The human connectome: a complex network

Altered white matter topological network and cognitive function in patients with major depressive disorder

OBJECTIVE

This study aims to explore the association between cognitive impairment and white matter topological networks in patients with major depressive disorder (MDD).

METHODS

We enrolled 51 patients diagnosed with MDD and 57 healthy controls for this study. Cognitive function was evaluated using the MATRICS Consensus Cognitive Battery. Diffusion tensor imaging magnetic resonance scans were performed to construct white matter brain networks based on deterministic fiber tracts and we quantified the topological properties of these networks. Then, we investigated the potential association between changes in these network properties and cognitive dysfunction.

RESULTS

MDD patients performed significantly worse in all cognitive domains, including speed of processing, attention/vigilance, working memory, verbal learning, visual learning, reasoning and problem solving, and social cognition. In MDD, there was a positive correlation between local efficiency and processing speed, as well as reasoning and problem solving. Additionally, the local efficiency of the right precuneus was positively correlated with verbal learning.

CONCLUSION

Cognitive symptoms in MDD patients may relate to dysfunction in white matter topological network.

Distinct functional cerebral hypersensitivity networks during incisional and inflammatory pain in rats

Although the pathophysiology of pain has been investigated tremendously, there are still many open questions with regard to specific pain entities and their pain-related symptoms. To increase the translational impact of (preclinical) animal neuroimaging pain studies, the use of disease-specific pain models, as well as relevant stimulus modalities, are critical. We developed a comprehensive framework for brain network analysis combining functional magnetic resonance imaging (MRI) with graph-theory (GT) and data classification by linear discriminant analysis. This enabled us to expand our knowledge of stimulus modalities processing under incisional (INC) and pathogen-induced inflammatory (CFA) pain entities compared to acute pain conditions. GT-analysis has uncovered specific features in pain modality processing that align well with those previously identified in humans. These include areas such as S1, M1, CPu, HC, piriform, and cingulate cortex. Additionally, we have identified unique Network Signatures of Pain Hypersensitivity (NSPH) for INC and CFA. This leads to a diminished ability to differentiate between stimulus modalities in both pain models compared to control conditions, while also enhancing aversion processing and descending pain modulation. Our findings further show that different pain entities modulate sensory input through distinct NSPHs. These neuroimaging signatures are an important step toward identifying novel cerebral pain biomarkers for certain diseases and relevant outcomes to evaluate target engagement of novel therapeutic and diagnostic options, which ultimately can be translated to the clinic.

Electroencephalography estimates brain age in infants with high precision: Leveraging advanced machine learning in healthcare

Changes in the pace of neurodevelopment are key indicators of atypical maturation during early life. Unfortunately, reliable prognostic tools rely on assessments of cognitive and behavioral skills that develop towards the second year of life and after. Early assessment of brain maturation using electroencephalography (EEG) is crucial for clinical intervention and care planning. We developed a reliable methodology using conventional machine learning (ML) and novel deep learning (DL) networks to efficiently quantify the difference between chronological and biological age, so-called brain age gap (BAG) as a marker of accelerated/decelerated biological brain development. In this cross-sectional study, EEG from 219 typically-developing infants aged from three to 14-months was used. For DL networks, the input samples were increased to 2628 recordings. We further validated the BAG tool in a population at clinical risk with abnormal brain growth (macrocephaly) to capture deviation from normal aging. Our results indicate that DL networks outperform conventional ML models, capturing complex non-monotonic EEG characteristics and predicting the biological age with a mean absolute error of only one month (MAE = 1 month, 95 %CI:0.88-1.15, r = 0.82, 95 %CI:0.78-0.85). Additionally, the developing brain follows a trajectory characterized by increased non-linearity and complexity in which alpha rhythm plays an important role. BAG could detect group-level maturational delays between typically-developing and macrocephaly (pvalue=0.009). In macrocephaly, BAG negatively correlated with the general adaptive composite of the ABAS-II (pvalue=0.04) at 18-months and the information processing speed scale of the WPSSI-IV at age four (pvalue=0.006). The EEG-based BAG score offers a reliable non-invasive measure of brain maturation, with significant advantages and implications for developmental neuroscience and clinical practice.

The effect of diet on the development of EEG microstates in healthy infant throughout the first year of life

Electroencephalography (EEG) is used to directly measure neuronal activity and evaluate network dynamics with an excellent temporal resolution. These network dynamics in the form of EEG microstates - distinct yet transiently stable topographies captured at peaks of the global field power - are increasingly used as markers of disease, neurodegeneration, and neurodevelopment. However, few studies have evaluated EEG microstates throughout the first year of life, and currently none have examined the potential effects of infant diet. The current study seeks to investigate whether different diets impact EEG microstates throughout the first year of life. EEGs were collected from approximately 500 healthy infants who were fed a human milk, diary-, or soy-based formula at three, six, nine, and twelve months of age. Microstate classes and temporal characteristics were then calculated for each timepoint and diet. Microstates classes showed a clear developmental trajectory, with duration decreasing with age, and coverage, globally explained variance, and occurrence generally increasing with age. There were relatively few significant differences between infants fed different diets, indicating that diet potentially effects functional neurodevelopment more subtly than previously indicated in the literature. This study adds to the growing body of literature demonstrating that formula feeding does not have clear disadvantages in terms of infant functional neuronal development.

Challenges and perspectives with understanding the concept of mental fatigue

Mental fatigue is referred to as a psychophysiological or neurobiological state caused by prolonged periods of demanding cognitive activity. Sport and exercise science research studies have investigated the effects of experimentally induced mental fatigue on cognitive performance, with mixed results. It has been suggested that negative effects of mental fatigue on cognition performance in laboratory studies could translate to impaired sport performance. However, it remains unclear if impairments in sport performance are due to mental fatigue and how mental fatigue may differ from physical fatigue. Fatigue is well understood as a complex multifactorial construct involving interactions between physiological and neuropsychological responses across brain regions. It may be prudent for researchers to return to the origins of fatigue and cognition before attempting to connect mental fatigue and sport cognition. This article reviews the concept of mental fatigue, its mechanisms and neuroanatomical basis, models of cognition relevant to sports science, investigates how mental fatigue may influence cognition, and suggests future research directions. Mental fatigue as a construct separated from fatigue could be an oversight that has hindered the development of our understanding of mental fatigue. Future sports science research could work to enhance our knowledge of our definitions of fatigue.

The temporal-insula type of temporal plus epilepsy patients with different postoperative seizure outcomes have different cerebral blood flow patterns

PURPOSE

This study retrospectively analyzed preoperative arterial spin labeling (ASL) perfusion MRI data of patients with the temporal-insula type of temporal plus epilepsy (TI-TPE). We aimed to investigate the differences in presurgical cerebral blood flow (CBF) changes in TI-TPE patients with different surgical outcomes.

METHOD

A total of 48 TI-TPE patients confirmed by SEEG were meticulously reviewed for this study. Patients were divided into the seizure-free (SF) group (Engel IA) and the non-seizure-free (NSF) group (Engel IB to IV) according to the Engel seizure classification. The 3D-ASL data of all patients before surgery were analyzed using statistical parametric mapping (SPM) and graph theory analysis. These findings were then compared to healthy controls (HC) based on whole-brain voxel-level analysis and covariance network analysis.

RESULT

At the voxel-level, both SF and NSF groups showed significantly decreased CBF in the ipsilateral transverse temporal gyrus and insula (TTG/insula), contralateral middle cingulate gyrus, precuneus (MCG/precuneus), and increased CBF in the ipsilateral superior temporal gyrus and the superior temporal pole (STG/STP). Wherein the SF group showed more lower CBF in the contralateral MCG/precuneus, with unique increased CBF in the contralateral STG/insula and decreased CBF in the contralateral calcarine as well. In terms of network attributes, the NSF group showed a significantly higher clustering coefficient (Cp), global efficiency (Eglob), local efficiency (Eloc), shorter shortest path length (Lp), and more extensive abnormal nodes compared to the SF and HC groups. While the SF group has higher synchronicity than the HC group.

CONCLUSION

Both SF and NSF groups had abnormal CBF changes at the voxel and network levels with different patterns. The SF group showed more obvious regional CBF changes, while the NSF group showed more extended network disruption, which might underlie different seizure outcomes after local surgical resection.

