Mapping anatomical connectivity patterns of human cerebral cortex using in vivo diffusion tensor imaging tractography

Predominant white matter microstructural changes over gray matter in tinnitus brain

INTRODUCTION

To explore microstructure changes across brain white matter and gray matter in tinnitus patients and its effect on neuropsychological performance.

METHODS

The cross-sectional study used Multi-shell Diffusion Weighted Imaging data and neuropsychological assessment from 48 tinnitus patients and 48 healthy controls. Microstructural features across over white matter and gray matter based on Diffusion Tensor Imaging (DTI) and Neurite Orientation Dispersion and Density Imaging (NODDI) model using Tract-Based Spatial Statistics (TBSS) and Gray Matter-Based Spatial Statistics (GBSS), as well as topological properties were derived from an advanced tractography model in subjects. Brain-neuropsychological performance correlations were analyzed.

RESULTS

Tinnitus patients showed decreased axial diffusivity in forceps minor and right corticospinal tract, increased orientation dispersion in forceps minor, decreased connection strength between the right caudate and pericalcarine, right caudate and superior temporal lobe, and left putamen and cuneus. Global network efficiency and local network efficiency were significantly less in tinnitus patients while feeder connection strength was significantly less in tinnitus patients. The orientation dispersion value mediated the relationship between tinnitus status and Trail Making Test-Part B scores. However, no obvious microstructural changes in gray matter were observed.

CONCLUSION

Leveraging multi-shell DWI data, the current study indicated that fiber disruption and internal connectivity organizational changes in brain white matter, rather than gray matter, were more susceptible in tinnitus patients. These microstructural changes in white matter could be associated with changes in cognitive function in tinnitus patients.

Multimodal gradients unify local and global cortical organization

Functional specialization of brain areas and subregions, as well as their integration into large-scale networks, are key principles in neuroscience. Consolidating both local and global perspectives on cortical organization, however, remains challenging. Here, we present an approach to integrate inter- and intra-areal similarities of microstructure, structural connectivity, and functional interactions. Using high-field in-vivo 7 tesla (7 T) Magnetic Resonance Imaging (MRI) data and a probabilistic post-mortem atlas of cortical cytoarchitecture, we derive multimodal gradients that capture cortex-wide organization. Inter-areal similarities follow a canonical sensory-fugal gradient, linking cortical integration with functional diversity across tasks. However, intra-areal heterogeneity does not follow this pattern, with greater variability in association cortices. Findings are replicated in an independent 7 T dataset and a 100-subject 3 tesla (3 T) cohort. These results highlight a robust coupling between local arealization and global cortical motifs, advancing our understanding of how specialization and integration shape human brain function.

Characterization of the white matter networks in schizophrenia patients with metabolic syndrome undergoing risperidone or clozapine treatment

Background

The characteristics of the white matter network in schizophrenia patients with metabolic syndrome (MetS) remain unclear. This study analyzed white matter network characteristics in schizophrenia patients with MetS undergoing risperidone or clozapine treatment and explored their potential association with metabolic index and cognitive function.

Methods

Diffusion tensor imaging was used to evaluate 19 schizophrenia patients with comorbid MetS (MetS-SZ) and 20 schizophrenia patients without MetS (nMetS-SZ), as well as 25 healthy controls (HC). Differences in these network metrics were compared among these through groups using ANCOVAs and post-hoc testing. Associations between differential network metrics and clinical characteristics were also analyzed.

Results

Relative to HC individuals, both MetS-SZ and nMetS-SZ patients exhibited a reduction in bilateral thalamic degree centrality (DC) and nodal efficiency (NE). Relative to the HC group, MetS-SZ patients exhibited reductions in both global efficiency and local efficiency, lower levels of DC in the superior occipital gyrus, and reduced NE in the prefrontal and occipital cortices. Relative to nMetS-SZ patients, MetS-SZ patients also exhibited reduced global efficiency and local efficiency, together with decreases in NE in the prefrontal cortex, medial and paracentral cingulate gyrus, occipital cortex, angular gyrus, and temporal pole. Impairments in executive function were associated with reduced NE values in the right angular gyrus, left medial and paracingulate gyrus. Increases in waist circumference and hip circumference, as well as impairments in executive function, were associated with reductions in NE among patients with schizophrenia.

Conclusion

Specific changes in the structure of the white matter network accompanying cognitive deficits were observed in MetS-SZ patients. These results offer new insight into the mechanisms underlying the neural network in schizophrenia patients with MetS.

Functional and Structural Network Alterations in HIV-Associated Asymptomatic Neurocognitive Disorders: Evidence for Functional Disruptions Preceding Structural Changes

Purpose

This study focuses on the asymptomatic neurocognitive impairment (ANI) stage of HIV-associated neurocognitive disorders (HAND). Using multimodal MRI and large-scale brain network analysis, we aimed to investigate alterations in functional networks, structural networks, and functional-structural coupling in persons with ANI.

Patients and Methods

A total of 95 participants, including 48 healthy controls and 47 persons with HIV-ANI, were enrolled. Resting-state fMRI and diffusion tensor imaging were used to construct functional and structural connectivity matrices. Graph-theoretical analysis was employed to assess inter-group differences in global metrics, nodal characteristics, and functional-structural coupling patterns. Furthermore, machine learning classifiers were used to construct and evaluate classification models based on imaging features from both groups. The performance of different models was compared to identify the optimal diagnostic model for detecting HIV-ANI.

Results

Structural network analysis showed no significant changes in the global or local topological properties of persons with ANI. In contrast, functional networks exhibited significant reorganization in key regions, including the visual, executive control, and default mode networks. Functional-structural coupling was significantly enhanced in the occipital and frontal networks. These changes correlated with immune status, infection duration, and cognitive performance. Furthermore, the classification model integrating graph-theoretical topological features and functional connectivity achieved the best performance, with an area under the curve (AUC) of 0.962 in the test set.

Conclusion

Functional network reorganization and enhanced functional-structural coupling may reflect early synaptic and dendritic damage in persons with ANI, serving as potential early warning signals for HAND progression. These findings provide sensitive biomarkers and valuable perspectives for early diagnosis and intervention.

Investigating brain network dynamics in state-dependent stimulation: A concurrent electroencephalography and transcranial magnetic stimulation study using hidden Markov models

BACKGROUND

Systems neuroscience studies have shown that baseline brain activity can be categorized into large-scale networks (resting-state-networks, RNSs), with influence on cognitive abilities and clinical symptoms. These insights have guided millimeter-precise selection of brain stimulation targets based on RSNs. Concurrently, Transcranial Magnetic Stimulation (TMS) studies revealed that baseline brain states, measured by EEG signal power or phase, affect stimulation outcomes. However, EEG dynamics in these studies are mostly limited to single regions or channels, lacking the spatial resolution needed for accurate network-level characterization.

OBJECTIVE

We aim at mapping brain networks with high spatial and temporal precision and to assess whether the occurrence of specific network-level-states impact TMS outcome. To this end, we will identify large-scale brain networks and explore how their dynamics relates to corticospinal excitability.

METHODS

This study leverages Hidden Markov Models to identify large-scale brain states from pre-stimulus source space high-density-EEG data collected during TMS targeting the left primary motor cortex in twenty healthy subjects. The association between states and fMRI-defined RSNs was explored using the Yeo atlas, and the trial-by-trial relation between states and corticospinal excitability was examined.

RESULTS

We extracted fast-dynamic large-scale brain states with unique spatiotemporal and spectral features resembling major RSNs. The engagement of different networks significantly influences corticospinal excitability, with larger motor evoked potentials when baseline activity was dominated by the sensorimotor network.

CONCLUSIONS

These findings represent a step forward towards characterizing brain network in EEG-TMS with both high spatial and temporal resolution and underscore the importance of incorporating large-scale network dynamics into TMS experiments.

Identifying influential nodes in brain networks via self-supervised graph-transformer

BACKGROUND

Studying influential nodes (I-nodes) in brain networks is of great significance in the field of brain imaging. Most existing studies consider brain connectivity hubs as I-nodes such as the regions of high centrality or rich-club organization. However, this approach relies heavily on prior knowledge from graph theory, which may overlook the intrinsic characteristics of the brain network, especially when its architecture is not fully understood. In contrast, self-supervised deep learning dispenses with manual features, allowing it to learn meaningful representations directly from the data. This approach enables the exploration of I-nodes for brain networks, which is also lacking in current studies.

