frequency bands and levels of brain interactions, with these abnormal patterns strongly associated with the side of the stroke lesion. Compared to healthy controls, patients displayed significant alterations in asymmetry fluctuations, disrupting and reconfiguring the balance of inter-hemispheric integration and segregation. Additionally, analyses reveal that specific abnormal asymmetry metrics in patients tend to move towards those observed in healthy controls after short-term brain-computer interface rehabilitation. Furthermore, preliminary evidence suggests that baseline clinical and asymmetry features can predict poststroke improvements in the Fugl-Meyer assessment of the lower extremity (mean absolute error of about 2). Overall, these findings advance our understanding of hemispheric asymmetry. Our framework offers new insights into the mechanisms underlying brain alterations and recovery after a brain lesion, may help identify prognostic biomarkers, and can be easily extended to different functional modalities.

Structural brain network changes in patients with neurofibromatosis type 1: A retrospective study

We investigated the changes in structural connectivity (using diffusion tensor imaging [DTI]) and the structural covariance network based on structural volume using graph theory in patients with neurofibromatosis type 1 (NF1) compared to a healthy control group. We included 14 patients with NF1, according to international consensus recommendations, and 16 healthy individuals formed the control group. This was retrospectively observational study followed STROBE guideline. Both groups underwent brain magnetic resonance imaging including DTI and 3-dimensional T1-weighted imaging. We analyzed structural connectivity using DTI and Diffusion Spectrum Imaging Studio software and evaluated the structural covariance network based on the structural volumes using FreeSurfer and Brain Analysis Using Graph Theory software. There were no differences in the global structural connectivity between the 2 groups, but several brain regions showed significant differences in local structural connectivity. Additionally, there were differences between the global structural covariance networks. The characteristic path length was longer and the small-worldness index was lower in patients with NF1. Furthermore, several regions showed significant differences in the local structural covariance networks. We observed changes in structural connectivity and covariance networks in patients with NF1 compared to a healthy control group. We found that global structural efficiency is decreased in the brains of patients with NF1, and widespread changes in the local structural network were found. These results suggest that NF1 is a brain network disease, and our study provides direction for further research to elucidate the biological processes of NF1.

UNRAVELing the synergistic effects of psilocybin and environment on brain-wide immediate early gene expression in mice

The effects of context on the subjective experience of serotonergic psychedelics have not been fully examined in human neuroimaging studies, partly due to limitations of the imaging environment. Here, we administered saline or psilocybin to mice in their home cage or an enriched environment, immunofluorescently-labeled brain-wide c-Fos, and imaged iDISCO+ cleared tissue with light sheet fluorescence microscopy (LSFM) to examine the impact of environmental context on psilocybin-elicited neural activity at cellular resolution. Voxel-wise analysis of c-Fos-immunofluorescence revealed clusters of neural activity associated with main effects of context and psilocybin-treatment, which were validated with c-Fos+ cell density measurements. Psilocybin increased c-Fos expression in subregions of the neocortex, caudoputamen, central amygdala, and parasubthalamic nucleus while it decreased c-Fos in the hypothalamus, cortical amygdala, striatum, and pallidum in a predominantly context-independent manner. To gauge feasibility of future mechanistic studies on ensembles activated by psilocybin, we confirmed activity- and Cre-dependent genetic labeling in a subset of these neurons using TRAP2+/-;Ai14+ mice. Network analyses treating each psilocybin-sensitive cluster as a node indicated that psilocybin disrupted co-activity between highly correlated regions, reduced brain modularity, and dramatically attenuated intermodular co-activity. Overall, our results indicate that main effects of context and psilocybin were robust, widespread, and reorganized network architecture, whereas context×psilocybin interactions were surprisingly sparse.

Quantitative EEG and brain network analysis: predicting awakening from early coma after cardiopulmonary resuscitation

OBJECTIVE

For patients in early coma after cardiopulmonary resuscitation (CPR), quantitative electroencephalogram (EEG) and brain network analysis was performed to identify relevant indicators of awakening.

METHODS

A prospective cohort study was conducted on comatose patients after CPR in the neuro-critical care unit. The included patients received clinical evaluation. The bedside high-density (64-lead) EEG monitoring was performed for visual grading and calculation of power spectrum and brain network parameters. A 3-month prognostic assessment was performed and the patients were dichotomized into the awakening group and the unawakening group.

RESULTS

A total of 25 patients were included. The awakening group had higher GCS score, more slow wave pattern and reactive EEG than the unawakening group (P = 0.003, P < 0.001, P < 0.001, respectively). Compared with the unawakening group, (1) the awakening group had significantly higher absolute and relative θ power and slow/fast band ratio of the whole brain (P < 0.05), (2) the awakening group had stronger connection based on coherence, phase synchronization, phase lag index and cross-correlation (P < 0.05), (3) the awakening group had higher small-worldness, clustering coefficient and average path length based on graph theory (P < 0.05).

CONCLUSIONS

The power spectrum and brain network characteristics in patients in early coma after CPR have predictive value for recovery.

Optimizing network neuroscience computation of individual differences in human spontaneous brain activity for test-retest reliability

A rapidly emerging application of network neuroscience in neuroimaging studies has provided useful tools to understand individual differences in intrinsic brain function by mapping spontaneous brain activity, namely intrinsic functional network neuroscience (ifNN). However, the variability of methodologies applied across the ifNN studies-with respect to node definition, edge construction, and graph measurements-makes it difficult to directly compare findings and also challenging for end users to select the optimal strategies for mapping individual differences in brain networks. Here, we aim to provide a benchmark for best ifNN practices by systematically comparing the measurement reliability of individual differences under different ifNN analytical strategies using the test-retest design of the Human Connectome Project. The results uncovered four essential principles to guide ifNN studies: (1) use a whole brain parcellation to define network nodes, including subcortical and cerebellar regions; (2) construct functional networks using spontaneous brain activity in multiple slow bands; and (3) optimize topological economy of networks at individual level; and (4) characterize information flow with specific metrics of integration and segregation. We built an interactive online resource of reliability assessments for future ifNN (https://ibraindata.com/research/ifNN).

Ex-utero third trimester developmental changes in functional brain network organization in infants born very and extremely preterm

Introduction

The latter half of gestation is a period of rapid brain development, including the formation of fundamental functional brain network architecture. Unlike in-utero fetuses, infants born very and extremely preterm undergo these critical maturational changes in the extrauterine environment, with growing evidence suggesting this may result in altered brain networks. To date, however, the development of functional brain architecture has been unexplored.

Methods

From a prospective cohort of preterm infants, graph parameters were calculated for fMRI scans acquired prior to reaching term equivalent age. Eight graph properties were calculated, Clustering Coefficient (C), Characteristic Path Length (L), Modularity (Q), Local Efficiency (LE), Global Efficiency (GE), Normalized Clustering (λ), Normalized Path Length (γ), and Small-Worldness (σ). Properties were first compared to values generated from random and lattice networks and cost efficiency was evaluated. Subsequently, linear mixed effect models were used to assess relationship with postmenstrual age and infant sex.