Machine learning and complex network analysis of drug effects on neuronal microelectrode biosensor data

Biosensors, such as microelectrode arrays that record in vitro neuronal activity, provide powerful platforms for studying neuroactive substances. This study presents a machine learning workflow to analyze drug-induced changes in neuronal biosensor data using complex network measures from graph theory. Microelectrode array recordings of neuronal networks exposed to bicuculline, a GABA[Formula: see text] receptor antagonist known to induce hypersynchrony, demonstrated the workflow's ability to detect and characterize pharmacological effects. The workflow integrates network-based features with synchrony, optimizing preprocessing parameters, including spike train bin sizes, segmentation window sizes, and correlation methods. It achieved high classification accuracy (AUC up to 90%) and used Shapley Additive Explanations to interpret feature importance rankings. Significant reductions in network complexity and segregation, hallmarks of epileptiform activity induced by bicuculline, were revealed. While bicuculline's effects are well established, this framework is designed to be broadly applicable for detecting both strong and subtle network alterations induced by neuroactive compounds. The results demonstrate the potential of this methodology for advancing biosensor applications in neuropharmacology and drug discovery.

Is what I think what you think? Multilayer network-based inter-brain synchrony approach

Social interaction plays a crucial role in human societies, encompassing complex dynamics among individuals. To understand social interaction at the neural level, researchers have utilized hyperscanning in several social settings. These studies have mainly focused on inter-brain synchrony and the efficiency of paired functional brain networks, examining group interactions in dyads. However, this approach may not fully capture the complexity of multiple interactions, potentially leading to gaps in understanding inter-network differences. To overcome this limitation, the present study aims to bridge this gap by introducing methodological enhancements using the multilayer network approach, which is tailored to extract features from multiple networks. We applied this strategy to analyze the triad condition during social behavior processes to identify group interaction indices. Additionally, to validate our methodology, we compared the multilayer networks of triad conditions with group synchrony to paired conditions without group synchrony, focusing on statistical differences between alpha and beta waves. Correlation analysis between inter-brain and group networks revealed that this methodology accurately reflects the characteristics of actual behavioral synchrony. The findings of our study suggest that measures of paired brain synchrony and group interaction may exhibit distinct trends, offering valuable insights into interpreting group synchrony.

Depthwise cortical iron relates to functional connectivity and fluid cognition in healthy aging

Age-related differences in fluid cognition have been associated with both the merging of functional brain networks, defined from resting-state functional magnetic resonance imaging (rsfMRI), and with elevated cortical iron, assessed by quantitative susceptibility mapping (QSM). Limited information is available, however, regarding the depthwise profile of cortical iron and its potential relation to functional connectivity. Here, using an adult lifespan sample (n = 138; 18-80 years), we assessed relations among graph theoretical measures of functional connectivity, column-based depthwise measures of cortical iron, and fluid cognition (i.e., tests of memory, perceptual-motor speed, executive function). Increased age was related both to less segregated functional networks and to increased cortical iron, especially for superficial depths. Functional network segregation mediated age-related differences in memory, whereas depthwise iron mediated age-related differences in general fluid cognition. Lastly, higher mean parietal iron predicted lower network segregation for adults younger than 45 years of age. These findings suggest that functional connectivity and depthwise cortical iron have distinct, complementary roles in the relation between age and fluid cognition in healthy adults.

Contrastive machine learning reveals species -shared and -specific brain functional architecture

A deep comparative analysis of brain functional connectome across species in primates has the potential to yield valuable insights for both scientific and clinical applications. However, the interspecies commonality and differences are inherently entangled with each other and with other irrelevant factors. Here we develop a novel contrastive machine learning method, called shared-unique variation autoencoder (SU-VAE), to allow disentanglement of the species-shared and species-specific functional connectome variation between macaque and human brains on large-scale resting-state fMRI datasets. The method was validated by confirming that human-specific features are differentially related to cognitive scores, while features shared with macaque better capture sensorimotor ones. The projection of disentangled connectomes to the cortex revealed a gradient that reflected species divergence. In contrast to macaque, the introduction of human-specific connectomes to the shared ones enhanced network efficiency. We identified genes enriched on 'axon guidance' that could be related to the human-specific connectomes. The code contains the model and analysis can be found in https://github.com/BBBBrain/SU-VAE.

The multiscale brain structural re-organization that occurs from childhood to adolescence correlates with cortical morphology maturation and functional specialization

From childhood to adolescence, the structural organization of the human brain undergoes dynamic and regionally heterogeneous changes across multiple scales, from synapses to macroscale white matter pathways. However, during this period, the developmental process of multiscale structural architecture, its association with cortical morphological changes, and its role in the maturation of functional organization remain largely unknown. Here, using two independent multimodal imaging developmental datasets aged 6-14 years, we investigated developmental process of multiscale cortical organization by constructing an in vivo multiscale structural connectome model incorporating white matter tractography, cortico-cortical proximity, and microstructural similarity. By employing the gradient mapping method, the principal gradient derived from the multiscale structural connectome effectively recapitulated the sensory-association axis. Our findings revealed a continuous expansion of the multiscale structural gradient space during development, characterized by enhanced differentiation between primary sensory and higher-order transmodal regions along the principal gradient. This age-related differentiation paralleled regionally heterogeneous changes in cortical morphology. Furthermore, the developmental changes in coupling between multiscale structural and functional connectivity were correlated with functional specialization refinement, as evidenced by changes in the participation coefficient. Notably, the differentiation of the principal multiscale structural gradient was associated with improved cognitive abilities, such as enhanced working memory and attention performance, and potentially underpinned by synaptic and hormone-related biological processes. These findings advance our understanding of the intricate maturation process of brain structural organization and its implications for cognitive performance.

Abnormalities in brain complexity in children with autism spectrum disorder: a sleeping state functional MRI study

BACKGROUND AND OBJECTIVE

The theory of complexity loss in neurodivergent brain is widely acknowledged. However, the findings of autism research do not seem to align well with this theory. We aim to investigate the brain complexity in children with ASD (Autism Spectrum Disorders) compared with the TD (Typical Developed) children in sleeping state.

METHOD

42 ASD children and 42 TD children were imaged using sleep-state functional magnetic resonance imaging (ss-fMRI), and brain complexity was analyzed by employing sample entropy (SampEn) and transfer entropy (TE). For the ASD group, we also investigated the relationship of symptom severity with SampEn and with TE.

RESULTS

In compared with TD group, ASD group showed significant increased SampEn in the right inferior frontal gyrus. However, in the group of TD, 13 pairs of brain regions exhibit higher TE compared to the ASD group. In the ASD group, the TE of 5 pairs of brain regions is higher than in the TD group.

CONCLUSION

This sleeping-state fMRI study provide evidence that ASD children exhibited aberrant brain complexity in compare with the TD children. The complexity of the autistic brain is composed of aberrant randomness in brain activity and anomalous information transmission between brain regions. We believe that brain complexity in ASD is a highly valuable area of research. Differences in the entropy of local brain regions, as well as in the transfer entropy between brain regions, may be related to the brain complexity observed in children with ASD.

Structural Connectome Analysis using a Graph-based Deep Model for Age and Dementia Prediction

We tackle the prediction of age and mini-mental state examination (MMSE) score based on structural brain connectivity derived from diffusion magnetic resonance images. We propose a machine-learning model inspired by graph convolutional networks (GCNs), which takes a brain connectivity input graph and processes the data separately through a parallel GCN mechanism with multiple branches, thereby disentangling the input node and graph features. The novelty of our work lies in the model architecture, especially the connectivity attention module, which learns an embedding representation of brain graphs while providing graph-level attention. We show experiments on publicly available datasets of PREVENT-AD and OASIS3. Through our experiments, we validate our model by comparing it to existing methods and via ablations. This quantifies the degree to which the connectome varies depending on the task, which is important for improving our understanding of health and disease across the population. The proposed model generally demonstrates higher performance especially for age prediction compared to the existing machine-learning algorithms we tested, including classical methods and (graph and non-graph) deep learning. We provide a detailed analysis of each component.

The control costs of human brain dynamics

The human brain is a complex system with high metabolic demands and extensive connectivity that requires control to balance energy consumption and functional efficiency over time. How this control is manifested on a whole-brain scale is largely unexplored, particularly what the associated costs are. Using the network control theory, here, we introduce a novel concept, time-averaged control energy (TCE), to quantify the cost of controlling human brain dynamics at rest, as measured from functional and diffusion MRI. Importantly, TCE spatially correlates with oxygen metabolism measures from the positron emission tomography, providing insight into the bioenergetic footing of resting-state control. Examining the temporal dimension of control costs, we find that brain state transitions along a hierarchical axis from sensory to association areas are more efficient in terms of control costs and more frequent within hierarchical groups than between. This inverse correlation between temporal control costs and state visits suggests a mechanism for maintaining functional diversity while minimizing energy expenditure. By unpacking the temporal dimension of control costs, we contribute to the neuroscientific understanding of how the brain governs its functionality while managing energy expenses.