METHOD

This paper proposes a Self-Supervised Graph Reconstruction framework based on Graph-Transformer (SSGR-GT) to identify I-nodes, which has three main characteristics. First, as a self-supervised model, SSGR-GT extracts the importance of brain nodes to the reconstruction. Second, SSGR-GT uses Graph-Transformer, which is well-suited for extracting features from brain graphs, combining both local and global characteristics. Third, multimodal analysis of I-nodes uses graph-based fusion technology, combining functional and structural brain information.

RESULTS

The I-nodes we obtained are distributed in critical areas such as the superior frontal lobe, lateral parietal lobe, and lateral occipital lobe, with a total of 56 identified across different experiments. These I-nodes are involved in more brain networks than other regions, have longer fiber connections, and occupy more central positions in structural connectivity. They also exhibit strong connectivity and high node efficiency in both functional and structural networks. Furthermore, there is a significant overlap between the I-nodes and both the structural and functional rich-club.

CONCLUSIONS

Experimental results verify the effectiveness of the proposed method, and I-nodes are obtained and discussed. These findings enhance our understanding of the I-nodes within the brain network, and provide new insights for future research in further understanding the brain working mechanisms.

Recovery of activation propagation and self-sustained oscillation abilities in stroke brain networks

Healthy brain networks usually show highly efficient information communication and self-sustained oscillation abilities. However, how the brain network structure affects these dynamics after an injury (stroke) is not very clear. The recovery of structure and dynamics of stroke brain networks over time is still not known precisely. Based on the analysis of a large number of strokes' brain network data, we show that stroke changes the network properties in connection weights, average degree, clustering, community, etc. Yet, they will recover gradually over time to some extent. We then adopt a simplified reaction-diffusion model to investigate stroke patients' activation propagation and self-sustained oscillation abilities. Our results reveal that the stroke slows the adoption time across different brain scales, indicating a weakened brain's activation propagation ability. In addition, we show that the lifetime of self-sustained oscillatory patterns at 3 months post-stroke, patients' brains significantly depart from the healthy ones. Finally, we examine the properties of core networks of self-sustained oscillatory patterns, in which the directed edges denote the main pathways of activation propagation. Our results demonstrate that the lifetime and recovery of self-sustaining patterns are related to the properties of core networks, and the properties in the post-stroke greatly vary from those in the healthy group. Most importantly, the strokes' activation propagation and self-sustained oscillation abilities significantly improve at 1 year post-stroke, driven by structural connection repair. This work may help us to understand the relationship between structure and function in brain disorders.

Analyzing Information Exchange in Parkinson's Disease via Eigenvector Centrality: A Source-Level Magnetoencephalography Study

Background: Parkinson's disease (PD) is a progressive neurodegenerative disorder that manifests through motor and non-motor symptoms. Understanding the alterations in brain connectivity associated with PD remains a challenge that is crucial for enhancing diagnosis and clinical management. Methods: This study utilized Magnetoencephalography (MEG) to investigate brain connectivity in PD patients compared to healthy controls (HCs) by applying eigenvector centrality (EC) measures across different frequency bands. Results: Our findings revealed significant differences in EC between PD patients and HCs in the alpha (8-12 Hz) and beta (13-30 Hz) frequency bands. To go into further detail, in the alpha frequency band, PD patients in the frontal lobe showed higher EC values compared to HCs. Additionally, we found statistically significant correlations between EC measures and clinical impairment scores (UPDRS-III). Conclusions: The proposed results suggest that MEG-derived EC measures can reveal important alterations in brain connectivity in PD, potentially serving as biomarkers for disease severity.

Intermodal Consistency of Whole-Brain Connectivity and Signal Propagation Delays

Measuring propagation of perturbations across the human brain and their transmission delays is critical for network neuroscience, but it is a challenging problem that still requires advancement. Here, we compare results from a recently introduced, noninvasive technique of functional delays estimation from source-reconstructed electro/magnetoencephalography, to the corresponding findings from a large dataset of cortico-cortical evoked potentials estimated from intracerebral stimulations of patients suffering from pharmaco-resistant epilepsies. The two methods yield significantly similar probabilistic connectivity maps and signal propagation delays, in both cases characterized with Pearson correlations greater than 0.5 (when grouping by stimulated parcel is applied for delays). This similarity suggests a correspondence between the mechanisms underpinning the propagation of spontaneously generated scale-free perturbations (i.e., neuronal avalanches observed in resting state activity studied using magnetoencephalography) and the spreading of cortico-cortical evoked potentials. This manuscript provides evidence for the accuracy of the estimate of functional delays obtained noninvasively from reconstructed sources. Conversely, our findings show that estimates obtained from externally induced perturbations in patients capture physiological activities in healthy subjects. In conclusion, this manuscript constitutes a mutual validation between two modalities, broadening their scope of applicability and interpretation. Importantly, the capability to measure delays noninvasively (as per MEG) paves the way for the inclusion of functional delays in personalized large-scale brain models as well as in diagnostic and prognostic algorithms.

Deep Learning-Based Tract Classification of Preoperative DWI Tractography Advances the Prediction of Short-Term Postoperative Language Improvement in Children With Drug-Resistant Epilepsy

OBJECTIVE

To develop an innovative deep convolutional neural network (DCNN)-based tract classification to enhance the prediction of short-term postoperative language improvement using axonal connectivity markers derived from specific language modular networks (LMNs) within the preoperative whole-brain diffusion-weighted imaging connectome (wDWIC).

METHODS

We employed a three-step approach. First, our previous DCNN-based tract classification to detect true-positive eloquent tracts was extended using an open-source database of high-quality wDWIC to facilitate the accurate classification of true-positive tracts within the preoperative backbone wDWIC of individual patients. Next, we applied psychometry-driven DWIC analysis to the resulting DCNN-based backbone wDWIC in order to create core, expressive, and receptive LMNs. Finally, graph and circuit theory-based connectivity markers were assessed within the three LMNs and compared using a series of machine learning algorithms to predict the presence of postoperative language improvement from a given LMN.

RESULTS

The results showed that the extended DCNN tract classification significantly improved the reproducibility of connectivity markers by up to 35.5 of F-statistics across different LMNs. The prediction accuracy increased by up to 40 across different machine learning algorithms. Notably, the best algorithm achieved the accuracy of 96/94/96 to predict the presence of language improvement about two months after surgery in core/expressive/receptive domain of an independent validation cohort.

CONCLUSION

These domains hold great potential to assist physicians in identifying candidates whose language skills stand to benefit from early surgery.

SIGNIFICANCE

DCNN tract classification may be an effective tool to improve predicting short-term postoperative language improvement in pediatric epilepsy surgery.

Amyloid-β deposition predicts oscillatory slowing of magnetoencephalography signals and a reduction of functional connectivity over time in cognitively unimpaired adults

With the ongoing developments in the field of anti-amyloid therapy for Alzheimer's disease, it is crucial to better understand the longitudinal associations between amyloid-β deposition and altered network activity in the living human brain. We included 110 cognitively unimpaired individuals (67.9 ± 5.7 years), who underwent [18F]flutemetamol (amyloid-β)-PET imaging and resting-state magnetoencephalography (MEG) recording at baseline and 4-year follow-up. We tested associations between baseline amyloid-β deposition and MEG measures (oscillatory power and functional connectivity). Next, we examined the relationship between baseline amyloid-β deposition and longitudinal MEG measures, as well as between baseline MEG measures and longitudinal amyloid-β deposition. Finally, we assessed associations between longitudinal changes in both amyloid-β deposition and MEG measures. Analyses were performed using linear mixed models corrected for age, sex and family. At baseline, amyloid-β deposition in orbitofrontal-posterior cingulate regions (i.e. early Alzheimer's disease regions) was associated with higher theta (4-8 Hz) power (β = 0.17, P < 0.01) in- and lower functional connectivity [inverted Joint Permutation Entropy (JPEinv) theta, β = -0.24, P < 0.001] of these regions, lower whole-brain beta (13-30 Hz) power (β = -0.13, P < 0.05) and lower whole-brain functional connectivity (JPEinv theta, β = -0.18, P < 0.001). Whole-brain amyloid-β deposition was associated with higher whole-brain theta power (β = 0.17, P < 0.05), lower whole-brain beta power (β = -0.13, P < 0.05) and lower whole-brain functional connectivity (JPEinv theta, β = -0.21, P < 0.001). Baseline amyloid-β deposition in early Alzheimer's disease regions also predicted future oscillatory slowing, reflected by increased theta power over time in early Alzheimer's disease regions and across the whole brain (β = 0.11, β = 0.08, P < 0.001), as well as decreased whole-brain beta power over time (β = -0.04, P < 0.05). Baseline amyloid-β deposition in early Alzheimer's disease regions also predicted a reduction in functional connectivity between these regions and the rest of the brain over time (JPEinv theta, β = -0.07, P < 0.05). Baseline whole-brain amyloid-β deposition was associated with increased whole-brain theta power over time (β = 0.08, P < 0.01). Baseline MEG measures were not associated with longitudinal amyloid-β deposition. Longitudinal changes in amyloid-β deposition in early Alzheimer's disease regions were associated with longitudinal changes in functional connectivity of early Alzheimer's disease regions (JPEinv theta, β = -0.19, P < 0.05) and the whole brain [corrected amplitude envelope correlations alpha (8-13 Hz), β = -0.22, P < 0.05]. Finally, longitudinal changes in whole-brain amyloid-β deposition were associated with longitudinal changes in whole-brain relative theta power (β = 0.21, P < 0.05). Disruptions of oscillatory power and functional connectivity appear to represent early functional consequences of emerging amyloid-β deposition in cognitively unimpaired individuals. These findings suggest a role for neurophysiology in monitoring disease progression and potential treatment effects in pre-clinical Alzheimer's disease.