Results

A total of 111 fMRI scans were acquired from 85 preterm infants born at a mean GA 28.93 ± 2.8. Infants displayed robust small world properties as well as both locally and globally efficient networks. Regression models found that GE increased while L, Q, λ, γ, and σ decreased with increasing postmenstrual age following multiple comparison correction (r2Adj range 0.143-0.401, p < 0048), with C and LE exhibited trending increases with age.

Discussion

This is the first direct investigation on the extra-uterine formation of functional brain architecture in preterm infants. Importantly, our results suggest that changes in functional architecture with increasing age exhibit a different trajectory relative to in utero fetus. Instead, they exhibit developmental changes more similar to the early postnatal period in term born infants.

Brain-wide neuron quantification toolkit reveals strong sexual dimorphism in the evolution of fear memory

Fear responses are functionally adaptive behaviors that are strengthened as memories. Indeed, detailed knowledge of the neural circuitry modulating fear memory could be the turning point for the comprehension of this emotion and its pathological states. A comprehensive understanding of the circuits mediating memory encoding, consolidation, and retrieval presents the fundamental technological challenge of analyzing activity in the entire brain with single-neuron resolution. In this context, we develop the brain-wide neuron quantification toolkit (BRANT) for mapping whole-brain neuronal activation at micron-scale resolution, combining tissue clearing, high-resolution light-sheet microscopy, and automated image analysis. The robustness and scalability of this method allow us to quantify the evolution of activity patterns across multiple phases of memory in mice. This approach highlights a strong sexual dimorphism in recruited circuits, which has no counterpart in the behavior. The methodology presented here paves the way for a comprehensive characterization of the evolution of fear memory.

Brain disease research based on functional magnetic resonance imaging data and machine learning: a review

Brain diseases, including neurodegenerative diseases and neuropsychiatric diseases, have long plagued the lives of the affected populations and caused a huge burden on public health. Functional magnetic resonance imaging (fMRI) is an excellent neuroimaging technology for measuring brain activity, which provides new insight for clinicians to help diagnose brain diseases. In recent years, machine learning methods have displayed superior performance in diagnosing brain diseases compared to conventional methods, attracting great attention from researchers. This paper reviews the representative research of machine learning methods in brain disease diagnosis based on fMRI data in the recent three years, focusing on the most frequent four active brain disease studies, including Alzheimer's disease/mild cognitive impairment, autism spectrum disorders, schizophrenia, and Parkinson's disease. We summarize these 55 articles from multiple perspectives, including the effect of the size of subjects, extracted features, feature selection methods, classification models, validation methods, and corresponding accuracies. Finally, we analyze these articles and introduce future research directions to provide neuroimaging scientists and researchers in the interdisciplinary fields of computing and medicine with new ideas for AI-aided brain disease diagnosis.

Cerebellar Volume Reduction in Patients with Isolated REM Sleep Behavior Disorder: Evidence of a Potential Role of the Cerebellum

INTRODUCTION

In this study, we aimed to investigate changes in the total cerebellar volume, subdivisions of the cerebellar volume, and intrinsic cerebellar network in patients with isolated rapid eye movement (REM) sleep behavior disorder (iRBD) compared to healthy controls.

METHODS

We enrolled patients with newly diagnosed iRBD and healthy controls who had no structural lesions according to their brain MRI. All participants underwent three-dimensional T1-weighted imaging. We obtained the total cerebellar volume and subdivisions of the cerebellar volume using the ACAPULCO program and calculated the intrinsic cerebellar network using a BRAPH program based on the subdivisions of the cerebellar volume by applying a graph theory. We compared the cerebellar volumes and intrinsic cerebellar network between the patients with iRBD and healthy controls.

RESULTS

In total, we enrolled 43 patients with iRBD and 47 healthy controls. Total cerebellar volume in patients with iRBD was lower than that in the healthy controls (8.4637 vs. 9.0863%, p = 0.0001). There were significant differences in the subdivisions of cerebellar volume between the groups. The volumes of the right and left lobule VIIB in the patients with iRBD were lower than those in the healthy controls (right, 0.3495 vs. 0.4025%, p = 0.0009; left, 0.3561 vs. 0.4293%, p < 0.0001). However, the other cerebellar volumes, such as the corpus meullare and vermis, were not different between the groups. The intrinsic cerebellar network was not different between the patients with iRBD and healthy controls.

CONCLUSION

We found decreased total cerebellar volumes and subdivisions of the cerebellar volume, particularly in the right and left lobule VIIB, in patients with iRBD compared to healthy controls. The present results suggest that the cerebellum may play a potential role in the pathogenesis of iRBD.

Resting State Network Segregation Modulates Age-Related Differences in Language Production

Older adults typically exhibit decline in language production. However, how the brain supports or fails to support these processes is unclear. Moreover, there are competing hypotheses about the nature of age-related neural changes and whether age-related increases in neural activity reflect compensation or a decline in neural efficiency. In the current study, we investigated the neural bases of language production focusing on resting state functional connectivity. We hypothesized that language production performance, functional connectivity, and their relationship would differ as a function of age. Consistent with prior work, older age was associated with worse language production performance. Functional connectivity analyses showed that network segregation within the left hemisphere language network was maintained across adulthood. However, increased age was associated with lower whole brain network segregation. Moreover, network segregation was related to language production ability. In both network analyses, there were significant interactions with age-higher network segregation was associated with better language production abilities for younger and middle-aged adults, but not for older adults. Interestingly, there was a stronger relationship between language production and the whole brain network segregation than between production and the language network. These results highlight the utility of network segregation measures as an index of brain function, with higher network segregation associated with better language production ability. Moreover, these results are consistent with stability in the left hemisphere language network across adulthood and suggest that dedifferentiation among brain networks, outside of the language network, is a hallmark of aging and may contribute to age-related language production difficulties.

Task difficulty modulates age-related differences in functional connectivity during word production

Older adults typically report increased difficulty with language production, while its neural bases are less clear. The current study investigated the neural bases of age-related differences in language production at the word level and the modulating effect of task difficulty, focusing on task-based functional connectivity. Using an English phonological Go/No-Go picture naming task, task difficulty was manipulated by varying the proportion of naming trials (Go trials) and inhibition trials (No-Go trials) across runs. Behaviorally, compared to younger adults, older adults performed worse, and showed larger effects of task difficulty. Neurally, older adults had lower within language network connectivity compared to younger adults. Moreover, older adults' language network became less segregated as task difficulty increased. These results are consistent with the Compensation-Related Utilization of Neural Circuits Hypothesis, suggesting that the brain becomes less specified and efficient with increased task difficulty, and that these effects are stronger among older adults (i.e., more dedifferentiated).