Structural and functional connectomics of the olfactory system in Parkinson's disease: a systematic review

Olfactory dysfunction, affecting 75-90 % of Parkinson's disease (PD) patients, holds significant predictive value for PD development. Advanced imaging techniques, such as diffusion MRI (dMRI) and functional MRI (fMRI), offer insights into structural and functional changes within olfactory pathways. This review summarizes a decade of research on MRI-based connectivity of the olfactory system in PD, focusing on structural and functional alterations in olfactory brain areas and their links to early olfactory processing changes. Fifteen dMRI and eighteen fMRI studies met inclusion criteria and were carefully reviewed. Among the studies investigating diffusion metrics, the most consistent finding was the reduction of fractional anisotropy in the olfactory tract and anterior olfactory structures, though evidence correlating this result to olfactory dysfunction is limited and contrasting. dMRI support the hypothesis that olfactory function may be correlated to structural alterations at the network-level. In contrast, fMRI studies found more consistent evidence of dysconnectivity in both primary and secondary olfactory areas as directly correlated to olfactory processing and dysfunction. Results suggest a potential dissociation between structural alterations in olfactory brain regions and early functional impairment in olfactory processing, likely related to different patient subtypes. Heterogeneity in clinical and technical factors may limit the generalizability of the results, leaving room for further investigations. By providing a comprehensive perspective on the use of dMRI and fMRI to explore the olfactory connectome in PD, our review might facilitate future research towards earlier diagnosis and more targeted therapeutic and neurorehabilitation strategies.

Altered Directed-Connectivity Network in Temporal Lobe Epilepsy: A MEG Study

Temporal lobe epilepsy (TLE) is considered a network disorder rather than a localized lesion, making it essential to study the network mechanisms underlying TLE. In this study, we constructed directed brain networks based on clinical MEG data using the Granger Causality Analysis (GCA) method, aiming to provide new insights into the network mechanisms of TLE. MEG data from 13 lTLE and 21 rTLE patients and 14 healthy controls (HCs) were analyzed. The preprocessed MEG data were used to construct directed brain networks using the GCA method and undirected brain networks using the Pearson Correlation Coefficient (PCC) method. Graph theoretical analysis extracted global and local topologies from the binary matrix, and SVM classified topologies with significant differences (p < 0.05). Comparative studies were performed on connectivity strengths, graph theory metrics, and SVM classifications between GCA and PCC, with an additional analysis of GCA-weighted network connectivity. The results show that TLE patients showed significantly increased functional connectivity based on GCA compared to the control group; similarities of the hub brain regions between lTLE and rTLE patients and the cortical-limbic-thalamic-cortical loop were identified; TLE patients exhibited a significant increase in GCA-based Global Clustering Coefficient (GCC) and Global Local Efficiency (GLE); most brain regions with abnormal local topological properties in TLE patients overlapped with their hub regions. The directionality of brain connectivity has played a significantly more pivotal role in research on TLE. GCA may be a potential tool in MEG analysis to distinguish TLE patients and HC effectively.

Neurosurgical Breakthroughs of the Last 50 Years: A Historical Journey Through the Past and Present

This article presented the author's historical perspective on 25 of the most significant neurosurgical breakthrough events of the last 50 years. These breakthroughs have advanced neurosurgical patient care and management. They have improved the management of aneurysms, arteriovenous malformations, tumors, stroke, traumatic brain injury, movement disorders, epilepsy, hydrocephalus, and spine pathologies. Neurosurgery has evolved through research, innovation, and technology. Several neurosurgical breakthroughs were achieved using neuroendoscopy, neuronavigation, radiosurgery, endovascular techniques, and refinements in computer technology. With these breakthroughs, neurosurgery did not change; it just progressed. Neurosurgery should continue its progress through research to obtain new knowledge for the benefit of our patients.

Emerging Techniques for the Personalization of Deep Brain Stimulation Programming

The success of deep brain stimulation (DBS) relies on applying carefully titrated therapeutic stimulation at specific targets. Once implanted, the electrical stimulation parameters at each electrode contact can be modified. Iteratively adjusting the stimulation parameters enables testing for the optimal stimulation settings. Due to the large parameter space, the currently employed empirical testing of individual parameters based on acute clinical response is not sustainable. Within the constraints of short clinical visits, optimization is particularly challenging when clinical features lack immediate feedback, as seen in DBS for dystonia and depression and with the cognitive and axial side effects of DBS for Parkinson's disease. A personalized approach to stimulation parameter selection is desirable as the increasing complexity of modern DBS devices also expands the number of available parameters. This review describes three emerging imaging and electrophysiological methods of personalizing DBS programming. Normative connectome-base stimulation utilizes large datasets of normal or disease-matched connectivity imaging. The stimulation location for an individual patient can then be varied to engage regions associated with optimal connectivity. Electrophysiology-guided open- and closed-loop stimulation capitalizes on the electrophysiological recording capabilities of modern implanted devices to individualize stimulation parameters based on biomarkers of success or symptom onset. Finally, individual functional MRI (fMRI)-based approaches use fMRI during active stimulation to identify parameters resulting in characteristic patterns of functional engagement associated with long-term treatment response. Each method provides different but complementary information, and maximizing treatment efficacy likely requires a combined approach.

Understanding the Link Between Functional Profiles and Intelligence Through Dimensionality Reduction and Graph Analysis

There is a growing interest in neuroscience for how individual-specific structural and functional features of the cortex relate to cognitive traits. This work builds on previous research which, by using classical high-dimensional approaches, has proven that the interindividual variability of functional connectivity (FC) profiles reflects differences in fluid intelligence. To provide an additional perspective into this relationship, the present study uses a recent framework for investigating cortical organization: functional gradients. This approach places local connectivity profiles within a common low-dimensional space whose axes are functionally interpretable dimensions. Specifically, this study uses a data-driven approach to model the association between FC variability and interindividual differences in intelligence. For one of these loci, in the right ventral-lateral prefrontal cortex (vlPFC), we describe an association between fluid intelligence and the relative functional distance of this area from sensory and high-cognition systems. Furthermore, the topological properties of this region indicate that, with decreasing functional affinity with high-cognition systems, vlPFC functional connections are more evenly distributed across all networks. Participating in multiple functional networks may reflect a better ability to coordinate sensory and high-order cognitive systems.

Application of Transfer Entropy Measure to Characterize Environmental Sounds in Urban and Wild Parks

Anthropized green zones in urban areas and their surroundings develop complex soundscapes, characterized by the presence of multiple sound sources. This makes the interpretation of the sound environment challenging. To accurately distinguish between different sound components, a combination of selective analysis techniques is necessary. Urban parks are significant and interesting examples, where the interaction between anthropogenic and biophonic sound sources persists over broad temporal and spatial scales, making them important sites for evaluating local soundscape quality. In this work, we suggest that a transfer entropy measure (TEM) may more efficiently disentangle relevant information than traditional eco-acoustic indices. The two study areas were Parco Nord in Milan, Italy, and Ticino River Park, also in Italy. For Parco Nord, we used 3.5-h (1-min interval) recordings taken over an area of about 20 hectares, employing 16 sensors. For the Ticino River Park, we used 5-day (1 min plus 5 min pause) recordings taken over an area of approximately 10 hectares, using a smaller set of eight sensors. We calculated the classical eco-acoustic indices and selected two of them: the acoustic entropy (H) and the bio-acoustic index (BI), calculated for all sites with a 1 min time resolution obtained after a principal components analysis. For these time series, we studied the TEM of all sites in both directions, i.e., from one site to another and vice-versa, resulting in asymmetric transfer entropies depending on the location and period of the day. The results suggest the existence of a network of interconnections among sites characterized by strong bio-phonic activity, whereas the interconnection network is damped at sites close to sources of traffic noise. The TEM seems to be independent of the choice of eco-acoustic index time series, and therefore can be considered a robust index of sound quality in urban and wild park environments, providing additional structural insights complementing the traditional approach based on eco-acoustic indices. Specifically, TEM provides directional information about intersite sound connectivity in the area of study, enabling a nuanced understanding of the sound flows across varying anthropogenic and natural sound sources, which is not available using conventional methods.