Abnormal beta bursts of depression in the orbitofrontal cortex and its relationship with clinical symptoms

BACKGROUND

Recent researches have reported that frequency-specific patterns of neural activity contain not only rhythmically sustained oscillations but also transient-bursts of isolated events. The aim of this study was to investigated the correlation between beta burst and depression in order to explore depressive disease and the neurological underpinnings of disease-related symptoms.

METHODS

We collected resting-state MEG recordings from 30 depressive patients and a matched 40 healthy controls. A Hidden Markov Model (HMM) was applied on source-space time courses for 78 cortical regions of the AAL atlas and the temporal characteristics of beta burst from the matched HMM states were captured. Group differences were evaluated on these beta burst characteristics after permutation tests and, for the depressive group, associations between burst characteristics and clinical symptom severity were determined using Spearman correlation coefficients.

RESULTS

At a threshold of p=0.05corrected, burst characteristics revealed significant differences between depression patients and controls at the group level, including increased burst amplitude in frontal lobe, decreased burst duration in occipital regions, increased burst rate and decreased burst interval time in some brain regions. Furthermore, burst amplitude in the orbitofrontal cortex (OFC) was positively related to the severity of sleep disturbance and burst rate in the OFC was negatively related to the severity of anxiety in depression patients.

CONCLUSIONS

The findings highlight OFC may be a targeted area responsible for the anxiety and sleep disturbance symptom by abnormal beta burst in depressive patients and beta burst characteristics of OFC might serve as a neuro-marker for the depression.

Early childhood stress and amygdala structure in children and adolescents with neurodevelopmental disorders

Children and adolescents with neurodevelopmental disorders such as autism spectrum disorder (ASD) and attention deficit hyperactivity disorder (ADHD) may be more susceptible to early life stress compared to their neurotypical peers. This increased susceptibility may be linked to regionally-specific changes in the striatum and amygdala, brain regions sensitive to stress and critical for shaping maladaptive behavioural responses. This study examined early life stress and its impact on striatal and amygdala development in 62 children and adolescents (35 males, mean age = 10.12 years, SD = 3.6) with ASD (n = 14), ADHD (n = 28), or typical development (TD, n = 20) across two cohorts. We assessed stress from various sources, including from the family environment, loss of loved ones, social stress, and illness/injury. We further examined parenting styles as potential moderators of the effects of early life stress. Volumes of the striatum and amygdala were extracted using an automatic segmentation algorithm. Significant group differences in childhood stress exposure were observed (F = 3.29, df = 8, p = 0.002), with autistic children facing more early life stressors (social stress, illness/injury) compared to those with ADHD and neurotypical peers (both, p < 0.002). In autistic children, amygdala volumes were significantly associated with early life stress related to the familial environment, experiences of significant loss, and illness/injury (all, p < 0.03). Positive parenting moderated these effects. These findings suggest that autistic children are more likely to experience early life stress and exhibit region-specific changes in the amygdala, a key brain region implicated in emotional processing and stress responses. This underscores the need for targeted interventions to support autistic children in managing early life stress to potentially mitigate its impact on brain development.

Investigating the predictive models of efficacy of accelerated neuronavigation-guided rTMS for suicidal depression based on multimodal large-scale brain networks

Background

Accelerated neuronavigation-guided high-dose repetitive transcranial magnetic stimulation (NH-rTMS) can rapidly reduce suicidal ideation and alleviate depressive symptoms in one week. Exploring accelerated NH-rTMS-related biomarkers will enhance the precision of treatment decisions for patients with major depressive disorder (MDD). This study aimed to establish predictive models of treatment response to accelerated NH-rTMS in MDD based on multimodal large-scale brain networks.

Method

In this study, morphological, structural, and functional brain networks were constructed for untreated MDD patients with suicidal ideation before accelerated NH-rTMS treatment. Linear support vector regression methods were utilized to examine the ability of multimodal brain networks in predicting antidepressant and anti-suicidal effects of accelerated NH-rTMS.

Results

We found that both the morphological and structural networks predicted the percentage changes of total Beck Scale of Suicidal Ideation and 24-item Hamilton Depression Rating Scale (HAMD-24) scores. Additionally, the functional networks predicted the percentage changes of total HAMD-24 scores. Further analyses revealed that the structural networks outperformed the morphological and functional networks and the somatomotor module outperformed other subnetworks in the prediction.

Conclusions

In summary, our study provides brain connectome-based predictive models of treatment response to accelerated NH-rTMS in MDD patients with suicidal ideation.

Brain dynamics and personality: a preliminary study

Personality can be considered a system characterized by complex dynamics that are extremely adaptive depending on continuous interactions with the environment and situations. The present preliminary study explores the dynamic interplay between brain flexibility and personality by taking the dynamic approach to personality into account, thereby drawing from Cloninger's psychobiological model. 46 healthy individuals were recruited, and their brain dynamics were assessed using magnetoencephalography (MEG) during the resting state. We identified brain activation patterns and measured brain flexibility by employing the theory of neuronal avalanches. Subsequent correlation analyses revealed a significant positive association between brain flexibility and cooperativeness, thus highlighting the role of brain reconfiguration tendencies in fostering openness, tolerance, and empathy towards others. Additionally, this preliminary finding suggests a neurobiological basis for adaptive social behaviors. Although the results are preliminary, this study provides initial insights into the intricate relationship between brain dynamics and personality, thus laying the groundwork for further research in this emerging field using a dynamic network analysis of the functional activity of the brain.

A gradient of hemisphere-specific dorsal to ventral processing routes in parieto-premotor networks

Networks in the parietal and premotor cortices enable essential human abilities regarding motor processing, including attention and tool use. Even though our knowledge on its topography has steadily increased, a detailed picture of hemisphere-specific integrating pathways is still lacking. With the help of multishell diffusion magnetic resonance imaging, probabilistic tractography, and the Graph Theory Analysis, we investigated connectivity patterns between frontal premotor and posterior parietal brain areas in healthy individuals. With a two-stage node characterization approach, we defined the network role of precisely mapped cortical regions from the Julich-Brain atlas. We found evidence for a third, left-sided, medio-dorsal subpathway in a successively graded dorsal stream, referencing more specialized motor processing on the left. Supplementary motor areas had a strongly lateralized connectivity to either left dorsal or right ventral parietal domains, representing an action-attention dichotomy between hemispheres. The left sulcal parietal regions primarily coupled with areas 44 and 45, mirrored by the inferior frontal junction (IFJ) on the right, a structural lateralization we termed as "Broca's-IFJ switch." We were able to deepen knowledge on gyral and sulcal pathways as well as domain-specific contributions in parieto-premotor networks. Our study sheds new light on the complex lateralization of cortical routes for motor activity in the human brain.