Development of neonatal brain functional centrality and alterations associated with preterm birth

Formation of the functional connectome in early life underpins future learning and behavior. However, our understanding of how the functional organization of brain regions into interconnected hubs (centrality) matures in the early postnatal period is limited, especially in response to factors associated with adverse neurodevelopmental outcomes such as preterm birth. We characterized voxel-wise functional centrality (weighted degree) in 366 neonates from the Developing Human Connectome Project. We tested the hypothesis that functional centrality matures with age at scan in term-born babies and is disrupted by preterm birth. Finally, we asked whether neonatal functional centrality predicts general neurodevelopmental outcomes at 18 months. We report an age-related increase in functional centrality predominantly within visual regions and a decrease within the motor and auditory regions in term-born infants. Preterm-born infants scanned at term equivalent age had higher functional centrality predominantly within visual regions and lower measures in motor regions. Functional centrality was not related to outcome at 18 months old. Thus, preterm birth appears to affect functional centrality in regions undergoing substantial development during the perinatal period. Our work raises the question of whether these alterations are adaptive or disruptive and whether they predict neurodevelopmental characteristics that are more subtle or emerge later in life.

Altered Cerebellar Volumes and Intrinsic Cerebellar Network in Juvenile Myoclonic Epilepsy

Objectives. This study is aimed at investigating the alterations in cerebellar volumes and intrinsic cerebellar network in patients with juvenile myoclonic epilepsy (JME) in comparison with healthy controls. Methods. Patients newly diagnosed with JME and healthy controls were enrolled. Three-dimensional T1-weighted imaging was conducted, and no structural lesions were found on brain magnetic resonance imaging. Cerebellar volumes were obtained using the ACAPULCO program, while the intrinsic cerebellar network was evaluated by applying graph theory using the BRAPH program. The nodes were defined as individual cerebellar volumes and edges as partial correlations, controlling for the effects of age and sex. Cerebellar volumes and intrinsic cerebellar networks were compared between the two groups. Results. Forty-five patients with JME and 45 healthy controls were enrolled. Compared with the healthy controls, the patients with JME had significantly lower volumes of the right and left cerebellar white matter (3.33 vs. 3.48%, $p=0.009$ ; 3.35 vs. 3.49%, $p=0.009$ ), corpus medullare (0.99 vs. 1.03%, $p=0.04$ ), and left lobule V (0.19 vs. 0.22%, $p=0.002$ ). The intrinsic cerebellar networks also showed significant differences between the two groups. The small-worldness index in the patients with JME was significantly lower than that in the healthy controls (0.771 vs. 0.919, $p=0.04$ ). Conclusion. The cerebellar volumes and intrinsic cerebellar network demonstrated alterations in the patients with JME when compared with those of the healthy controls. Our study results provide evidence that the cerebellum may play a role in the pathogenesis of JME.

Altered white matter functional network in nicotine addiction

Nicotine addiction is a neuropsychiatric disorder with dysfunction in cortices as well as white matter (WM). The nature of the functional alterations in WM remains unclear. The small-world model can well characterize the structure and function of the human brain. In this study, we utilized the small-world model to compare the WM functional connectivity between 62 nicotine addiction participants (called the discovery sample) and 66 matched healthy controls (called the control sample). We also recruited an independent sample comprising 32 nicotine addicts (called the validation sample) for clinical application. The WM functional network data at the network level showed that the nicotine addiction group revealed decreased small-worldness index (σ) and normalized clustering coefficient (γ) compared with healthy controls. For clinical application, the small-world topology of WM functional connectivity could distinguish nicotine addicts from healthy controls (classification accuracy=0.59323, p = 0.0464). We trained abnormal small-world properties on the discovery sample to identify the severity of nicotine addiction, and the identification was successfully applied to the validation sample (classification accuracy=0.65625, p = 0.0106). Our neuroimaging findings provide direct evidence for WM functional changes in nicotine addiction and suggest that the small-world properties of WM function could be qualified as potential biomarkers in nicotine addiction.

UNRAVELing the synergistic effects of psilocybin and environment on brain-wide immediate early gene expression in mice

The effects of context on the subjective experience of serotonergic psychedelics have not been fully examined in human neuroimaging studies, partly due to limitations of the imaging environment. Here, we administered saline or psilocybin to mice in their home cage or an enriched environment, immunofluorescently-labeled brain-wide c-Fos, and imaged cleared tissue with light sheet microscopy to examine the impact of context on psilocybin-elicited neural activity at cellular resolution. Voxel-wise analysis of c-Fos-immunofluorescence revealed differential neural activity, which we validated with c-Fos + cell density measurements. Psilocybin increased c-Fos expression in the neocortex, caudoputamen, central amygdala, and parasubthalamic nucleus and decreased c-Fos in the hypothalamus, cortical amygdala, striatum, and pallidum. Main effects of context and psilocybin-treatment were robust, widespread, and spatially distinct, whereas interactions were surprisingly sparse.

A fast and intuitive method for calculating dynamic network reconfiguration and node flexibility

Dynamic interactions between brain regions, either during rest or performance of cognitive tasks, have been studied extensively using a wide variance of methods. Although some of these methods allow elegant mathematical interpretations of the data, they can easily become computationally expensive or difficult to interpret and compare between subjects or groups. Here, we propose an intuitive and computationally efficient method to measure dynamic reconfiguration of brain regions, also termed flexibility. Our flexibility measure is defined in relation to an a-priori set of biologically plausible brain modules (or networks) and does not rely on a stochastic data-driven module estimation, which, in turn, minimizes computational burden. The change of affiliation of brain regions over time with respect to these a-priori template modules is used as an indicator of brain network flexibility. We demonstrate that our proposed method yields highly similar patterns of whole-brain network reconfiguration (i.e., flexibility) during a working memory task as compared to a previous study that uses a data-driven, but computationally more expensive method. This result illustrates that the use of a fixed modular framework allows for valid, yet more efficient estimation of whole-brain flexibility, while the method additionally supports more fine-grained (e.g. node and group of nodes scale) flexibility analyses restricted to biologically plausible brain networks.

Network analysis reveals abnormal functional brain circuitry in anxious dogs

Anxiety is a common disease within human psychiatric disorders and has also been described as a frequently neuropsychiatric problem in dogs. Human neuroimaging studies showed abnormal functional brain networks might be involved in anxiety. In this study, we expected similar changes in network topology are also present in dogs. We performed resting-state functional MRI on 25 healthy dogs and 13 patients. The generic Canine Behavioral Assessment & Research Questionnaire was used to evaluate anxiety symptoms. We constructed functional brain networks and used graph theory to compare the differences between two groups. No significant differences in global network topology were found. However, focusing on the anxiety circuit, global efficiency and local efficiency were significantly higher, and characteristic path length was significantly lower in the amygdala in patients. We detected higher connectivity between amygdala-hippocampus, amygdala-mesencephalon, amygdala-thalamus, frontal lobe-hippocampus, frontal lobe-thalamus, and hippocampus-thalamus, all part of the anxiety circuit. Moreover, correlations between network metrics and anxiety symptoms were significant. Altered network measures in the amygdala were correlated with stranger-directed fear and excitability; altered degree in the hippocampus was related to attachment/attention seeking, trainability, and touch sensitivity; abnormal frontal lobe function was related to chasing and familiar dog aggression; attachment/attention seeking was correlated with functional connectivity between amygdala-hippocampus and amygdala-thalamus; familiar dog aggression was related to global network topology change. These findings may shed light on the aberrant topological organization of functional brain networks underlying anxiety in dogs.