Case report: Monitoring consciousness with fNIRS in a patient with prolonged reduced consciousness following hemorrhagic stroke undergoing adjunct taVNS therapy

Disorders of consciousness (DoC) resulting from severe brain injury present substantial challenges in rehabilitation due to disruptions in brain network connectivity, particularly within the frontal-parietal network critical for awareness. Transcutaneous auricular vagus nerve stimulation (taVNS) has emerged as a promising non-invasive intervention; however, the precise mechanisms through which it influences cortical function in DoC patients remain unclear. This study describes the effects of taVNS on fronto-parietal network connectivity and arousal in a 77-year-old female patient with unresponsive wakefulness syndrome (UWS). The patient received bilateral taVNS for 1 h daily over 3 months, with functional connectivity (FC) in the frontoparietal network assessed using functional near-infrared spectroscopy (fNIRS) and behavioral responsiveness evaluated through the Coma Recovery Scale-Revised (CRS-R). After taVNS intervention, mean FC was enhanced from 0.06 (SD = 0.31) to 0.33 (SD = 0.28) in the frontal-parietal network. The frontal-parietal were subdivided into 12 regions of interest (ROIs) and it was determined that the FC between the left dorsolateral prefrontal cortex (DLPFC) and the left prefrontal ROIs was 0.06 ± 0.41 before the intervention and 0.55 ± 0.24 after the intervention. Behavioral improvements were evidenced by an increase in CRS-R scores from 2 to 14, marking the patient's transition from UWS to minimally conscious state plus (MCS+). Additionally, regions associated with auditory and sensory processing showed increased cortical engagement, supporting the positive impact of taVNS on cortical responsiveness. This suggests its value as a non-invasive adjunctive therapy in the rehabilitation of DoC patients. Further studies are necessary to confirm these effects in a wider patient population and to refine the strategy for clinical application of taVNS.

From data to decisions: AI and functional connectivity for diagnosis, prognosis, and recovery prediction in stroke

Stroke is a severe medical condition which may lead to permanent disability conditions. The initial 8 weeks following a stroke are crucial for rehabilitation, as most recovery occurs during this period. Personalized approaches and predictive biomarkers are needed for tailored rehabilitation. In this context, EEG brain connectivity and Artificial Intelligence (AI) can play a crucial role in diagnosing and predicting stroke outcomes efficiently. In the present study, 127 patients with subacute ischemic lesions and 90 age- and gender-matched healthy controls were enrolled. EEG recordings were obtained from each participant within 15 days of stroke onset. Clinical evaluations were performed at baseline and at 40-days follow-up using the National Institutes of Health Stroke Scale (NIHSS). Functional connectivity analysis was conducted using Total Coherence (TotCoh) and Small Word (SW). Quadratic support vector machines (SVM) algorithms were implemented to classify healthy subjects compared to stroke patients (Healthy vs Stroke), determine the affected hemisphere (Left vs Right Hemisphere), and predict functional recovery (Functional Recovery Prediction). In the classification for Functional Recovery Prediction, an accuracy of 94.75%, sensitivity of 96.27% specificity of 92.33%, and AUC of 0.95 were achieved; for Healthy vs Stroke, an accuracy of 99.09%, sensitivity of 100%, specificity of 98.46%, and AUC of 0.99 were achieved. For Left vs Right Hemisphere classification, accuracy was 86.77%, sensitivity was 91.44%, specificity was 80.33%, and AUC was 0.87. These findings highlight the potential of utilizing functional connectivity measures based on EEG in combination with AI algorithms to improve patient outcomes by targeted rehabilitation interventions.

Temporoparietal structural-functional coupling abnormalities in drug-naïve first-episode major depressive disorder

INTRODUCTION

Major depressive disorder (MDD) is a debilitating and heterogeneous disease. Many MDD patients experience concurrent anxiety symptoms, often referred to as anxious depression (MDD-ANX). The relationships between network alterations in structural connectivity (SC) and functional connectivity (FC) in MDD and its anxiety-related subtype remain areas that require further investigation.

METHODS

We investigated SC-FC coupling at the system and regional levels in 80 never-treated first-episode MDD patients and 80 healthy control (HC) subjects. For brain systems and regions showing significant between-group coupling differences, we further conducted subgroup comparisons between MDD-ANX, non-anxious depression (MDD-NANX) and HC. We also investigated topological features at the corresponding levels, and assessed the correlation patterns between significant coupling alterations and the topological and clinical characteristics.

RESULTS

Relative to HC, MDD patients showed increased SC-FC coupling in the temporal system (right hippocampus and left superior temporal gyrus [STG]) but decreased coupling in the parietal system (right postcentral gyrus and left angular gyrus). These systems and regions were further characterized by disturbed inter-module connections and impaired structural network efficiency in MDD. Notably, SC-FC coupling of the right hippocampus was significantly increased in MDD-ANX compared to MDD-NANX, which further showed distinct correlation patterns with structural network efficiency.

CONCLUSIONS

Alterations in both SC-FC coupling and topological properties in the temporal and parietal regions provide insights into the interplay between the structural and functional network abnormalities in MDD. SC-FC coupling alterations in the right hippocampus, associated with structural nodal efficiency, may be implicated in the neuropathology of anxious depression.

The pharmacodynamic modulation effect of oxytocin on resting state functional connectivity network topology

Introduction

Neuroimaging studies have demonstrated that intranasal oxytocin has extensive effects on the resting state functional connectivity of social and emotional processing networks and may have therapeutic potential. However, the extent to which intranasal oxytocin modulates functional connectivity network topology remains less explored, with inconsistent findings in the existing literature. To address this gap, we conducted an exploratory data-driven study.

Methods

We recruited 142 healthy males and administered 24 IU of intranasal oxytocin or placebo in a randomized controlled double-blind design. Resting-state functional MRI data were acquired for each subject. Network-based statistical analysis and graph theoretical approaches were employed to evaluate oxytocin's effects on whole-brain functional connectivity and graph topological measures.

Results

Our results revealed that oxytocin altered connectivity patterns within brain networks involved in sensory and motor processing, attention, memory, emotion and reward functions as well as social cognition, including the default mode, limbic, frontoparietal, cerebellar, and visual networks. Furthermore, oxytocin increased local efficiency, clustering coefficients, and small-world propensity in specific brain regions including the cerebellum, left thalamus, posterior cingulate cortex, right orbitofrontal cortex, right superior frontal gyrus, left inferior frontal gyrus, and right middle orbitofrontal cortex, while decreasing nodal path topological measures in the left and right caudate.

Discussion

These findings suggest that intranasal oxytocin may produce its functional effects through influencing the integration and segregation of information flow within small-world brain networks, particularly in regions closely associated with social cognition and motivation.

Structural changes in early-stage Parkinson's disease with resting tremor at node, edge and network level

BACKGROUND

Resting tremor in Parkinson's disease (PD) is associated with the activity in the basal ganglia and cerebello-thalamo-cortical circuits/network. However, most insights stem from functional MRI research, and structural studies, which can provide basis for and constrain functional activity, remains limited.

METHODS

We investigated the structural change in PD patients with resting tremor (PD-WR) from a network perspective. 42 early-stage PD-WR, 27 PD patients without resting tremor (PD-NR), and 56 healthy controls (HC) were included.

RESULTS

PD-WR showed lower cortical thickness in several motor-related lobules. Compared to HC, significant atrophy was found in right lobule VIIA (t = -3.076, p = 0.016, Cohen's d = 0.627), left lobule VI (t = -3.323, p = 0.007, Cohen's d = 0.678), and right lobule VI (t = -3.052, p = 0.017, Cohen's d = 0.623) in PD-WR. Compared to PD-NR, left lobule V also had a significant reduction (t = -2.958, p = 0.023, d = -0.657). PD-WR had higher fractional anisotropy in cerebello-cortical connection compared to HC (t = 3.209, p = 0.009, d = 0.926), with reduced radial (t = -2.561, p = 0.046, d = 0.739) and mean (t = 2.614, p = 0.046, d = 0.871) diffusivity compared to PD-NR. At the network level, better hierarchy (rho = 0.598, p = 0.004), small-worldness (rho = 0.621, p = 0.003), and increased nodal involvement of the thalamus (rho = 0.718, p = 0.031) and motor cortex (rho = 0.660, p = 0.055) were positively correlated with tremor amplitude.

CONCLUSION

Our study supports the alternation of the cerebello-thalamo-cortical circuit in PD-WR. However, further research with other forms of PD, a wide range of disease stage and larger sample size is needed.