Developmental perspectives on the origins of psychotic disorders: The need for a transdiagnostic approach

Research on serious mental disorders, particularly psychosis, has revealed highly variable symptom profiles and developmental trajectories prior to illness-onset. As Dante Cicchetti pointed out decades before the term "transdiagnostic" was widely used, the pathways to psychopathology emerge in a system involving equifinality and multifinality. Like most other psychological disorders, psychosis is associated with multiple domains of risk factors, both genetic and environmental, and there are many transdiagnostic developmental pathways that can lead to psychotic syndromes. In this article, we discuss our current understanding of heterogeneity in the etiology of psychosis and its implications for approaches to conceptualizing etiology and research. We highlight the need for examining risk factors at multiple levels and to increase the emphasis on transdiagnostic developmental trajectories as a key variable associated with etiologic subtypes. This will be increasingly feasible now that large, longitudinal datasets are becoming available and researchers have access to more sophisticated analytic tools, such as machine learning, which can identify more homogenous subtypes with the ultimate goal of enhancing options for treatment and preventive intervention.

Neural Transformation from Retinotopic to Background-Centric Coordinates in the Macaque Precuneus

Visual information is initially represented in retinotopic coordinates and later in craniotopic coordinates. Psychophysical evidence suggests that visual information is further represented in more general coordinates related to the external world; however, the neural basis of nonegocentric coordinates remains elusive. This study investigates the automatic transformation from egocentric to nonegocentric coordinates in the macaque precuneus (two males, one female), identified by a functional imaging study as a key area for nonegocentric representation. We found that 6.2% of neurons in the precuneus have receptive fields (RFs) anchored to the background rather than to the retina or the head, while 16% had traditional retinotopic RFs. Notably, these two types were not exclusive: many background-centric neurons initially encode a stimulus' position in retinotopic coordinates (up to ∼90 ms from the stimulus onset) but later shift to background coordinates, peaking at ∼150 ms. Regarding retinotopic information, the stimulus dominated the initial period, whereas the background dominated the later period. In the absence of a background, there is a dramatic surge in retinotopic information about the stimulus during the later phase, clearly delineating two distinct periods of retinotopic encoding: one focusing on the figure to be attended and another on the background. These findings suggest that the initial retinotopic information of the stimulus is combined with the background retinotopic information in a subsequent stage, yielding a more stable representation of the stimulus relative to the background through time-division multiplexing.

Breakthroughs and challenges for generating brain network-based biomarkers of treatment response in depression

Treatment outcomes widely vary for individuals diagnosed with major depressive disorder, implicating a need for deeper understanding of the biological mechanisms conferring a greater likelihood of response to a particular treatment. Our improved understanding of intrinsic brain networks underlying depression psychopathology via magnetic resonance imaging and other neuroimaging modalities has helped reveal novel and potentially clinically meaningful biological markers of response. And while we have made considerable progress in identifying such biomarkers over the last decade, particularly with larger, multisite trials, there are significant methodological and practical obstacles that need to be overcome to translate these markers into the clinic. The aim of this review is to review current literature on brain network structural and functional biomarkers of treatment response or selection in depression, with a specific focus on recent large, multisite trials reporting predictive accuracy of candidate biomarkers. Regarding pharmaco- and psychotherapy, we discuss candidate biomarkers, reporting that while we have identified candidate biomarkers of response to a single intervention, we need more trials that distinguish biomarkers between first-line treatments. Further, we discuss the ways prognostic neuroimaging may help to improve treatment outcomes to neuromodulation-based therapies, such as transcranial magnetic stimulation and deep brain stimulation. Lastly, we highlight obstacles and technical developments that may help to address the knowledge gaps in this area of research. Ultimately, integrating neuroimaging-derived biomarkers into clinical practice holds promise for enhancing treatment outcomes and advancing precision psychiatry strategies for depression management. By elucidating the neural predictors of treatment response and selection, we can move towards more individualized and effective depression interventions, ultimately improving patient outcomes and quality of life.

Plastic reorganization of the topological asymmetry of hemispheric white matter networks induced by congenital visual experience deprivation

Congenital blindness offers a unique opportunity to investigate human brain plasticity. The influence of congenital visual loss on the asymmetry of the structural network remains poorly understood. To address this question, we recruited 21 participants with congenital blindness (CB) and 21 age-matched sighted controls (SCs). Employing diffusion and structural magnetic resonance imaging, we constructed hemispheric white matter (WM) networks using deterministic fiber tractography and applied graph theory methodologies to assess topological efficiency (i.e., network global efficiency, network local efficiency, and nodal local efficiency) within these networks. Statistical analyses revealed a consistent leftward asymmetry in global efficiency across both groups. However, a different pattern emerged in network local efficiency, with the CB group exhibiting a symmetric state, while the SC group showed a leftward asymmetry. Specifically, compared to the SC group, the CB group exhibited a decrease in local efficiency in the left hemisphere, which was caused by a reduction in the nodal properties of some key regions mainly distributed in the left occipital lobe. Furthermore, interhemispheric tracts connecting these key regions exhibited significant structural changes primarily in the splenium of the corpus callosum. This result confirms the initial observation that the reorganization in asymmetry of the WM network following congenital visual loss is associated with structural changes in the corpus callosum. These findings provide novel insights into the neuroplasticity and adaptability of the brain, particularly at the network level.

Peak width of skeletonized mean diffusivity as a neuroimaging biomarker in first-episode schizophrenia

Background and objective

Peak width of skeletonized mean diffusivity (PSMD), a fully automated diffusion tensor imaging (DTI) biomarker of white matter (WM) microstructure damage, has been shown to be associated with cognition in various WM pathologies. However, its application in schizophrenic disease remains unexplored. This study aims to investigate PSMD along with other DTI markers in first-episode schizophrenia patients compared to healthy controls (HCs), and explore the correlations between these metrics and clinical characteristics.

Methods

A total of 56 first-episode drug-naive schizophrenia patients and 64 HCs were recruited for this study. Participants underwent structural imaging and DTI, followed by comprehensive clinical assessments, including the Positive and Negative Syndrome Scale (PANSS) for patients and cognitive function tests for all participants. We calculated PSMD, peak width of skeletonized fractional anisotropy (PSFA), axial diffusivity (PSAD), radial diffusivity (PSRD) values, skeletonized average mean diffusivity (MD), average fractional anisotropy (FA), average axial diffusivity (AD), and average radial diffusivity (RD) values as well as structural network global topological parameters, and examined between-group differences in these WM metrics. Furthermore, we investigated associations between abnormal metrics and clinical characteristics.

Results

Compared to HCs, patients exhibited significantly increased PSMD values (t = 2.467, p = 0.015), decreased global efficiency (Z = -2.188, p = 0.029), and increased normalized characteristic path length (lambda) (t = 2.270, p = 0.025). No significant differences were observed between the groups in the remaining metrics, including PSFA, PSAD, PSRD, average MD, FA, AD, RD, local efficiency, normalized cluster coefficient, small-worldness, assortativity, modularity, or hierarchy (p > 0.05). After adjusting for relevant variables, both PSMD and lambda values exhibited a significant negative correlation with reasoning and problem-solving scores (PSMD: r = -0.409, p = 0.038; lambda: r = -0.520, p = 0.006). No statistically significant correlations were observed between each PANSS score and the aforementioned metrics in the patient group (p > 0.05). Multivariate linear regression analysis revealed that increased PSMD (β = -0.426, t = -2.260, p = 0.034) and increased lambda (β = -0.490, t = -2.994, p = 0.007) were independently associated with decreased reasoning and problem-solving scores respectively (R a d j 2 = 0.295, F = 2.951, p = 0.029). But these significant correlations did not withstand FDR correction (p_FDR > 0.05).

Conclusion

PSMD can be considered as a valuable neuroimaging biomarker that complements conventional diffusion measurements for investigating abnormalities in WM microstructural integrity and cognitive functions in schizophrenia.

Age-related differences in resting-state, task-related, and structural brain connectivity: graph theoretical analyses and visual search performance

Previous magnetic resonance imaging (MRI) research suggests that aging is associated with a decrease in the functional interconnections within and between groups of locally organized brain regions (modules). Further, this age-related decrease in the segregation of modules appears to be more pronounced for a task, relative to a resting state, reflecting the integration of functional modules and attentional allocation necessary to support task performance. Here, using graph-theoretical analyses, we investigated age-related differences in a whole-brain measure of module connectivity, system segregation, for 68 healthy, community-dwelling individuals 18-78 years of age. We obtained resting-state, task-related (visual search), and structural (diffusion-weighted) MRI data. Using a parcellation of modules derived from the participants' resting-state functional MRI data, we demonstrated that the decrease in system segregation from rest to task (i.e., reconfiguration) increased with age, suggesting an age-related increase in the integration of modules required by the attentional demands of visual search. Structural system segregation increased with age, reflecting weaker connectivity both within and between modules. Functional and structural system segregation had qualitatively different influences on age-related decline in visual search performance. Functional system segregation (and reconfiguration) influenced age-related decline in the rate of visual evidence accumulation (drift rate), whereas structural system segregation contributed to age-related slowing of encoding and response processes (nondecision time). The age-related differences in the functional system segregation measures, however, were relatively independent of those associated with structural connectivity.