Aberrant degree centrality of functional brain networks in subclinical depression and major depressive disorder

BACKGROUND

As one of the most common diseases, major depressive disorder (MDD) has a significant adverse impact on the li of patients. As a mild form of depression, subclinical depression (SD) serves as an indicator of progression to MDD. This study analyzed the degree centrality (DC) for MDD, SD, and healthy control (HC) groups and identified the brain regions with DC alterations.

METHODS

The experimental data were composed of resting-state functional magnetic resonance imaging (rs-fMRI) from 40 HCs, 40 MDD subjects, and 34 SD subjects. After conducting a one-way analysis of variance, two-sample t-tests were used for further analysis to explore the brain regions with changed DC. Receiver operating characteristic (ROC) curve analysis of single index and composite index features was performed to analyze the distinguishable ability of important brain regions.

RESULTS

For the comparison of MDD vs. HC, increased DC was found in the right superior temporal gyrus (STG) and right inferior parietal lobule (IPL) in the MDD group. For SD vs. HC, the SD group showed a higher DC in the right STG and the right middle temporal gyrus (MTG), and a smaller DC in the left IPL. For MDD vs. SD, increased DC in the right middle frontal gyrus (MFG), right IPL, and left IPL, and decreased DC in the right STG and right MTG was found in the MDD group. With an area under the ROC (AUC) of 0.779, the right STG could differentiate MDD patients from HCs and, with an AUC of 0.704, the right MTG could differentiate MDD patients from SD patients. The three composite indexes had good discriminative ability in each pairwise comparison, with AUCs of 0.803, 0.751, and 0.814 for MDD vs. HC, SD vs. HC, and MDD vs. SD, respectively.

CONCLUSION

Altered DC in the STG, MTG, IPL, and MFG were identified in depression groups. The DC values of these altered regions and their combinations presented good discriminative ability between HC, SD, and MDD. These findings could help to find effective biomarkers and reveal the potential mechanisms of depression.

Assessing the Repeatability of Multi-Frequency Multi-Layer Brain Network Topologies Across Alternative Researcher's Choice Paths

There is a growing interest in the neuroscience community on the advantages of multilayer functional brain networks. Researchers usually treated different frequencies separately at distinct functional brain networks. However, there is strong evidence that these networks share complementary information while their interdependencies could reveal novel findings. For this purpose, neuroscientists adopt multilayer networks, which can be described mathematically as an extension of trivial single-layer networks. Multilayer networks have become popular in neuroscience due to their advantage to integrate different sources of information. Here, Ι will focus on the multi-frequency multilayer functional connectivity analysis on resting-state fMRI (rs-fMRI) recordings. However, constructing a multilayer network depends on selecting multiple pre-processing steps that can affect the final network topology. Here, I analyzed the rs-fMRI dataset from a single human performing scanning over a period of 18 months (84 scans in total), and the rs-fMRI dataset containing 25 subjects with 3 repeat scans. I focused on assessing the reproducibility of multi-frequency multilayer topologies exploring the effect of two filtering methods for extracting frequencies from BOLD activity, three connectivity estimators, with or without a topological filtering scheme, and two spatial scales. Finally, I untangled specific combinations of researchers' choices that yield consistently brain networks with repeatable topologies, giving me the chance to recommend best practices over consistent topologies.

Multimodal multilayer network centrality relates to executive functioning

Executive functioning (EF) is a higher order cognitive process that is thought to depend on a network organization facilitating integration across subnetworks, in the context of which the central role of the fronto-parietal network (FPN) has been described across imaging and neurophysiological modalities. However, the potentially complementary unimodal information on the relevance of the FPN for EF has not yet been integrated. We employ a multilayer framework to allow for integration of different modalities into one 'network of networks.' We used diffusion MRI, resting-state functional MRI, MEG, and neuropsychological data obtained from 33 healthy adults to construct modality-specific single-layer networks as well as a single multilayer network per participant. We computed single-layer and multilayer eigenvector centrality of the FPN as a measure of integration in this network and examined their associations with EF. We found that higher multilayer FPN centrality, but not single-layer FPN centrality, was related to better EF. We did not find a statistically significant change in explained variance in EF when using the multilayer approach as compared to the single-layer measures. Overall, our results show the importance of FPN integration for EF and underline the promise of the multilayer framework toward better understanding cognitive functioning.

Unraveling the functional attributes of the language connectome: crucial subnetworks, flexibility and variability

Language processing is a highly integrative function, intertwining linguistic operations (processing the language code intentionally used for communication) and extra-linguistic processes (e.g., attention monitoring, predictive inference, long-term memory). This synergetic cognitive architecture requires a distributed and specialized neural substrate. Brain systems have mainly been examined at rest. However, task-related functional connectivity provides additional and valuable information about how information is processed when various cognitive states are involved. We gathered thirteen language fMRI tasks in a unique database of one hundred and fifty neurotypical adults (InLang [Interactive networks of Language] database), providing the opportunity to assess language features across a wide range of linguistic processes. Using this database, we applied network theory as a computational tool to model the task-related functional connectome of language (LANG atlas). The organization of this data-driven neurocognitive atlas of language was examined at multiple levels, uncovering its major components (or crucial subnetworks), and its anatomical and functional correlates. In addition, we estimated its reconfiguration as a function of linguistic demand (flexibility) or several factors such as age or gender (variability). We observed that several discrete networks could be specifically shaped to promote key functional features of language: coding-decoding (Net1), control-executive (Net2), abstract-knowledge (Net3), and sensorimotor (Net4) functions. The architecture of these systems and the functional connectivity of the pivotal brain regions varied according to the nature of the linguistic process, gender, or age. By accounting for the multifaceted nature of language and modulating factors, this study can contribute to enriching and refining existing neurocognitive models of language. The LANG atlas can also be considered a reference for comparative or clinical studies involving various patients and conditions.

Potential biomarkers for distinguishing primary from acquired premature ejaculation: A diffusion tensor imaging based network study

Introduction

Premature ejaculation (PE) is classified as primary and acquired and may be facilitated by different pathophysiology. Brain plays an important role in PE, however, differences in the central neuropathological mechanisms among subtypes of PE are unknown.