Resting-state dynamic functional connectivity in major depressive disorder: A systematic review

As a novel measure, dynamic functional connectivity (dFC) provides insight into the dynamic nature of brain networks and their interactions in resting-state, surpassing traditional static functional connectivity in pathological conditions such as depression. Since a comprehensive review is still lacking, we then reviewed forty-five eligible papers to explore pathological mechanisms of major depressive disorder (MDD) from perspectives including abnormal brain regions and functional networks, brain state, topological properties, relevant recognition, along with longitudinal studies. Though inconsistencies could be found, common findings are: (1) From different perspectives based on dFC, default-mode network (DMN) with its subregions exhibited a close relation to the pathological mechanism of MDD. (2) With a corrupted integrity within large-scale functional networks and imbalance between them, longer fraction time in a relatively weakly-connected state may be a possible property of MDD concerning its relation with DMN. Abnormal transition frequencies between states were correlated to the severity of MDD. (3) Including dynamic properties in topological network metrics enhanced recognition effect. In all, this review summarized its use for clinical diagnosis and treatment, elucidating the non-stationary of MDD patients' aberrant brain activity in the absence of stimuli and bringing new views into its underlying neuro mechanism.

Aberrant brain structural-functional coupling and structural/functional network topology explain developmental delays in pediatric Prader-Willi syndrome

Prader-Willi syndrome (PWS) is a neurodevelopmental disorder characterized by dysplasia in early life. Psychoradiology studies have suggested that mental and behavioral deficits in individuals with PWS are linked to abnormalities in brain structural and functional networks. However, little is known about changes in network-based structural-functional coupling and structural/functional topological properties and their correlations with developmental scales in children with PWS. Here, we acquired diffusion tensor imaging and resting-state functional magnetic resonance imaging data from 25 children with PWS and 28 age- and sex-matched healthy controls, constructed structural and functional networks, examined intergroup differences in structural-functional coupling and structural/functional topological properties (both global and nodal), and tested their partial correlations with developmental scales. We found that children with PWS exhibited (1) decreased structural-functional coupling, (2) a higher characteristic path length and lower global efficiency in the structural network in terms of global properties, (3) alterations in classical cortical and subcortical networks in terms of nodal properties, with the structural network dominated by decreases and the functional network dominated by increases, and (4) partial correlation with developmental scales, especially for functional networks. These findings suggest that structural-functional decoupling and abundant structural/functional network topological properties may reveal the mechanism of early neurodevelopmental delays in PWS from a neuroimaging perspective and might serve as potential markers to assess early neurodevelopmental backwardness in PWS.

Decoding cortical chronotopy-Comparing the influence of different cortical organizational schemes

The brain's diverse intrinsic timescales enable us to perceive stimuli with varying temporal persistency. This study aimed to uncover the cortical organizational schemes underlying these variations, revealing the neural architecture for processing a wide range of sensory experiences. We collected resting-state fMRI, task-fMRI, and diffusion-weighted imaging data from 47 individuals. Based on this data, we extracted six organizational schemes: (1) the structural Rich Club (RC) architecture, shown to synchronize the connectome; (2) the structural Diverse Club architecture, as an alternative to the RC based on the network's module structure; (3) the functional uni-to-multimodal gradient, reflected in a wide range of structural and functional features; and (4) the spatial posterior/lateral-to-anterior/medial gradient, established for hierarchical levels of cognitive control. Also, we explored the effects of (5) structural graph theoretical measures of centrality and (6) cytoarchitectural differences. Using Bayesian model comparison, we contrasted the impact of these organizational schemes on (1) intrinsic resting-state timescales and (2) inter-subject correlation (ISC) from a task involving hierarchically nested digit sequences. As expected, resting-state timescales were slower in structural network hubs, hierarchically higher areas defined by the functional and spatial gradients, and thicker cortical regions. ISC analysis demonstrated hints for the engagement of higher cortical areas with more temporally persistent stimuli. Finally, the model comparison identified the uni-to-multimodal gradient as the best organizational scheme for explaining the chronotopy in both task and rest. Future research should explore the microarchitectural features that shape this gradient, elucidating how our brain adapts and evolves across different modes of processing.

Changes in Functional and Structural Brain Connectivity Following Bilateral Hand Transplantation

As a surgical treatment following amputation or loss of an upper limb, nearly 200 hand transplantations have been completed to date. We report here a magnetic resonance imaging (MRI) investigation of functional and structural brain connectivity for a bilateral hand transplant patient (female, 60 years of age), with a preoperative baseline and three postoperative testing sessions each separated by approximately six months. We used graph theoretical analyses to estimate connectivity within and between modules (networks of anatomical nodes), particularly a sensorimotor network (SMN), from resting-state functional MRI and structural diffusion-weighted imaging (DWI). For comparison, corresponding MRI measures of connectivity were obtained from 10 healthy, age-matched controls, at a single testing session. The patient's within-module functional connectivity (both SMN and non-SMN modules), and structural within-SMN connectivity, were higher preoperatively than that of the controls, indicating a response to amputation. Postoperatively, the patient's within-module functional connectivity decreased towards the control participants' values, across the 1.5 years postoperatively, particularly for hand-related nodes within the SMN module, suggesting a return to a more canonical functional organization. Whereas the patient's structural connectivity values remained relatively constant postoperatively, some evidence suggested that structural connectivity supported the postoperative changes in within-module functional connectivity.

Learning functional brain networks with heterogeneous connectivities for brain disease identification

Functional brain networks (FBNs), which are used to portray interactions between different brain regions, have been widely used to identify potential biomarkers of neurological and mental disorders. The FBNs estimated using current methods tend to be homogeneous, indicating that different brain regions exhibit the same type of correlation. This homogeneity limits our ability to accurately encode complex interactions within the brain. Therefore, to the best of our knowledge, in the present study, for the first time, we propose the existence of heterogeneous FBNs and introduce a novel FBN estimation model that adaptively assigns heterogeneous connections to different pairs of brain regions, thereby effectively encoding the complex interaction patterns in the brain. Specifically, we first construct multiple types of candidate correlations from different views or based on different methods and then develop an improved orthogonal matching pursuit algorithm to select at most one correlation for each brain region pair under the guidance of label information. These adaptively estimated heterogeneous FBNs were then used to distinguish subjects with neurological/mental disorders from healthy controls and identify potential biomarkers related to these disorders. Experimental results on real datasets show that the proposed scheme improves classification performance by 7.07% and 7.58% at the two sites, respectively, compared with the baseline approaches. This emphasizes the plausibility of the heterogeneity hypothesis and effectiveness of the heterogeneous connection assignment algorithm.

Developmental Timing of Associations Among Parenting, Brain Architecture, and Mental Health

Importance

Parenting is associated with brain development and long-term health outcomes, although whether these associations depend on the developmental timing of exposure remains understudied. Identifying these sensitive periods can inform when and how parenting is associated with neurodevelopment and risk for mental illness.

Objective

To characterize how harsh and warm parenting during early, middle, and late childhood are associated with brain architecture during adolescence and, in turn, psychiatric symptoms in early adulthood during the COVID-19 pandemic.

Design, Setting, and Participants

This population-based, 21-year observational, longitudinal birth cohort study of low-income youths and families from Detroit, Michigan; Toledo, Ohio; and Chicago, Illinois, used data from the Future of Families and Child Well-being Study. Data were collected from February 1998 to June 2021. Analyses were conducted from May to October 2023.

Exposures

Parent-reported harsh parenting (psychological aggression or physical aggression) and observer-rated warm parenting (responsiveness) at ages 3, 5, and 9 years.

Main Outcomes and Measures

The primary outcomes were brainwide (segregation, integration, and small-worldness), circuit (prefrontal cortex [PFC]-amygdala connectivity), and regional (betweenness centrality of amygdala and PFC) architecture at age 15 years, determined using functional magnetic resonance imaging, and youth-reported anxiety and depression symptoms at age 21 years. The structured life-course modeling approach was used to disentangle timing-dependent from cumulative associations between parenting and brain architecture.

Results

A total of 173 youths (mean [SD] age, 15.88 [0.53] years; 95 female [55%]) were included. Parental psychological aggression during early childhood was positively associated with brainwide segregation (β = 0.30; 95% CI, 0.14 to 0.45) and small-worldness (β = 0.17; 95% CI, 0.03 to 0.28), whereas parental psychological aggression during late childhood was negatively associated with PFC-amygdala connectivity (β = -0.37; 95% CI, -0.55 to -0.12). Warm parenting during middle childhood was positively associated with amygdala centrality (β = 0.23; 95% CI, 0.06 to 0.38) and negatively associated with PFC centrality (β = -0.18; 95% CI, -0.31 to -0.03). Warmer parenting during middle childhood was associated with reduced anxiety (β = -0.05; 95% CI -0.10 to -0.01) and depression (β = -0.05; 95% CI -0.10 to -0.003) during early adulthood via greater adolescent amygdala centrality.