The brain network hub degeneration in Alzheimer's disease

Alzheimer's disease (AD) has been conceptualized as a syndrome of brain network dysfunction. Recent imaging connectomics studies have provided unprecedented opportunities to map structural and functional brain networks in AD. By reviewing molecular, imaging, and computational modeling studies, we have shown that highly connected brain hubs are primarily distributed in the medial and lateral prefrontal, parietal, and temporal regions in healthy individuals and that the hubs are selectively and severely affected in AD as manifested by increased amyloid-beta deposition and regional atrophy, hypo-metabolism, and connectivity dysfunction. Furthermore, AD-related hub degeneration depends on the imaging modality with the most notable degeneration in the medial temporal hubs for morphological covariance networks, the prefrontal hubs for structural white matter networks, and in the medial parietal hubs for functional networks. Finally, the AD-related hub degeneration shows metabolic, molecular, and genetic correlates. Collectively, we conclude that the brain-network-hub-degeneration framework is promising to elucidate the biological mechanisms of network dysfunction in AD, which provides valuable information on potential diagnostic biomarkers and promising therapeutic targets for the disease.

Sex differences in human brain networks in normal and psychiatric populations from the perspective of small-world properties

Females and males are known to be different in the prevalences of multiple psychiatric disorders, while the underlying neural mechanisms are unclear. Based on non-invasive neuroimaging techniques and graph theory, many researchers have tried to use a small-world network model to elucidate sex differences in the brain. This manuscript aims to compile the related research findings from the past few years and summarize the sex differences in human brain networks in both normal and psychiatric populations from the perspective of small-world properties. We reviewed published reports examining altered small-world properties in both the functional and structural brain networks between males and females. Based on four patterns of altered small-world properties proposed: randomization, regularization, stronger small-worldization, and weaker small-worldization, we found that current results point to a significant trend toward more regularization in normal females and more randomization in normal males in functional brain networks. On the other hand, there seems to be no consensus to date on the sex differences in small-world properties of the structural brain networks in normal populations. Nevertheless, we noticed that the sample sizes in many published studies are small, and future studies with larger samples are warranted to obtain more reliable results. Moreover, the number of related studies conducted in psychiatric populations is still limited and more investigations might be needed. We anticipate that these conclusions will contribute to a deeper understanding of the sex differences in the brain, which may be also valuable for developing new methods in the treatment of psychiatric disorders.

Optimal navigability of weighted human brain connectomes in physical space

Communication protocols in the brain connectome describe how to transfer information from one region to another. Typically, these protocols hinge on either the spatial distances between brain regions or the intensity of their connections. Yet, none of them combine both factors to achieve optimal efficiency. Here, we introduce a continuous spectrum of decentralized routing strategies that integrates link weights and the spatial embedding of connectomes to route signal transmission. We implemented the protocols on connectomes from individuals in two cohorts and on group-representative connectomes designed to capture weighted connectivity properties. We identified an intermediate domain of routing strategies, a sweet spot, where navigation achieves maximum communication efficiency at low transmission cost. This phenomenon is robust and independent of the particular configuration of weights. Our findings suggest an interplay between the intensity of neural connections and their topology and geometry that amplifies communicability, where weights play the role of noise in a stochastic resonance phenomenon. Such enhancement may support more effective responses to external and internal stimuli, underscoring the intricate diversity of brain functions.

Meso-scale reorganization of local-global brain networks under mild sedation of propofol anesthesia

The fragmentation of the functional brain network has been identified through the functional connectivity (FC) analysis in studies investigating anesthesia-induced loss of consciousness (LOC). However, it remains unclear whether mild sedation of anesthesia can cause similar effects. This paper aims to explore the changes in local-global brain network topology during mild anesthesia, to better understand the macroscopic neural mechanism underlying anesthesia sedation. We analyzed high-density EEG from 20 participants undergoing mild and moderate sedation of propofol anesthesia. By employing a local-global brain parcellation in EEG source analysis, we established binary functional brain networks for each participant. Furthermore, we investigated the global-scale properties of brain networks by estimating global efficiency and modularity, and examined the changes in meso-scale properties of brain networks by quantifying the distribution of high-degree and high-betweenness hubs and their corresponding rich-club coefficients. It is evident from the results that the mild sedation of anesthesia does not cause a significant change in the global-scale properties of brain networks. However, network components centered on SomMot L show a significant decrease, while those centered on Default L, Vis L and Limbic L exhibit a significant increase during the transition from wakefulness to mild sedation (p<0.05). Compared to the baseline state, mild sedation almost doubled the number of high-degree hubs in Vis L, DorsAttn L, Limbic L, Cont L, and reduced by half the number of high-degree hubs in SomMot R, DorsAttn R, SalVentAttn R. Further, mild sedation almost doubled the number of high-betweenness hubs in Vis L, Vis R, Limbic R, Cont R, and reduced by half the number of high-betweenness hubs in SomMot L, SalVentAttn L, Default L, and SomMot R. Our results indicate that mild anesthesia cannot affect the global integration and segregation of brain networks, but influence meso-scale function for integrating different resting-state systems involved in various segregation processes. Our findings suggest that the meso-scale brain network reorganization, situated between global integration and local segregation, could reflect the autonomic compensation of the brain for drug effects. As a direct response and adjustment of the brain network system to drug administration, this spontaneous reorganization of the brain network aims at maintaining consciousness in the case of sedation.

Amyloid, Tau, and APOE in Alzheimer's Disease: Impact on White Matter Tracts

Alzheimer's disease (AD) is characterized by cognitive decline and memory loss due to the abnormal accumulation of amyloid-beta (Aβ) plaques and tau tangles in the brain; its onset and progression also depend on genetic factors such as the apolipoprotein E (APOE) genotype. Understanding how these factors affect the brain's neural pathways is important for early diagnostics and interventions. Tractometry is an advanced technique for 3D quantitative assessment of white matter tracts, localizing microstructural abnormalities in diseased populations in vivo. In this work, we applied BUAN (Bundle Analytics) tractometry to 3D diffusion MRI data from 730 participants in ADNI3 (phase 3 of the Alzheimer's Disease Neuroimaging Initiative; age range: 55-95 years, 349M/381F, 214 with mild cognitive impairment, 69 with AD, and 447 cognitively healthy controls). Using along-tract statistical analysis, we assessed the localized impact of amyloid, tau, and APOE genetic variants on the brain's neural pathways. BUAN quantifies microstructural properties of white matter tracts, supporting along-tract statistical analyses that identify factors associated with brain microstructure. We visualize the 3D profile of white matter tract associations with tau and amyloid burden in Alzheimer's disease; strong associations near the cortex may support models of disease propagation along neural pathways. Relative to the neutral genotype, APOE ϵ3/ϵ3, carriers of the AD-risk conferring APOE ϵ4 genotype show microstructural abnormalities, while carriers of the protective ϵ2 genotype also show subtle differences. Of all the microstructural metrics, mean diffusivity (MD) generally shows the strongest associations with AD pathology, followed by axial diffusivity (AxD) and radial diffusivity (RD), while fractional anisotropy (FA) is typically the least sensitive metric. Along-tract microstructural metrics are sensitive to tau and amyloid accumulation, showing the potential of diffusion MRI to track AD pathology and map its impact on neural pathways.

Individualized Texture Similarity Network in Schizophrenia

BACKGROUND

Structural covariance network disruption has been considered an important pathophysiological indicator for schizophrenia. Here, we introduced a novel individualized structural covariance network measure, referred to as a texture similarity network (TSN), and hypothesized that the TSN could reliably reveal unique intersubject heterogeneity and complex dysconnectivity patterns in schizophrenia.

METHODS

The TSN was constructed by measuring the covariance of 180 three-dimensional voxelwise gray-level co-occurrence matrix feature maps between brain areas in each participant. We first tested the validity and reproducibility of the TSN in characterizing the intersubject variability in 2 longitudinal test-retest healthy cohorts. The TSN was further applied to elucidate intersubject variability and dysconnectivity patterns in 10 schizophrenia case-control datasets (609 schizophrenia cases vs. 579 controls) as well as in a first-episode depression dataset (69 patients with depression vs. 69 control participants).