Materials and methods

We acquired diffusion tensor imaging (DTI) data from 44 healthy controls (HC) and 47 PE patients (24 primary PE and 23 acquired PE). Then, the whole-brain white matter (WM) structural networks were constructed and between-group differences of nodal segregative parameters were identified by the method of graph theoretical analysis. Moreover, receiver operating characteristic (ROC) curves were performed to determine the suitability of the altered parameters as potential neuroimaging biomarkers for distinguishing primary PE from acquired PE.

Results

PE patients showed significantly increased clustering coefficient C(i) in the left inferior frontal gyrus (triangular part) (IFGtriang.L) and increased local efficiency E_loc(i) in the left precental gyrus (PreCG.L) and IFGtriang.L when compared with HC. Compared to HC, primary PE patients had increased C(i) and E_loc(i) in IFGtriang.L and the left amygdala (AMYG.L) while acquired PE patients had increased C(i) and E_loc(i) in IFGtriang.L, and decreased C(i) and E_loc(i) in AMYG.L. Compared to acquired PE, primary PE patients had increased C(i) and E_loc(i) in AMYG.L. Moreover, ROC analysis revealed that PreCG.L, IFGtriang.L and AMYG.L might be helpful for distinguishing different subtypes of PE from HC (PE from HC: sensitivity, 61.70–78.72%; specificity, 56.82–77.27%; primary PE from HC: sensitivity, 66.67–87.50%; specificity, 52.27–77.27%; acquired PE from HC: sensitivity, 34.78–86.96%; specificity, 54.55–100%) while AMYG.L might be helpful for distinguishing primary PE from acquired PE (sensitivity, 83.33–91.70%; specificity, 69.57–73.90%).

Conclusion

These findings improved our understanding of the pathophysiological processes that occurred in patients with ejaculatory dysfunction and suggested that the abnormal segregation of left amygdala might serve as a useful marker to help clinicians distinguish patients with primary PE from those with acquired PE.

It's about time: Linking dynamical systems with human neuroimaging to understand the brain

Most human neuroscience research to date has focused on statistical approaches that describe stationary patterns of localized neural activity or blood flow. While these patterns are often interpreted in light of dynamic, information-processing concepts, the static, local, and inferential nature of the statistical approach makes it challenging to directly link neuroimaging results to plausible underlying neural mechanisms. Here, we argue that dynamical systems theory provides the crucial mechanistic framework for characterizing both the brain's time-varying quality and its partial stability in the face of perturbations, and hence, that this perspective can have a profound impact on the interpretation of human neuroimaging results and their relationship with behavior. After briefly reviewing some key terminology, we identify three key ways in which neuroimaging analyses can embrace a dynamical systems perspective: by shifting from a local to a more global perspective, by focusing on dynamics instead of static snapshots of neural activity, and by embracing modeling approaches that map neural dynamics using "forward" models. Through this approach, we envisage ample opportunities for neuroimaging researchers to enrich their understanding of the dynamic neural mechanisms that support a wide array of brain functions, both in health and in the setting of psychopathology.

Neuronal avalanche dynamics and functional connectivity elucidate information propagation in vitro

Cascading activity is commonly observed in complex dynamical systems, including networks of biological neurons, and how these cascades spread through the system is reliant on how the elements of the system are connected and organized. In this work, we studied networks of neurons as they matured over 50 days in vitro and evaluated both their dynamics and their functional connectivity structures by observing their electrophysiological activity using microelectrode array recordings. Correlations were obtained between features of their activity propagation and functional connectivity characteristics to elucidate the interplay between dynamics and structure. The results indicate that in vitro networks maintain a slightly subcritical state by striking a balance between integration and segregation. Our work demonstrates the complementarity of these two approaches—functional connectivity and avalanche dynamics—in studying information propagation in neurons in vitro, which can in turn inform the design and optimization of engineered computational substrates.

An exploratory study on functional connectivity after mild traumatic brain injury: Preserved global but altered local organization

INTRODUCTION

This study aimed to investigate alterations in whole-brain functional connectivity after a concussion using graph-theory analysis from global and local perspectives and explore the association between changes in the functional network properties and cognitive performance.

METHODS

Individuals with mild traumatic brain injury (mTBI, n = 29) within a month after injury, and age- and sex-matched healthy controls (n = 29) were included. Graph-theory measures on functional connectivity assessed using resting state functional magnetic resonance imaging data were acquired from each participant. These included betweenness centrality, strength, clustering coefficient, local efficiency, and global efficiency. Multi-domain cognitive functions were correlated with the graph-theory measures.

RESULTS

In comparison to the controls, the mTBI group showed preserved network characteristics at a global level. However, in the local network, we observed decreased betweenness centrality, clustering coefficient, and local efficiency in several brain areas, including the fronto-parietal attention network. Network strength at the local level showed mixed-results in different areas. The betweenness centrality of the right parahippocampus showed a significant positive correlation with the cognitive scores of the verbal learning test only in the mTBI group.

CONCLUSION

The intrinsic functional connectivity after mTBI is preserved globally, but is suboptimally organized locally in several areas. This possibly reflects the neurophysiological sequelae of a concussion. The present results may imply that the network property could be used as a potential indicator for clinical outcomes after mTBI.

Science Forum: How failure to falsify in high-volume science contributes to the replication crisis

The number of scientific papers published every year continues to increase, but scientific knowledge is not progressing at the same rate. Here we argue that a greater emphasis on falsification – the direct testing of strong hypotheses – would lead to faster progress by allowing well-specified hypotheses to be eliminated. We describe an example from neuroscience where there has been little work to directly test two prominent but incompatible hypotheses related to traumatic brain injury. Based on this example, we discuss how building strong hypotheses and then setting out to falsify them can bring greater precision to the clinical neurosciences, and argue that this approach could be beneficial to all areas of science.

Lawrence K, Tachtsidis I, Tong Y, Torricelli A, Urner T, Wabnitz H, Wolf M, Wolf U, Xu S, Yang C, Yodh AG, Yücel MA, Zhou W. Optical imaging and spectroscopy for the study of the human brain: status report

This report is the second part of a comprehensive two-part series aimed at reviewing an extensive and diverse toolkit of novel methods to explore brain health and function. While the first report focused on neurophotonic tools mostly applicable to animal studies, here, we highlight optical spectroscopy and imaging methods relevant to noninvasive human brain studies. We outline current state-of-the-art technologies and software advances, explore the most recent impact of these technologies on neuroscience and clinical applications, identify the areas where innovation is needed, and provide an outlook for the future directions.

Correlates and predictors of the severity of suicidal ideation in adolescence: an examination of brain connectomics and psychosocial characteristics

BACKGROUND

Suicidal ideation (SI) typically emerges during adolescence but is challenging to predict. Given the potentially lethal consequences of SI, it is important to identify neurobiological and psychosocial variables explaining the severity of SI in adolescents.