Conclusions and Relevance

Neural associations with harsh parenting were widespread across the brain in early childhood but localized in late childhood. Neural associations with warm parenting were localized in middle childhood and, in turn, were associated with mental health during future stress. These developmentally contingent associations can inform the type and timing of interventions.

The Functional Connectome and Long-Term Symptom Presentation Associated With Mild Traumatic Brain Injury and Blast Exposure in Combat Veterans

Mild traumatic brain injury (TBI) sustained in a deployment environment (deployment TBI) can be associated with increased severity of long-term symptom presentation, despite the general expectation of full recovery from a single mild TBI. The heterogeneity in the effects of deployment TBI on the brain can be difficult for a case-control design to capture. The functional connectome of the brain is an approach robust to heterogeneity that allows global measurement of effects using a common set of outcomes. The present study evaluates how differences in the functional connectome relate to remote symptom presentation following combat deployment and determines if deployment TBI, blast exposure, or post-traumatic stress disorder (PTSD) are associated with these neurological differences. Participants included 181 Iraq and Afghanistan combat-exposed Veterans, approximately 9.4 years since deployment. Structured clinical interviews provided diagnoses and characterizations of TBI, blast exposure, and PTSD. Self-report measures provided characterization of long-term symptoms (psychiatric, behavioral health, and quality of life). Resting-state magnetoencephalography was used to characterize the functional connectome of the brain individually for each participant. Linear regression identified factors contributing to symptom presentation including relevant covariates, connectome metrics, deployment TBI, blast exposure PTSD, and conditional relationships. Results identified unique contributions of aspects of the connectome to symptom presentation. Furthermore, several conditional relationships were identified, demonstrating that the connectome was related to outcomes in the presence of only deployment-related TBI (including blast-related TBI, primary blast TBI, and blast exposure). No conditional relationships were identified for PTSD; however, the main effect of PTSD on symptom presentation was significant for all models. These results demonstrate that the connectome captures aspects of brain function relevant to long-term symptom presentation, highlighting that deployment-related TBI influences symptom outcomes through a neurological pathway. These findings demonstrate that changes in the functional connectome associated with deployment-related TBI are relevant to symptom presentation over a decade past the injury event, providing a clear demonstration of a brain-based mechanism of influence.

Identification of asymmetrical abnormalities in functional connectivity and brain network topology for migraine sufferers: A preliminary study based on resting-state fMRI data

Research on the neural mechanisms underlying brain asymmetry in patients with migraine patients using fMRI is insufficient. This study proposed using lateralized algorithms for functional connectivity and brain network topology and investigated changes in their lateralization in patients with migraine. In study 1, laterality indices of functional connectivity (LFunctionCorr) and brain network topological properties (LBetweennessCentrality, LDegree, and LStrength) were defined. Differences between migraineurs and normal subjects were compared at whole-brain, half-brain, and region levels. In study 2, laterality indices were used to classify migraine and were validated using independent samples and the segment method for repeatability. In study 3, abnormal brain regions related to migraine were extracted based on the classification results and differences analysis. Study 1 found no significant differences related to in for migraine at the whole-brain level; however, significant differences were identified at the half-brain level for the hemispheric lateralization of the LFunctionCorr, while 11 significantly different brain regions were also identified at the brain region level. Furthermore, the classification accuracy in study 2 was 0.9366. With repeated validation, the accuracy reached 0.8561. Furthermore, after extending the samples according to the segmentation strategy, the classification accuracies were improved to 0.9408 and 0.8585. Study 3 identified 10 crucial brain regions with asymmetrical specificity based on laterality indices distributed across the visual network, the frontoparietal control network, the default mode network, the salience/ventral attention network and the limbic system. The results revealed novel insights and avenues for research into the mechanisms of migraine asymmetry and showed that the laterality indices could be used as a potential diagnostic imaging marker for migraine.

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.

Classical, spaced, or accelerated transcranial magnetic stimulation of motor cortex for treating neuropathic pain: A 3-arm parallel non-inferiority study

BACKGROUND

Repetitive transcranial magnetic stimulation (rTMS) of the primary motor cortex (M1) at high frequency (HF) is an effective treatment of neuropathic pain. The classical HF-rTMS protocol (CHF-rTMS) includes a daily session for one week as an induction phase of treatment followed by more spaced sessions. Another type of protocol without an induction phase and based solely on spaced sessions of HF-rTMS (SHF-rTMS) has also been shown to produce neuropathic pain relief. However, CHF-rTMS and SHF-rTMS of M1 have never been compared regarding their analgesic potential. Another type of rTMS paradigm, called accelerated intermittent theta burst stimulation (ACC-iTBS), has recently been proposed for the treatment of depression, the other clinical condition for which HF-rTMS is proposed as an effective therapeutic strategy. ACC-iTBS combines a high number of pulses delivered in short sessions grouped into a few days of stimulation. This type of protocol has never been applied to M1 for the treatment of pain.

METHODS/DESIGN

The objective of this single-centre randomized study is to compare the efficacy of three different rTMS protocols for the treatment of chronic neuropathic pain: CHF-rTMS, SHF-rTMS, and ACC-iTBS. The CHF-rTMS will consists of 10 stimulation sessions, including 5 daily sessions of 10Hz-rTMS (3,000 pulses per session) over one week, then one session per week for 5 weeks, for a total of 30,000 pulses delivered in 10 stimulation days. The SHF-rTMS protocol will only include 4 sessions of 20Hz-rTMS (1,600 pulses per session), one every 15 days, for a total of 6,400 pulses delivered in 4 stimulation days. The ACC-iTBS protocol will comprise 5 sessions of iTBS (600 pulses per session) completed in half a day for 2 consecutive days, repeated 5 weeks later, for a total of 30,000 pulses delivered in 4 stimulation days. Thus, CHF-rTMS and ACC-iTBS protocols will share a higher total number of TMS pulses (30,000 pulses) compared to SHF-rTMS protocol (6,400 pulses), while CHF-rTMS protocol will include a higher number of stimulation days (10 days) compared to ACC-iTBS and SHF-rTMS protocols (4 days). In all protocols, the M1 target will be defined in the same way and stimulated at the same intensity using a navigated rTMS (nTMS) procedure. The evaluation will be based on clinical outcomes with various scales and questionnaires assessed every week, from two weeks before the 7-week period of therapeutic stimulation until 4 weeks after. Additionally, three sets of neurophysiological outcomes (resting-state electroencephalography (EEG), nTMS-EEG recordings, and short intracortical inhibition measurement with threshold tracking method) will be assessed the week before and after the 7-week period of therapeutic stimulation.

DISCUSSION

This study will make it possible to compare the analgesic efficacy of the CHF-rTMS and SHF-rTMS protocols and to appraise that of the ACC-iTBS protocol for the first time. This study will also make it possible to determine the respective influence of the total number of pulses and days of stimulation delivered to M1 on the extent of pain relief. Thus, if their analgesic efficacy is not inferior to that of CHF-rTMS, SHF-rTMS and especially the new ACC-iTBS protocol could be an optimal compromise of a more easy-to-perform rTMS protocol for the treatment of patients with chronic neuropathic pain.

Somatomotor disconnection links sleep duration with socioeconomic context, screen time, cognition, and psychopathology

Background

Sleep is critical for healthy brain development and emotional wellbeing, especially during adolescence when sleep, behavior, and neurobiology are rapidly evolving. Theoretical reviews and empirical research have historically focused on how sleep influences mental health through its impact on higher-order brain systems. No studies have leveraged data-driven network neuroscience methods to uncover interpretable, brain-wide signatures of sleep duration in adolescence, their socio-environmental origins, or their consequences for cognition and mental health.

Methods

Here, we implement graph theory and component-based predictive modeling to examine how a multimodal index of sleep duration is associated with intrinsic brain architecture in 3,173 youth (11-12 years) from the Adolescent Brain Cognitive DevelopmentSM Study.