RESULTS

The test-retest analysis demonstrated higher TSN intersubject than intrasubject variability. Moreover, the TSN reliably revealed higher intersubject variability in both chronic and first-episode schizophrenia, but not in depression. The TSN also reproducibly detected coexistent increased and decreased TSN strength in widespread brain areas, increased global small-worldness, and the coexistence of both structural hyposynchronization in the central networks and hypersynchronization in peripheral networks in patients with schizophrenia but not in patients with depression. Finally, aberrant intersubject variability and covariance strength patterns revealed by the TSN showed a missing or weak correlation with other individualized structural covariance network measures, functional connectivity, and regional volume changes.

CONCLUSIONS

These findings support the reliability of a TSN in revealing unique structural heterogeneity and complex dysconnectivity in patients with schizophrenia.

Altered spreading of fast aperiodic brain waves relates to disease duration in Amyotrophic Lateral Sclerosis

OBJECTIVE

To test the hypothesis that patients affected by Amyotrophic Lateral Sclerosis (ALS) show an altered spatio-temporal spreading of neuronal avalanches in the brain, and that this may related to the clinical picture.

METHODS

We obtained the source-reconstructed magnetoencephalography (MEG) signals from thirty-six ALS patients and forty-two healthy controls. Then, we used the construct of the avalanche transition matrix (ATM) and the corresponding network parameter nodal strength to quantify the changes in each region, since this parameter provides key information about which brain regions are mostly involved in the spreading avalanches.

RESULTS

ALS patients presented higher values of the nodal strength in both cortical and sub-cortical brain areas. This parameter correlated directly with disease duration.

CONCLUSIONS

In this work, we provide a deeper characterization of neuronal avalanches propagation in ALS, describing their spatio-temporal trajectories and identifying the brain regions most likely to be involved in the process. This makes it possible to recognize the brain areas that take part in the pathogenic mechanisms of ALS. Furthermore, the nodal strength of the involved regions correlates directly with disease duration.

SIGNIFICANCE

Our results corroborate the clinical relevance of aperiodic, fast large-scale brain activity as a biomarker of microscopic changes induced by neurophysiological processes.

Gaming expertise induces meso‑scale brain plasticity and efficiency mechanisms as revealed by whole-brain modeling

Video games are a valuable tool for studying the effects of training and neural plasticity on the brain. However, the underlying mechanisms related to plasticity-associated brain structural changes and their impact on brain dynamics are unknown. Here, we used a semi-empirical whole-brain model to study structural neural plasticity mechanisms linked to video game expertise. We hypothesized that video game expertise is associated with neural plasticity-mediated changes in structural connectivity that manifest at the meso‑scale level, resulting in a more segregated functional network topology. To test this hypothesis, we combined structural connectivity data of StarCraft II video game players (VGPs, n = 31) and non-players (NVGPs, n = 31), with generic fMRI data from the Human Connectome Project and computational models, to generate simulated fMRI recordings. Graph theory analysis on simulated data was performed during both resting-state conditions and external stimulation. VGPs' simulated functional connectivity was characterized by a meso‑scale integration, with increased local connectivity in frontal, parietal, and occipital brain regions. The same analyses at the level of structural connectivity showed no differences between VGPs and NVGPs. Regions that increased their connectivity strength in VGPs are known to be involved in cognitive processes crucial for task performance such as attention, reasoning, and inference. In-silico stimulation suggested that differences in FC between VGPs and NVGPs emerge in noisy contexts, specifically when the noisy level of stimulation is increased. This indicates that the connectomes of VGPs may facilitate the filtering of noise from stimuli. These structural alterations drive the meso‑scale functional changes observed in individuals with gaming expertise. Overall, our work sheds light on the mechanisms underlying structural neural plasticity triggered by video game experiences.

Delineating abnormal individual structural covariance brain network organization in pediatric epilepsy with unilateral resection of visual cortex

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

A Hybrid Routing Pattern in Human Brain Structural Network Revealed By Evolutionary Computation

The human brain functional connectivity network (FCN) is constrained and shaped by the communication processes in the structural connectivity network (SCN). The underlying communication mechanism thus becomes a critical issue for understanding the formation and organization of the FCN. A number of communication models supported by different routing strategies have been proposed, with shortest path (SP), random diffusion (DIF), and spatial navigation (NAV) as the most typical, respectively requiring network global knowledge, local knowledge, and both for path seeking. Yet these models all assumed every brain region to use one routing strategy uniformly, ignoring convergent evidence that supports the regional heterogeneity in both terms of biological substrates and functional roles. In this regard, the current study developed a hybrid communication model that allowed each brain region to choose a routing strategy from SP, DIF, and NAV independently. A genetic algorithm was designed to uncover the underlying region-wise hybrid routing strategy (namely HYB). The HYB was found to outperform the three typical routing strategies in predicting FCN and facilitating robust communication. Analyses on HYB further revealed that brain regions in lower-order functional modules inclined to route signals using global knowledge, while those in higher-order functional modules preferred DIF that requires only local knowledge. Compared to regions that used global knowledge for routing, regions using DIF had denser structural connections, participated in more functional modules, but played a less dominant role within modules. Together, our findings further evidenced that hybrid routing underpins efficient SCN communication and locally heterogeneous structure-function coupling.

Weighting the structural connectome: Exploring its impact on network properties and predicting cognitive performance in the human brain

Brain function does not emerge from isolated activity, but rather from the interactions and exchanges between neural elements that form a network known as the connectome. The human connectome consists of structural and functional aspects. The structural connectome (SC) represents the anatomical connections, and the functional connectome represents the resulting dynamics that emerge from this arrangement of structures. As there are different ways of weighting these connections, it is important to consider how such different approaches impact study conclusions. Here, we propose that different weighted connectomes result in varied network properties, and while neither superior the other, selection might affect interpretation and conclusions in different study cases. We present three different weighting models, namely, number of streamlines (NOS), fractional anisotropy (FA), and axon diameter distribution (ADD), to demonstrate these differences. The later, is extracted using recently published AxSI method and is first compared to commonly used weighting methods. Moreover, we explore the functional relevance of each weighted SC, using the Human Connectome Project (HCP) database. By analyzing intelligence-related data, we develop a predictive model for cognitive performance based on graph properties and the National Institutes of Health (NIH) toolbox. Results demonstrate that the ADD SC, combined with a functional subnetwork model, outperforms other models in estimating cognitive performance.

Local signal variability and functional connectivity: Sensitive measures of the excitation-inhibition ratio?

A novel network version of permutation entropy, the inverted joint permutation entropy (JPEinv), holds potential as non-invasive biomarker of abnormal excitation-inhibition (E-I) ratio in Alzheimer's disease (AD). In this computational modelling study, we test the hypotheses that this metric, and related measures of signal variability and functional connectivity, are sensitive to altered E-I ratios. The E-I ratio in each neural mass of a whole-brain computational network model was systematically varied. We evaluated whether JPEinv, local signal variability (by permutation entropy) and functional connectivity (by weighted symbolic mutual information (wsMI)) were related to E-I ratio, on whole-brain and regional level. The hub disruption index can identify regions primarily affected in terms of functional connectivity strength (or: degree) by the altered E-I ratios. Analyses were performed for a range of coupling strengths, filter and time-delay settings. On whole-brain level, higher E-I ratios were associated with higher functional connectivity (by JPEinv and wsMI) and lower local signal variability. These relationships were nonlinear and depended on the coupling strength, filter and time-delay settings. On regional level, hub-like regions showed a selective decrease in functional degree (by JPEinv and wsMI) upon a lower E-I ratio, and non-hub-like regions showed a selective increase in degree upon a higher E-I ratio. These results suggest that abnormal functional connectivity and signal variability, as previously reported in patients across the AD continuum, can inform us about altered E-I ratios.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11571-023-10003-x.