METHODS

In 106 participants (59 female) recruited from the community, we assessed psychosocial characteristics and obtained resting-state fMRI data in early adolescence (baseline: aged 9-13 years). Across 250 brain regions, we assessed local graph theory-based properties of interconnectedness: local efficiency, eigenvector centrality, nodal degree, within-module z-score, and participation coefficient. Four years later (follow-up: ages 13-19 years), participants self-reported their SI severity. We used least absolute shrinkage and selection operator (LASSO) regressions to identify a linear combination of psychosocial and brain-based variables that best explain the severity of SI symptoms at follow-up. Nested-cross-validation yielded model performance statistics for all LASSO models.

RESULTS

A combination of psychosocial and brain-based variables explained subsequent severity of SI (R² = .55); the strongest was internalizing and externalizing symptom severity at follow-up. Follow-up LASSO regressions of psychosocial-only and brain-based-only variables indicated that psychosocial-only variables explained 55% of the variance in SI severity; in contrast, brain-based-only variables performed worse than the null model.

CONCLUSIONS

A linear combination of baseline and follow-up psychosocial variables best explained the severity of SI. Follow-up analyses indicated that graph theory resting-state metrics did not increase the prediction of the severity of SI in adolescents. Attending to internalizing and externalizing symptoms is important in early adolescence; resting-state connectivity properties other than local graph theory metrics might yield a stronger prediction of the severity of SI.

Graph Theoretical Analysis of Structural Covariance Reveals the Relevance of Visuospatial and Attentional Areas in Essential Tremor Recovery After Stereotactic Radiosurgical Thalamotomy

Essential tremor (ET) is the most common movement disorder. Its pathophysiology is only partially understood. Here, we leveraged graph theoretical analysis on structural covariance patterns quantified from morphometric estimates for cortical thickness, surface area, and mean curvature in patients with ET before and one year after (to account for delayed clinical effect) ventro-intermediate nucleus (Vim) stereotactic radiosurgical thalamotomy. We further contrasted the observed patterns with those from matched healthy controls (HCs). Significant group differences at the level of individual morphometric properties were specific to mean curvature and the post-/pre-thalamotomy contrast, evidencing brain plasticity at the level of the targeted left thalamus, and of low-level visual, high-level visuospatial and attentional areas implicated in the dorsal visual stream. The introduction of cross-correlational analysis across pairs of morphometric properties strengthened the presence of dorsal visual stream readjustments following thalamotomy, as cortical thickness in the right lingual gyrus, bilateral rostral middle frontal gyrus, and left pre-central gyrus was interrelated with mean curvature in the rest of the brain. Overall, our results position mean curvature as the most relevant morphometric feature to understand brain plasticity in drug-resistant ET patients following Vim thalamotomy. They also highlight the importance of examining not only individual features, but also their interactions, to gain insight into the routes of recovery following intervention.

Disrupted Topological Organization of Resting-State Functional Brain Networks in Age-Related Hearing Loss

Purpose

Age-related hearing loss (ARHL), associated with the function of speech perception decreases characterized by bilateral sensorineural hearing loss at high frequencies, has become an increasingly critical public health problem. This study aimed to investigate the topological features of the brain functional network and structural dysfunction of the central nervous system in ARHL using graph theory.

Methods

Forty-six patients with ARHL and forty-five age, sex, and education-matched healthy controls were recruited to undergo a resting-state functional magnetic resonance imaging (fMRI) scan in this study. Graph theory was applied to analyze the topological properties of the functional connectomes by studying the local and global organization of neural networks.

Results

Compared with healthy controls, the patient group showed increased local efficiency (E_loc) and clustering coefficient (C_p) of the small-world network. Besides, the degree centrality (Dc) and nodal efficiency (Ne) values of the left inferior occipital gyrus (IOG) in the patient group showed a decrease in contrast with the healthy control group. In addition, the intra-modular interaction of the occipital lobe module and the inter-modular interaction of the parietal occipital module decreased in the patient group, which was positively correlated with Dc and Ne. The intra-modular interaction of the occipital lobe module decreased in the patient group, which was negatively correlated with the E_loc.

Conclusion

Based on fMRI and graph theory, we indicate the aberrant small-world network topology in ARHL and dysfunctional interaction of the occipital lobe and parietal lobe, emphasizing the importance of dysfunctional left IOG. These results suggest that early diagnosis and treatment of patients with ARHL is necessary, which can avoid the transformation of brain topology and decreased brain function.

Elevated Gamma Connectivity in Nidopallium Caudolaterale of Pigeons during Spatial Path Adjustment

Simple Summary

Imagine that you need to reach a designated destination, but the familiar path you most often choose suddenly becomes impassable. Then, what will you do? Of course, you will try to adjust the path according to your cognition of the current environment and the goal. During this, how will be the spatial environment, especially the path adjustment process, be represented in your brain? That is a very interesting research topic. In this study, we attempted to explore the internal neural patterns within the brain, especially within the higher-order cognitive brain areas, by taking pigeons, a species with excellent navigation ability, as a model animal. The most classical detour paradigm was used to train pigeons in a task of spatial path adjustment, and the neural signals in pigeons’ nidopallium caudolaterale ((NCL) functionally similar to mammalian “prefrontal cortex”) were recorded. We found that the spatial path adjustment process is accompanied by modifications of the changes in spectral and connectivity properties of the neural activities in the NCL. The elevated gamma connectivity in the NCL found in this study supports the role of the NCL in spatial cognition and contributes to explaining the potential neural mechanism of path adjustment.

Abstract

Previous studies showed that spatial navigation depends on a local network including multiple brain regions with strong interactions. However, it is still not fully understood whether and how the neural patterns in avian nidopallium caudolaterale (NCL), which is suggested to play a key role in navigation as a higher cognitive structure, are modulated by the behaviors during spatial navigation, especially involved path adjustment needs. Hence, we examined neural activity in the NCL of pigeons and explored the local field potentials’ (LFPs) spectral and functional connectivity patterns in a goal-directed spatial cognitive task with the detour paradigm. We found the pigeons progressively learned to solve the path adjustment task when the learned path was blocked suddenly. Importantly, the behavioral changes during the adjustment were accompanied by the modifications in neural patterns in the NCL. Specifically, the spectral power in lower bands (1–4 Hz and 5–12 Hz) decreased as the pigeons were tested during the adjustment. Meanwhile, an elevated gamma (31–45 Hz and 55–80 Hz) connectivity in the NCL was also detected. These results and the partial least square discriminant analysis (PLS-DA) modeling analysis provide insights into the neural activities in the avian NCL during the spatial path adjustment, contributing to understanding the potential mechanism of avian spatial encoding. This study suggests the important role of the NCL in spatial learning, especially path adjustment in avian navigation.