Results

We demonstrate that network integration/segregation exhibit a strong, generalizable multivariate association with sleep duration. We next identify a single component of brain architecture centered on a single network as the dominant contributor of this relationship. This component is characterized by increasing disconnection of a lower-order system - the somatomotor network - from other systems, with shorter sleep duration. Finally, greater somatomotor disconnection is associated with lower socioeconomic resources, longer screen times, reduced cognitive/academic performance, and elevated externalizing problems.

Conclusions

These findings reveal a novel neural signature of shorter sleep in adolescence that is intertwined with environmental risk, cognition, and psychopathology. By robustly elucidating the key involvement of an understudied brain system in sleep, cognition, and psychopathology, this study can inform theoretical and translational research directions on sleep to promote neurobehavioral development and mental health during the adolescent transition.

Multiclass classification of Autism Spectrum Disorder, attention deficit hyperactivity disorder, and typically developed individuals using fMRI functional connectivity analysis

Neurodevelopmental conditions, such as Autism Spectrum Disorder (ASD) and Attention Deficit Hyperactivity Disorder (ADHD), present unique challenges due to overlapping symptoms, making an accurate diagnosis and targeted intervention difficult. Our study employs advanced machine learning techniques to analyze functional magnetic resonance imaging (fMRI) data from individuals with ASD, ADHD, and typically developed (TD) controls, totaling 120 subjects in the study. Leveraging multiclass classification (ML) algorithms, we achieve superior accuracy in distinguishing between ASD, ADHD, and TD groups, surpassing existing benchmarks with an area under the ROC curve near 98%. Our analysis reveals distinct neural signatures associated with ASD and ADHD: individuals with ADHD exhibit altered connectivity patterns of regions involved in attention and impulse control, whereas those with ASD show disruptions in brain regions critical for social and cognitive functions. The observed connectivity patterns, on which the ML classification rests, agree with established diagnostic approaches based on clinical symptoms. Furthermore, complex network analyses highlight differences in brain network integration and segregation among the three groups. Our findings pave the way for refined, ML-enhanced diagnostics in accordance with established practices, offering a promising avenue for developing trustworthy clinical decision-support systems.

Reorganization of integration and segregation networks in brain-based visual impairment

Growing evidence suggests that cerebral connectivity changes its network organization by altering modular topology in response to developmental and environmental experience. However, changes in cerebral connectivity associated with visual impairment due to early neurological injury are still not fully understood. Cerebral visual impairment (CVI) is a brain-based visual disorder associated with damage and maldevelopment of retrochiasmal pathways and areas implicated in visual processing. In this study, we used a multimodal imaging approach and connectomic analyses based on structural (voxel-based morphometry; VBM) and resting state functional connectivity (rsfc) to investigate differences in weighted degree and link-level connectivity in individuals with CVI compared to controls with neurotypical development. We found that participants with CVI showed significantly reduced grey matter volume within the primary visual cortex and intraparietal sulcus (IPS) compared to controls. Participants with CVI also exhibited marked reorganization characterized by increased integration of visual connectivity to somatosensory and multimodal integration areas (dorsal and ventral attention regions) and lower connectivity from visual to limbic and default mode networks. Link-level functional changes in CVI were also associated with key clinical outcomes related to visual function and development. These findings provide early insight into how visual impairment related to early brain injury distinctly reorganizes the functional network architecture of the human brain.

Regional brain structural network topology mediates the associations between white matter damage and disease severity in first-episode, Treatment-naïve pubertal children with major depressive disorder

Puberty is a vulnerable period for the onset of major depressive disorder (MDD) due to considerable neurodevelopmental changes. Prior diffusion tensor imaging (DTI) studies in depressed youth have had heterogeneous participants, making assessment of early pathology challenging due to illness chronicity and medication confounds. This study leveraged whole-brain DTI and graph theory approaches to probe white matter (WM) abnormalities and disturbances in structural network topology related to first-episode, treatment-naïve pediatric MDD. Participants included 36 first-episode, unmedicated adolescents with MDD (mean age 15.8 years) and 29 age- and sex-matched healthy controls (mean age 15.2 years). Compared to controls, the MDD group showed reduced fractional anisotropy in the internal and external capsules, unveiling novel regions of WM disruption in early-onset depression. The right thalamus and superior temporal gyrus were identified as network hubs where betweenness centrality changes mediated links between WM anomalies and depression severity. A diagnostic model incorporating demographics, DTI, and network metrics achieved an AUROC of 0.88 and a F1 score of 0.80 using a neural network algorithm. By examining first-episode, treatment-naïve patients, this work identified novel WM abnormalities and a potential causal pathway linking WM damage to symptom severity via regional structural network alterations in brain hubs.

Alterations in spatiotemporal characteristics of dynamic networks in juvenile myoclonic epilepsy

BACKGROUND

Juvenile myoclonic epilepsy (JME) is characterized by altered patterns of brain functional connectivity (FC). However, the nature and extent of alterations in the spatiotemporal characteristics of dynamic FC in JME patients remain elusive. Dynamic networks effectively encapsulate temporal variations in brain imaging data, offering insights into brain network abnormalities and contributing to our understanding of the seizure mechanisms and origins.

METHODS

Resting-state functional magnetic resonance imaging (rs-fMRI) data were procured from 37 JME patients and 37 healthy counterparts. Forty-seven network nodes were identified by group-independent component analysis (ICA) to construct the dynamic network. Ultimately, patients' and controls' spatiotemporal characteristics, encompassing temporal clustering and variability, were contrasted at the whole-brain, large-scale network, and regional levels.

RESULTS

Our findings reveal a marked reduction in temporal clustering and an elevation in temporal variability in JME patients at the whole-brain echelon. Perturbations were notably pronounced in the default mode network (DMN) and visual network (VN) at the large-scale level. Nodes exhibiting anomalous were predominantly situated within the DMN and VN. Additionally, there was a significant correlation between the severity of JME symptoms and the temporal clustering of the VN.

CONCLUSIONS

Our findings suggest that excessive temporal changes in brain FC may affect the temporal structure of dynamic brain networks, leading to disturbances in brain function in patients with JME. The DMN and VN play an important role in the dynamics of brain networks in patients, and their abnormal spatiotemporal properties may underlie abnormal brain function in patients with JME in the early stages of the disease.

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.

Volume-based structural connectome of epilepsy partialis continua in Rasmussen's encephalitis

Rasmussen's encephalitis is a rare, progressive neurological inflammatory with hemispheric brain atrophy. Epilepsy partialis continua (EPC) is a diagnostic clinical condition in patients with Rasmussen's encephalitis. However, the incidence of EPC in the natural course of Rasmussen's encephalitis is only about 50%. The majority of experts hold the belief that EPC is associated with dysfunction in the motor cortex, yet the whole pathogenesis remains unclear. We hypothesize that there is a characteristic topological discrepancy between groups with EPC and without EPC from the perspective of structural connectome. To this end, we described the structural MRI findings of 20 Rasmussen's encephalitis cases, 11 of which had EPC, and 9 of which did not have EPC (NEPC), and 20 healthy controls. We performed voxel-based morphometry to evaluate the alterations of grey matter volume. Using a volume-based structural covariant network, the hub distribution and modularity were studied at the group level. Based on the radiomic features, an individual radiomics structural similarity network was constructed for global topological properties, such as small-world index, higher path length, and clustering coefficient. And then, the Pearson correlation was used to delineate the association between duration and topology properties. In the both EPC and NEPC groups, the volume of the motor cortex on the affected side was significantly decreased, but putamen atrophy was most pronounced in the EPC group. Hubs in the EPC group consisted of the executive network, and the contralateral putamen was the hub in the NEPC group with the highest betweenness centrality. Compared to the NEPC, the EPC showed a higher path length and clustering coefficient in the structural similarity network. Moreover, the function of morphological network integration in EPC patients was diminished as the duration of Rasmussen's encephalitis increased. Our study indicates that motor cortex atrophy may not be directly related to EPC patients. Whereas atrophy of the putamen, and a more regularized configuration may contribute to the generation of EPC. The findings further suggest that the putamen could potentially serve as a viable target for controlling EPC in patients with Rasmussen's encephalitis.

Fingerprints of brain disease: connectome identifiability in Alzheimer's disease

Functional connectivity patterns in the human brain, like the friction ridges of a fingerprint, can uniquely identify individuals. Does this "brain fingerprint" remain distinct even during Alzheimer's disease (AD)? Using fMRI data from healthy and pathologically ageing subjects, we find that individual functional connectivity profiles remain unique and highly heterogeneous during mild cognitive impairment and AD. However, the patterns that make individuals identifiable change with disease progression, revealing a reconfiguration of the brain fingerprint. Notably, connectivity shifts towards functional system connections in AD and lower-order cognitive functions in early disease stages. These findings emphasize the importance of focusing on individual variability rather than group differences in AD studies. Individual functional connectomes could be instrumental in creating personalized models of AD progression, predicting disease course, and optimizing treatments, paving the way for personalized medicine in AD management.