Lower serum uric acid and impairment of right cerebral hemisphere structural brain networks are related to depressive symptoms in cerebral small vessel disease: A cross-sectional study

Cerebral small vessel disease (SVD) may be associated with an increased risk of depressive symptoms. Serum uric acid (SUA), an antioxidant, may be involved in the occurrence and development of depressive symptoms, but the mechanism remains unknown. Moreover, the relationship between structural brain networks and SUA has not been explored. This study examined the relationship between SUA and depressive symptoms in patients with SVD using graph theory analysis. We recruited 208 SVD inpatients and collected fasting blood samples upon admission. Depressive symptoms were assessed using the 24-item Hamilton Depression Rating Scale (HAMD-24). Magnetic resonance imaging was used to evaluate SVD, and diffusion tensor images were used to analyze structural brain networks using graph theory. Patients with depressive symptoms (n = 34, 25.76%) compared to those without (334.53 vs 381.28 μmol/L, p = 0.017) had lower SUA levels. Graph theoretical analyses showed a positive association of SUA with betweenness centrality, nodal efficiency, and clustering coefficients and a negative correlation with the shortest path length in SVD with depressive symptoms group. HAMD scores were significantly associated with nodal network metrics in the right cerebral hemisphere. Our findings suggested that lower SUA levels are significantly associated with disrupted structural brain networks in the right cerebral hemisphere of patients with SVD who have depressive symptoms.

Non-homogenous axonal bouton distribution in whole-brain single-cell neuronal networks

We examined the distribution of pre-synaptic contacts in axons of mouse neurons and constructed whole-brain single-cell neuronal networks using an extensive dataset of 1,891 fully reconstructed neurons. We found that bouton locations were not homogeneous throughout the axon and among brain regions. As our algorithm was able to generate whole-brain single-cell connectivity matrices from full morphology reconstruction datasets, we further found that non-homogeneous bouton locations have a significant impact on network wiring, including degree distribution, triad census, and community structure. By perturbing neuronal morphology, we further explored the link between anatomical details and network topology. In our in silico exploration, we found that dendritic and axonal tree span would have the greatest impact on network wiring, followed by synaptic contact deletion. Our results suggest that neuroanatomical details must be carefully addressed in studies of whole-brain networks at the single-cell level.

Dynamic Changes in Long-Standing Multiple Sclerosis Revealed by Longitudinal Structural Network Analysis Using Diffusion Tensor Imaging

BACKGROUND AND PURPOSE

DTI can be used to derive conventional diffusion measurements, which can measure WM abnormalities in multiple sclerosis. DTI can also be used to construct structural brain networks and derive network measurements. However, few studies have compared their sensitivity in detecting brain alterations, especially in longitudinal studies. Therefore, in this study, we aimed to determine which type of measurement is more sensitive in tracking the dynamic changes over time in MS.

MATERIALS AND METHODS

Eighteen patients with MS were recruited at baseline and followed up at 6 and 12 months. All patients underwent MR imaging and clinical evaluation at 3 time points. Diffusion and network measurements were derived, and their brain changes were evaluated.

RESULTS

None of the conventional DTI measurements displayed statistically significant changes during the follow-up period; however, the nodal degree, nodal efficiency, and nodal path length of the left middle frontal gyrus and bilateral inferior frontal gyrus, opercular part showed significant longitudinal changes between baseline and at 12 months, respectively.

CONCLUSIONS

The nodal degree, nodal efficiency, and nodal path length of the left middle frontal gyrus and bilateral inferior frontal gyrus, opercular part may be used to monitor brain changes over time in MS.

Distinct subdivisions of subcortical U-fiber regions in the gyrencephalic ferret brain

The human cerebrum contains a large amount of cortico-cortical association fibers. Among them, U-fibers are short-range association fibers located in white matter immediately deep to gray matter. Although U-fibers are thought to be crucial for higher cognitive functions, the organization within U-fiber regions are still unclear. Here we investigated the properties of U-fiber regions in the ferret cerebrum using neurochemical, neuronal tracing, immunohistochemical and electron microscopic techniques. We found that U-fiber regions can be subdivided into two regions, which we named outer and inner U-fiber regions. We further uncovered that outer U-fiber regions have smaller-diameter axons with thinner myelin compared with inner U-fiber regions. These findings may indicate functional complexity within U-fiber regions in the cerebrum.

Deep brain stimulation of the central thalamus restores arousal and motivation in a zolpidem-responsive patient with akinetic mutism after severe brain injury

After severe brain injury, zolpidem is known to cause spectacular, often short-lived, restorations of brain functions in a small subgroup of patients. Previously, we showed that these zolpidem-induced neurological recoveries can be paralleled by significant changes in functional connectivity throughout the brain. Deep brain stimulation (DBS) is a neurosurgical intervention known to modulate functional connectivity in a wide variety of neurological disorders. In this study, we used DBS to restore arousal and motivation in a zolpidem-responsive patient with severe brain injury and a concomitant disorder of diminished motivation, more than 10 years after surviving hypoxic ischemia. We found that DBS of the central thalamus, targeted at the centromedian-parafascicular complex, immediately restored arousal and was able to transition the patient from a state of deep sleep to full wakefulness. Moreover, DBS was associated with temporary restoration of communication and ability to walk and eat in an otherwise wheelchair-bound and mute patient. With the use of magnetoencephalography (MEG), we revealed that DBS was generally associated with a marked decrease in aberrantly high levels of functional connectivity throughout the brain, mimicking the effects of zolpidem. These results imply that 'pathological hyperconnectivity' after severe brain injury can be associated with reduced arousal and behavioral performance and that DBS is able to modulate connectivity towards a 'healthier baseline' with lower synchronization, and, can restore functional brain networks long after severe brain injury. The presence of hyperconnectivity after brain injury may be a possible future marker for a patient's responsiveness for restorative interventions, such as DBS, and suggests that lower degrees of overall brain synchronization may be conducive to cognition and behavioral responsiveness.

Alterations of White Matter Connectivity in Adults with Essential Hypertension

Purpose

To explore the topology of the white matter network in individuals with essential hypertension by graph theory.

Patients and Methods

T1-weighted image and diffusion tensor imaging (DTI) data from 43 patients diagnosed with essential hypertension (EHT) and 33 individuals with normotension (healthy controls, HCs) were incorporated in this cross-sectional study. Furthermore, structural networks were constructed by graph theory to calculate whole brain network characteristics and intracerebral node characteristics.

Results

Both EHT and HC groups displayed small-worldness in their structural networks. The area under the curve (AUC) of the small-worldness coefficient (σ) was higher in the EHT group compared to the HC group, whereas the AUC of assortativity was lower in the EHT group in contrast to the HC group. The nodal clustering coefficient (CP) and local efficiency (Eloc) of the EHT group decreased in the right dorsolateral superior frontal gyrus and the left medial superior frontal gyrus. These values increased in the left anterior cingulate and paracingulate gyrus. Furthermore, weight and body mass index (BMI) were positively correlated with σ.

Conclusion

The EHT group showed brain network separation and integration dysfunction. Weight and BMI were positively correlated with σ. The data acquired in this investigation implied that altered structural connectivity in the prefrontal region may be a potential neuroimaging marker in EHT patients.

Mapping individual structural covariance network in development brain with dynamic time warping

A conspicuous property of brain development or maturity is coupled with coordinated or synchronized brain structural co-variation. However, there is still a lack of effective approach to map individual structural covariance network. Here, we developed a novel individual structural covariance network method using dynamic time warping algorithm and applied it to delineate developmental trajectories of topological organizations of structural covariance network from childhood to early adulthood with a large sample of 655 individuals from Human Connectome Project-Development dataset. We found that the individual structural covariance network exhibited small-worldness property and the network global topological characteristics including small-worldness, global efficiency, local efficiency, and modularity linearly increase with age while the shortest path length linearly decreases with age. The nodal topological properties including betweenness and degree increased with age in language and emotion regulation related brain areas, while it decreased with age mainly in visual cortex, sensorimotor area, and hippocampus. Moreover, the topological attributes of structural covariance network as features could predict the age of each individual. Taken together, our results demonstrate that dynamic time warping can effectively map individual structural covariance network to uncover the developmental trajectories of network topology, which may facilitate future investigations to establish the links of structural co-variations with respect to cognition and disease vulnerability.

A radiomics-based brain network in T1 images: construction, attributes, and applications

T1 image is a widely collected imaging sequence in various neuroimaging datasets, but it is rarely used to construct an individual-level brain network. In this study, a novel individualized radiomics-based structural similarity network was proposed from T1 images. In detail, it used voxel-based morphometry to obtain the preprocessed gray matter images, and radiomic features were then extracted on each region of interest in Brainnetome atlas, and an individualized radiomics-based structural similarity network was finally built using the correlational values of radiomic features between any pair of regions of interest. After that, the network characteristics of individualized radiomics-based structural similarity network were assessed, including graph theory attributes, test-retest reliability, and individual identification ability (fingerprinting). At last, two representative applications for individualized radiomics-based structural similarity network, namely mild cognitive impairment subtype discrimination and fluid intelligence prediction, were exemplified and compared with some other networks on large open-source datasets. The results revealed that the individualized radiomics-based structural similarity network displays remarkable network characteristics and exhibits advantageous performances in mild cognitive impairment subtype discrimination and fluid intelligence prediction. In summary, the individualized radiomics-based structural similarity network provides a distinctive, reliable, and informative individualized structural brain network, which can be combined with other networks such as resting-state functional connectivity for various phenotypic and clinical applications.