Altered Default Mode Network Associated with Pesticide Exposure in Latinx Children from Rural Farmworker Families

Pesticide exposure has been associated with adverse cognitive and neurological effects. However, neuroimaging studies aimed at examining the impacts of pesticide exposure on brain networks underlying abnormal neurodevelopment in children remain limited. It has been demonstrated that pesticide exposure in children is associated with disrupted brain anatomy in regions that make up the default mode network (DMN), a subnetwork engaged across a diverse set of cognitive processes, particularly higher-order cognitive tasks. This study tested the hypothesis that functional brain network connectivity/topology in Latinx children from rural farmworker families (FW children) would differ from urban Latinx children from non-farmworker families (NFW children). We also tested the hypothesis that probable historic childhood exposure to pesticides among FW children would be associated with network connectivity/topology in a manner that parallels differences between FW and NFW children. We used brain networks from functional magnetic resonance imaging (fMRI) data from 78 children and a mixed-effects regression framework to test our hypotheses. We found that network topology was differently associated with the connection probability between FW and NFW children in the DMN. Our results also indicated that, among 48 FW children, historic reports of exposure to pesticides from prenatal to 96 months old were significantly associated with DMN topology, as hypothesized. Although the cause of the differences in brain networks between FW and NFW children cannot be determined using a cross-sectional study design, the observed associations between network connectivity/topology and historic exposure reports in FW children provide compelling evidence for a contribution of pesticide exposure on altering the DMN network organization in this vulnerable population. Although longitudinal follow-up of the children is necessary to further elucidate the cause and reveal the ultimate neurological implications, these findings raise serious concerns about the potential adverse health consequences from developmental neurotoxicity associated with pesticide exposure in this vulnerable population.

Network Analysis of Time Series: Novel Approaches to Network Neuroscience

In the last two decades, there has been an explosion of interest in modeling the brain as a network, where nodes correspond variously to brain regions or neurons, and edges correspond to structural or statistical dependencies between them. This kind of network construction, which preserves spatial, or structural, information while collapsing across time, has become broadly known as "network neuroscience." In this work, we provide an alternative application of network science to neural data: network-based analysis of non-linear time series and review applications of these methods to neural data. Instead of preserving spatial information and collapsing across time, network analysis of time series does the reverse: it collapses spatial information, instead preserving temporally extended dynamics, typically corresponding to evolution through some kind of phase/state-space. This allows researchers to infer a, possibly low-dimensional, "intrinsic manifold" from empirical brain data. We will discuss three methods of constructing networks from nonlinear time series, and how to interpret them in the context of neural data: recurrence networks, visibility networks, and ordinal partition networks. By capturing typically continuous, non-linear dynamics in the form of discrete networks, we show how techniques from network science, non-linear dynamics, and information theory can extract meaningful information distinct from what is normally accessible in standard network neuroscience approaches.

A hands-on tutorial on network and topological neuroscience

The brain is an extraordinarily complex system that facilitates the optimal integration of information from different regions to execute its functions. With the recent advances in technology, researchers can now collect enormous amounts of data from the brain using neuroimaging at different scales and from numerous modalities. With that comes the need for sophisticated tools for analysis. The field of network neuroscience has been trying to tackle these challenges, and graph theory has been one of its essential branches through the investigation of brain networks. Recently, topological data analysis has gained more attention as an alternative framework by providing a set of metrics that go beyond pairwise connections and offer improved robustness against noise. In this hands-on tutorial, our goal is to provide the computational tools to explore neuroimaging data using these frameworks and to facilitate their accessibility, data visualisation, and comprehension for newcomers to the field. We will start by giving a concise (and by no means complete) overview of the field to introduce the two frameworks and then explain how to compute both well-established and newer metrics on resting-state functional magnetic resonance imaging. We use an open-source language (Python) and provide an accompanying publicly available Jupyter Notebook that uses the 1000 Functional Connectomes Project dataset. Moreover, we would like to highlight one part of our notebook dedicated to the realistic visualisation of high order interactions in brain networks. This pipeline provides three-dimensional (3-D) plots of pairwise and higher-order interactions projected in a brain atlas, a new feature tailor-made for network neuroscience.

A Spatiotemporal Brain Network Analysis of Alzheimer's Disease Based on Persistent Homology

Current brain network studies based on persistent homology mainly focus on the spatial evolution over multiple spatial scales, and there is little research on the evolution of a spatiotemporal brain network of Alzheimer's disease (AD). This paper proposed a persistent homology-based method by combining multiple temporal windows and spatial scales to study the spatiotemporal evolution of brain functional networks. Specifically, a time-sliding window method was performed to establish a spatiotemporal network, and the persistent homology-based features of such a network were obtained. We evaluated our proposed method using the resting-state functional MRI (rs-fMRI) data set from Alzheimer's Disease Neuroimaging Initiative (ADNI) with 31 patients with AD and 37 normal controls (NCs). In the statistical analysis experiment, most network properties showed a better statistical power in spatiotemporal networks than in spatial networks. Moreover, compared to the standard graph theory properties in spatiotemporal networks, the persistent homology-based features detected more significant differences between the groups. In the clustering experiment, the brain networks on the sliding windows of all subjects were clustered into two highly structured connection states. Compared to the NC group, the AD group showed a longer residence time and a higher window ratio in a weak connection state, which may be because patients with AD have not established a firm connection. In summary, we constructed a spatiotemporal brain network containing more detailed information, and the dynamic spatiotemporal brain network analysis method based on persistent homology provides stronger adaptability and robustness in revealing the abnormalities of the functional organization of patients with AD.

Lower Socioeconomic Position Is Associated with Greater Activity in and Integration within an Allostatic-Interoceptive Brain Network in Response to Affective Stimuli

Socioeconomic inequities shape physical health and emotional well-being. As such, recent work has examined the neural mechanisms through which socioeconomic position (SEP) may influence health. However, there remain critical gaps in knowledge regarding the relationships between SEP and brain function. These gaps include a lack of research on: (1) the association between SEP and brain functioning in later life, (2) relationships between SEP and functioning of the whole brain beyond specific regions of interest, and (3) how neural responses to positive affective stimuli differ by SEP. The current study addressed these gaps by examining the association between SEP (i.e., education, income) and neural responses to affective stimuli among 122 mid- to late-life adults. During MRI scanning, participants viewed 30 positive, 30 negative, and 30 neutral images; activation and network connectivity analyses explored associations between SEP and neural responses to these affective stimuli. Analyses revealed that those with lower SEP showed greater neural activity to both positive and negative images in regions within the allostatic-interoceptive network, a system of regions implicated in representing and regulating physiological states of the body and the external environment. There were no positive associations between SEP and neural responses to negative or positive images. In addition, graph-theory network analyses showed that individuals with lower SEP demonstrated greater global efficiency within the allostatic-interoceptive network and executive control network, across all task conditions. The findings suggest that lower SEP is associated with enhanced neural sensitivity to affective cues that may be metabolically costly to maintain over time and suggest a mechanism by which SEP might get "under the skull" to influence mental and physical well-being.