Group-common and individual-specific effects of structure-function coupling in human brain networks with graph neural networks

The human cerebral cortex is organized into functionally segregated but synchronized regions bridged by the structural connectivity of white matter pathways. While structure-function coupling has been implicated in cognitive development and neuropsychiatric disorders, it remains unclear to what extent the structure-function coupling reflects a group-common characteristic or varies across individuals, at both the global and regional brain levels. By leveraging two independent, high-quality datasets, we found that the graph neural network accurately predicted unseen individuals' functional connectivity from structural connectivity, reflecting a strong structure-function coupling. This coupling was primarily driven by network topology and was substantially stronger than that of the correlation approaches. Moreover, we observed that structure-function coupling was dominated by group-common effects, with subtle yet significant individual-specific effects. The regional group and individual effects of coupling were hierarchically organized across the cortex along a sensorimotor-association axis, with lower group and higher individual effects in association cortices. These findings emphasize the importance of considering both group and individual effects in understanding cortical structure-function coupling, suggesting insights into interpreting individual differences of the coupling and informing connectivity-guided therapeutics.

Hierarchical communities in the larval Drosophila connectome: Links to cellular annotations and network topology

One of the longstanding aims of network neuroscience is to link a connectome's topological properties-i.e., features defined from connectivity alone-with an organism's neurobiology. One approach for doing so is to compare connectome properties with annotational maps. This type of analysis is popular at the meso-/macroscale, but is less common at the nano-scale, owing to a paucity of neuron-level connectome data. However, recent methodological advances have made possible the reconstruction of whole-brain connectomes at single-neuron resolution for a select set of organisms. These include the fruit fly, Drosophila melanogaster, and its developing larvae. In addition to fine-scale descriptions of connectivity, these datasets are accompanied by rich annotations. Here, we use a variant of the stochastic blockmodel to detect multilevel communities in the larval Drosophila connectome. We find that communities partition neurons based on function and cell type and that most interact assortatively, reflecting the principle of functional segregation. However, a small number of communities interact nonassortatively, forming form a "rich-club" of interneurons that receive sensory/ascending inputs and deliver outputs along descending pathways. Next, we investigate the role of community structure in shaping communication patterns. We find that polysynaptic signaling follows specific trajectories across modular hierarchies, with interneurons playing a key role in mediating communication routes between modules and hierarchical scales. Our work suggests a relationship between system-level architecture and the biological function and classification of individual neurons. We envision our study as an important step toward bridging the gap between complex systems and neurobiological lines of investigation in brain sciences.

The cognitive triad network - oscillation - behaviour links individual differences in EEG theta frequency with task performance and effective connectivity

We reconcile two significant lines of Cognitive Neuroscience research: the relationship between the structural and functional architecture of the brain and behaviour on the one hand and the functional significance of oscillatory brain processes to behavioural performance on the other. Network neuroscience proposes that the three elements, behavioural performance, EEG oscillation frequency, and network connectivity should be tightly connected at the individual level. Young and old healthy adults were recruited as a proxy for performance variation. An auditory inhibitory control task was used to demonstrate that task performance correlates with the individual EEG frontal theta frequency. Older adults had a significantly slower theta frequency, and both theta frequency and task performance correlated with the strengths of two network connections that involve the main areas of inhibitory control and speech processing. The results suggest that both the recruited functional network and the oscillation frequency induced by the task are specific to the task, are inseparable, and mark individual differences that directly link structure and function to behaviour in health and disease.

Happy children! A network of psychological and environmental factors associated with the development of positive affect in 9-13 children

To deepen the development of positive affect during early adolescence and shed new light on its predictors, this study adopts an exploratory network approach to first identify the main domains that describe the variability of children's psychological, environmental, and behavioral characteristics, and then use these domains to longitudinally predict positive affect and its development within a latent growth framework. To this aim, we considered 10,904 US participants (9 years old at baseline; 13 years old 42 months later), six measurement occasions of positive affect, and 46 baseline indicators from the ABCD study. Our results not only confirm that positive affect declines between 9 and 13 years old, but also show that among the five domains identified (behavioral dysregulation, cognitive functioning, psychological problems, supportive social environment, and extracurricular activities), only a supportive social environment consistently predicts positive affect. This is crucial for practitioners and policymakers, as it can help them focus on the elements within our complex network of psychological, social, and environmental variability.

Socioeconomic resources in youth are linked to divergent patterns of network integration/segregation across the brain's transmodal axis

Socioeconomic resources (SER) calibrate the developing brain to the current context, which can confer or attenuate risk for psychopathology across the lifespan. Recent multivariate work indicates that SER levels powerfully relate to intrinsic functional connectivity patterns across the entire brain. Nevertheless, the neuroscientific meaning of these widespread neural differences remains poorly understood, despite its translational promise for early risk identification, targeted intervention, and policy reform. In the present study, we leverage graph theory to precisely characterize multivariate and univariate associations between SER across household and neighborhood contexts and the intrinsic functional architecture of brain regions in 5,821 youth (9-10 years) from the Adolescent Brain Cognitive Development Study. First, we establish that decomposing the brain into profiles of integration and segregation captures more than half of the multivariate association between SER and functional connectivity with greater parsimony (100-fold reduction in number of features) and interpretability. Second, we show that the topological effects of SER are not uniform across the brain; rather, higher SER levels are associated with greater integration of somatomotor and subcortical systems, but greater segregation of default mode, orbitofrontal, and cerebellar systems. Finally, we demonstrate that topological associations with SER are spatially patterned along the unimodal-transmodal gradient of brain organization. These findings provide critical interpretive context for the established and widespread associations between SER and brain organization. This study highlights both higher-order and somatomotor networks that are differentially implicated in environmental stress, disadvantage, and opportunity in youth.

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.

Siamese Graph Convolutional Network quantifies increasing structure-function discrepancy over the cognitive decline continuum

BACKGROUND AND OBJECTIVE

Alzheimer's disease dementia (ADD) is well known to induce alterations in both structural and functional brain connectivity. However, reported changes in connectivity are mostly limited to global/local network features, which have poor specificity for diagnostic purposes. Following recent advances in machine learning, deep neural networks, particularly Graph Neural Network (GNN) based approaches, have found applications in brain research as well. The majority of existing applications of GNNs employ a single network (uni-modal or structure/function unified), despite the widely accepted view that there is a nontrivial interdependence between the brain's structural connectivity and the neural activity patterns, which is hypothesized to be disrupted in ADD. This disruption is quantified as a discrepancy score by the proposed "structure-function discrepancy learning network" (sfDLN) and its distribution is studied over the spectrum of clinical cognitive decline. The measured discrepancy score is utilized as a diagnostic biomarker and is compared with state-of-the-art diagnostic classifiers.

METHODS

sfDLN is a GNN with a siamese architecture built on the hypothesis that the mismatch between structural and functional connectivity patterns increases over the cognitive decline spectrum, starting from subjective cognitive impairment (SCI), passing through a mid-stage mild cognitive impairment (MCI), and ending up with ADD. The structural brain connectome (sNET) built using diffusion MRI-based tractography and the novel, sparse (lean) functional brain connectome (ℓNET) built using fMRI are input to sfDLN. The siamese sfDLN is trained to extract connectome representations and a discrepancy (dissimilarity) score that complies with the proposed hypothesis and is blindly tested on an MCI group.

RESULTS

The sfDLN generated structure-function discrepancy scores show high disparity between ADD and SCI subjects. Leave-one-out experiments of SCI-ADD classification over a cohort of 42 subjects reach 88% accuracy, surpassing state-of-the-art GNN-based classifiers in the literature. Furthermore, a blind assessment over a cohort of 46 MCI subjects confirmed that it captures the intermediary character of the MCI group. GNNExplainer module employed to investigate the anatomical determinants of the observed discrepancy confirms that sfDLN attends to cortical regions neurologically relevant to ADD.

CONCLUSION

In support of our hypothesis, the harmony between the structural and functional organization of the brain degrades with increasing cognitive decline. This discrepancy, shown to be rooted in brain regions neurologically relevant to ADD, can be quantified by sfDLN and outperforms state-of-the-art GNN-based ADD classification methods when used as a biomarker.