Structural connectome architecture shapes the maturation of cortical morphology from childhood to adolescence

Cortical thinning is an important hallmark of the maturation of brain morphology during childhood and adolescence. However, the connectome-based wiring mechanism that underlies cortical maturation remains unclear. Here, we show cortical thinning patterns primarily located in the lateral frontal and parietal heteromodal nodes during childhood and adolescence, which are structurally constrained by white matter network architecture and are particularly represented using a network-based diffusion model. Furthermore, connectome-based constraints are regionally heterogeneous, with the largest constraints residing in frontoparietal nodes, and are associated with gene expression signatures of microstructural neurodevelopmental events. These results are highly reproducible in another independent dataset. These findings advance our understanding of network-level mechanisms and the associated genetic basis that underlies the maturational process of cortical morphology during childhood and adolescence.

Network Hyperexcitability in Early-Stage Alzheimer's Disease: Evaluation of Functional Connectivity Biomarkers in a Computational Disease Model

Background

There is increasing evidence from animal and clinical studies that network hyperexcitability (NH) may be an important pathophysiological process and potential target for treatment in early Alzheimer's disease (AD). Measures of functional connectivity (FC) have been proposed as promising biomarkers for NH, but it is unknown which measure has the highest sensitivity for early-stage changes in the excitation/inhibition balance.

Objective

We aim to test the performance of different FC measures in detecting NH at the earliest stage using a computational approach.

Methods

We use a whole brain computational model of activity dependent degeneration to simulate progressive AD pathology and NH. We investigate if and at what stage four measures of FC (amplitude envelope correlation corrected [AECc], phase lag index [PLI], joint permutation entropy [JPE] and a new measure: phase lag time [PLT]) can detect early-stage AD pathophysiology.

Results

The activity dependent degeneration model replicates spectral changes in line with clinical data and demonstrates increasing NH. Compared to relative theta power as a gold standard the AECc and PLI are shown to be less sensitive in detecting early-stage NH and AD-related neurophysiological abnormalities, while the JPE and the PLT show more sensitivity with excellent test characteristics.

Conclusions

Novel FC measures, which are better in detecting rapid fluctuations in neural activity and connectivity, may be superior to well-known measures such as the AECc and PLI in detecting early phase neurophysiological abnormalities and in particular NH in AD. These markers could improve early diagnosis and treatment target identification.

Integration and segregation manifolds in the brain ensure cognitive flexibility during tasks and rest

Adapting to a constantly changing environment requires the human brain to flexibly switch among many demanding cognitive tasks, processing both specialized and integrated information associated with the activity in functional networks over time. In this study, we investigated the nature of the temporal alternation between segregated and integrated states in the brain during rest and six cognitive tasks using functional MRI. We employed a deep autoencoder to explore the 2D latent space associated with the segregated and integrated states. Our results show that the integrated state occupies less space in the latent space manifold compared to the segregated states. Moreover, the integrated state is characterized by lower entropy of occupancy than the segregated state, suggesting that integration plays a consolidating role, while segregation may serve as cognitive expertness. Comparing rest and the tasks, we found that rest exhibits higher entropy of occupancy, indicating a more random wandering of the mind compared to the expected focus during task performance. Our study demonstrates that both transient, short-lived integrated and segregated states are present during rest and task performance, flexibly switching between them, with integration serving as information compression and segregation related to information specialization.

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.

Longitudinal development of the human white matter structural connectome and its association with brain transcriptomic and cellular architecture

From childhood to adolescence, the spatiotemporal development pattern of the human brain white matter connectome and its underlying transcriptomic and cellular mechanisms remain largely unknown. With a longitudinal diffusion MRI cohort of 604 participants, we map the developmental trajectory of the white matter connectome from global to regional levels and identify that most brain network properties followed a linear developmental trajectory. Importantly, connectome-transcriptomic analysis reveals that the spatial development pattern of white matter connectome is potentially regulated by the transcriptomic architecture, with positively correlated genes involve in ion transport- and development-related pathways expressed in excitatory and inhibitory neurons, and negatively correlated genes enriches in synapse- and development-related pathways expressed in astrocytes, inhibitory neurons and microglia. Additionally, the macroscale developmental pattern is also associated with myelin content and thicknesses of specific laminas. These findings offer insights into the underlying genetics and neural mechanisms of macroscale white matter connectome development from childhood to adolescence.

Individual-level brain morphological similarity networks: Current methodologies and applications

AIMS

The human brain is an extremely complex system in which neurons, clusters of neurons, or regions are connected to form a complex network. With the development of neuroimaging techniques, magnetic resonance imaging (MRI)-based brain networks play a key role in our understanding of the intricate architecture of human brain. Among them, the structural MRI-based brain morphological network approach has attracted increasing attention due to the advantages in data acquisition, image quality, and in revealing the structural organizing principles intrinsic to the brain. This review is to summarize the methodology and related applications of individual-level morphological networks.

BACKGROUND

There have been a growing number of studies related to brain morphological similarity networks. Conventional morphological networks are intersubject covariance networks constructed using a certain morphological indicator of a group of subjects; individual-level morphological networks, on the other hand, measure the morphological similarity between brain regions for individual brains and can reflect the morphological information of single subjects. In recent years, individual morphological networks have demonstrated significant worth in exploring the topological changes of the human brain under both normal and disease conditions. Such studies provided novel perspectives for understanding human brain development and exploring the pathological mechanisms of neuropsychiatric disorders.

CONCLUSION

This paper mainly focuses on the studies of brain morphological networks at the individual level, introduces several ways for network construction, reviews representative work in this field, and finally points out current problems and future directions.

Connectomes for 40,000 UK Biobank participants: A multi-modal, multi-scale brain network resource

We mapped functional and structural brain networks for more than 40,000 UK Biobank participants. Structural connectivity was estimated with tractography and diffusion MRI. Resting-state functional MRI was used to infer regional functional connectivity. We provide high-quality structural and functional connectomes for multiple parcellation granularities, several alternative measures of interregional connectivity, and a variety of common data pre-processing techniques, yielding more than one million connectomes in total and requiring more than 200,000 h of compute time. For a single subject, we provide 28 out-of-the-box versions of structural and functional brain networks, allowing users to select, e.g., the parcellation and connectivity measure that best suit their research goals. Furthermore, we provide code and intermediate data for the time-efficient reconstruction of more than 1000 different versions of a subject's connectome based on an array of methodological choices. All connectomes are available via the UK Biobank data-sharing platform and our connectome mapping pipelines are openly available. In this report, we describe our connectome resource in detail for users, outline key considerations in developing an efficient pipeline to map an unprecedented number of connectomes, and report on the quality control procedures that were completed to ensure connectome reliability and accuracy. We demonstrate that our structural and functional connectivity matrices meet a number of quality control checks and replicate previously established findings in network neuroscience. We envisage that our resource will enable new studies of the human connectome in health, disease, and aging at an unprecedented scale.

Topological changes of fast large-scale brain dynamics in mild cognitive impairment predict early memory impairment: a resting-state, source reconstructed, magnetoencephalography study

Functional connectivity has been used as a framework to investigate widespread brain interactions underlying cognitive deficits in mild cognitive impairment (MCI). However, many functional connectivity metrics focus on the average of the periodic activities, disregarding the aperiodic bursts of activity (i.e., the neuronal avalanches) characterizing the large-scale dynamic activities of the brain. Here, we apply the recently described avalanche transition matrix framework to source-reconstructed magnetoencephalography signals in a cohort of 32 MCI patients and 32 healthy controls to describe the spatio-temporal features of neuronal avalanches and explore their topological properties. Our results showed that MCI patients showed a more centralized network (as assessed by higher values of the degree divergence and leaf fraction) as compared to healthy controls. Furthermore, we found that the degree divergence (in the theta band) was predictive of hippocampal memory impairment. These findings highlight the role of the changes of aperiodic bursts in clinical conditions and may contribute to a more thorough phenotypical assessment of patients.