Feeding the machine: Challenges to reproducible predictive modeling in resting-state connectomics

In this critical review, we examine the application of predictive models, for example, classifiers, trained using machine learning (ML) to assist in interpretation of functional neuroimaging data. Our primary goal is to summarize how ML is being applied and critically assess common practices. Our review covers 250 studies published using ML and resting-state functional MRI (fMRI) to infer various dimensions of the human functional connectome. Results for holdout ("lockbox") performance was, on average, ∼13% less accurate than performance measured through cross-validation alone, highlighting the importance of lockbox data, which was included in only 16% of the studies. There was also a concerning lack of transparency across the key steps in training and evaluating predictive models. The summary of this literature underscores the importance of the use of a lockbox and highlights several methodological pitfalls that can be addressed by the imaging community. We argue that, ideally, studies are motivated both by the reproducibility and generalizability of findings as well as the potential clinical significance of the insights. We offer recommendations for principled integration of machine learning into the clinical neurosciences with the goal of advancing imaging biomarkers of brain disorders, understanding causative determinants for health risks, and parsing heterogeneous patient outcomes.

Reproducibility of graph measures derived from resting-state MEG functional connectivity metrics in sensor and source spaces

Prior studies have used graph analysis of resting‐state magnetoencephalography (MEG) to characterize abnormal brain networks in neurological disorders. However, a present challenge for researchers is the lack of guidance on which network construction strategies to employ. The reproducibility of graph measures is important for their use as clinical biomarkers. Furthermore, global graph measures should ideally not depend on whether the analysis was performed in the sensor or source space. Therefore, MEG data of the 89 healthy subjects of the Human Connectome Project were used to investigate test–retest reliability and sensor versus source association of global graph measures. Atlas‐based beamforming was used for source reconstruction, and functional connectivity (FC) was estimated for both sensor and source signals in six frequency bands using the debiased weighted phase lag index (dwPLI), amplitude envelope correlation (AEC), and leakage‐corrected AEC. Reliability was examined over multiple network density levels achieved with proportional weight and orthogonal minimum spanning tree thresholding. At a 100% density, graph measures for most FC metrics and frequency bands had fair to excellent reliability and significant sensor versus source association. The greatest reliability and sensor versus source association was obtained when using amplitude metrics. Reliability was similar between sensor and source spaces when using amplitude metrics but greater for the source than the sensor space in higher frequency bands when using the dwPLI. These results suggest that graph measures are useful biomarkers, particularly for investigating functional networks based on amplitude synchrony.

Group-Level Ranking-Based Hubness Analysis of Human Brain Connectome Reveals Significant Interhemispheric Asymmetry and Intraparcel Heterogeneities

Objective: Graph theory applications are commonly used in connectomics research to better understand connectivity architecture and characterize its role in cognition, behavior and disease conditions. One of the numerous open questions in the field is how to represent inter-individual differences with graph theoretical methods to make inferences for the population. Here, we proposed and tested a simple intuitive method that is based on finding the correlation between the rank-ordering of nodes within each connectome with respect to a given metric to quantify the differences/similarities between different connectomes.

Methods: We used the diffusion imaging data of the entire HCP-1065 dataset of the Human Connectome Project (HCP) (n = 1,065 subjects). A customized cortical subparcellation of HCP-MMP atlas (360 parcels) (yielding a total of 1,598 ROIs) was used to generate connectivity matrices. Six graph measures including degree, strength, coreness, betweenness, closeness, and an overall “hubness” measure combining all five were studied. Group-level ranking-based aggregation method (“measure-then-aggregate”) was used to investigate network properties on population level.

Results: Measure-then-aggregate technique was shown to represent population better than commonly used aggregate-then-measure technique (overall r_s: 0.7 vs 0.5). Hubness measure was shown to highly correlate with all five graph measures (r_s: 0.88–0.99). Minimum sample size required for optimal representation of population was found to be 50 to 100 subjects. Network analysis revealed a widely distributed set of cortical hubs on both hemispheres. Although highly-connected hub clusters had similar distribution between two hemispheres, average ranking values of homologous parcels of two hemispheres were significantly different in 71% of all cortical parcels on group-level.

Conclusion: In this study, we provided experimental evidence for the robustness, limits and applicability of a novel group-level ranking-based hubness analysis technique. Graph-based analysis of large HCP dataset using this new technique revealed striking hemispheric asymmetry and intraparcel heterogeneities in the structural connectivity of the human brain.

ENIGMA brain injury: Framework, challenges, and opportunities

Traumatic brain injury (TBI) is a major cause of disability worldwide, but the heterogeneous nature of TBI with respect to injury severity and health comorbidities make patient outcome difficult to predict. Injury severity accounts for only some of this variance, and a wide range of preinjury, injury-related, and postinjury factors may influence outcome, such as sex, socioeconomic status, injury mechanism, and social support. Neuroimaging research in this area has generally been limited by insufficient sample sizes. Additionally, development of reliable biomarkers of mild TBI or repeated subconcussive impacts has been slow, likely due, in part, to subtle effects of injury and the aforementioned variability. The ENIGMA Consortium has established a framework for global collaboration that has resulted in the largest-ever neuroimaging studies of multiple psychiatric and neurological disorders. Here we describe the organization, recent progress, and future goals of the Brain Injury working group.

Altered brain network topology in children with auditory processing disorder: A resting-state multi-echo fMRI study

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A graph-theoretical approach was used to assess the functional topology in APD.

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Brain networks in APD are similarly integrated and segregated compared to HCs.

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Children with APD have different hub organization.

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Significant group differences were found in the PC measure in the bilateral STG.

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Regional differences observed within the DMN indicate multimodal roles in APD.

Children with auditory processing disorder (APD) experience hearing difficulties, particularly in the presence of competing sounds, despite having normal audiograms. There is considerable debate on whether APD symptoms originate from bottom-up (e.g., auditory sensory processing) and/or top-down processing (e.g., cognitive, language, memory). A related issue is that little is known about whether functional brain network topology is altered in APD. Therefore, we used resting-state functional magnetic resonance imaging data to investigate the functional brain network organization of 57 children from 8 to 14 years old, diagnosed with APD (n = 28) and without hearing difficulties (healthy control, HC; n = 29). We applied complex network analysis using graph theory to assess the whole-brain integration and segregation of functional networks and brain hub architecture. Our results showed children with APD and HC have similar global network properties –i.e., an average of all brain regions– and modular organization. Still, the APD group showed different hub architecture in default mode-ventral attention, somatomotor and frontoparietal-dorsal attention modules. At the nodal level –i.e., single-brain regions–, we observed decreased participation coefficient (PC – a measure quantifying the diversity of between-network connectivity) in auditory cortical regions in APD, including bilateral superior temporal gyrus and left middle temporal gyrus. Beyond auditory regions, PC was also decreased in APD in bilateral posterior temporo-occipital cortices, left intraparietal sulcus, and right posterior insular cortex. Correlation analysis suggested a positive association between PC in the left parahippocampal gyrus and the listening-in-spatialized-noise -sentences task where APD children were engaged in auditory perception. In conclusion, our findings provide evidence of altered brain network organization in children with APD, specific to auditory networks, and shed new light on the neural systems underlying children's listening difficulties.