Brain Network Topology Research Articles

Decreased Local Specialization of Brain Structural Networks Associated with Cognitive Dysfuntion Revealed by Probabilistic Diffusion Tractography for Different Cerebral Small Vessel Disease Burdens

To reveal the network-level structural disruptions associated with cognitive dysfunctions in different cerebral small vessel disease (CSVD) burdens, we used probabilistic diffusion tractography and graph theory to investigate the brain network topology in 67 patients with a severe CSVD burden (CSVD-s), 133 patients with a mild CSVD burden (CSVD-m) and 89 healthy controls. We used one-way analysis of covariance to assess the altered topological measures between groups, and then evaluated their Pearson correlation with cognitive parameters. Both the CSVD and control groups showed efficient small-world organization in white matter (WM) networks. However, compared with CSVD-m patients and controls, CSVD-s patients exhibited significantly decreased local efficiency, with partially reorganized hub distributions. For regional topology, CSVD-s patients showed significantly decreased nodal efficiency in the bilateral anterior cingulate gyrus, caudate nucleus, right opercular inferior frontal gyrus (IFGoperc), supplementary motor area (SMA), insula and left orbital superior frontal gyrus and angular gyrus. Intriguingly, global/local efficiency and nodal efficiency of the bilateral caudate nucleus, right IFGoperc, SMA and left angular gyrus showed significant correlations with cognitive parameters in the CSVD-s group, while only the left pallidum showed significant correlations with cognitive metrics in the CSVD-m group. In conclusion, the decreased local specialization of brain structural networks in patients with different CSVD burdens provides novel insights into understanding the brain structural alterations in relation to CSVD severity. Cognitive correlations with brain structural network efficiency suggest their potential use as neuroimaging biomarkers to assess the severity of CSVD.

Resilient functioning is associated with altered structural brain network topology in adolescents exposed to childhood adversity.

Childhood adversity is one of the strongest predictors of adolescent mental illness. Therefore, it is critical that the mechanisms that aid resilient functioning in individuals exposed to childhood adversity are better understood. Here, we examined whether resilient functioning was related to structural brain network topology. We quantified resilient functioning at the individual level as psychosocial functioning adjusted for the severity of childhood adversity in a large sample of adolescents (N = 2406, aged 14-24). Next, we examined nodal degree (the number of connections that brain regions have in a network) using brain-wide cortical thickness measures in a representative subset (N = 275) using a sliding window approach. We found that higher resilient functioning was associated with lower nodal degree of multiple regions including the dorsolateral prefrontal cortex, the medial prefrontal cortex, and the posterior superior temporal sulcus (z > 1.645). During adolescence, decreases in nodal degree are thought to reflect a normative developmental process that is part of the extensive remodeling of structural brain network topology. Prior findings in this sample showed that decreased nodal degree was associated with age, as such our findings of negative associations between nodal degree and resilient functioning may therefore potentially resemble a more mature structural network configuration in individuals with higher resilient functioning.

Brain network characteristics between subacute and chronic stroke survivors in active, imagery, passive movement task: a pilot study.

The activation patterns and functional network characteristics between stroke survivors and healthy individuals based on resting-or task-state neuroimaging and neurophysiological techniques have been extensively explored. However, the discrepancy between stroke patients at different recovery stages remains unclear. To investigate the changes in brain connectivity and network topology between subacute and chronic patients, and hope to provide a basis for rehabilitation strategies at different stages after stroke. Fifteen stroke survivors were assigned to the subacute group (SG, N = 9) and chronic group (CG, N = 6). They were asked to perform hand grasping under active, passive, and MI conditions when recording EEG. The Fugl-Meyer Assessment Upper Extremity subscale (FMA_UE), modified Ashworth Scale (MAS), Manual Muscle Test (MMT), grip and pinch strength, modified Barthel Index (MBI), and Berg Balance Scale (BBS) were measured. Functional connectivity analyses showed significant interactions on frontal, parietal and occipital lobes connections in each frequency band, particularly in the delta band. The coupling strength of premotor cortex, M1, S1 and several connections linked to frontal, parietal, and occipital lobes in subacute subjects were lower than in chronic subjects in low alpha, high alpha, low beta, and high beta bands. Nodal clustering coefficient (CC) analyses revealed that the CC in chronic subjects was higher than in subacute subjects in the ipsilesional S1 and occipital area, contralesional dorsolateral prefrontal cortex and parietal area. Characteristic path length (CPL) analyses showed that CPL in subacute subjects was lower than in chronic subjects in low beta, high beta, and gamma bands. There were no significant differences between subacute and chronic subjects for small-world property. Subacute stroke survivors were characterized by higher transfer efficiency of the entire brain network and weak local nodal effects. Transfer efficiency was reduced, the local nodal role was strengthened, and more neural resources needed to be mobilized to perform motor tasks for chronic survivors. Overall, these results may help to understand the remodeling pattern of the brain network for different post-stroke stages on task conditions and the mechanism of spontaneous recovery.

Default Mode Network Hypoalignment of Function to Structure Correlates With Depression and Rumination

BackgroundRecent studies have begun to examine how signals in the brain correspond to the underlying white matter structure using tools from the field of graph signal processing to quantify brain function alignment to brain network topology. Here, we applied this framework for the first time toward a transdiagnostic cohort of individuals with internalizing psychopathologies, including mood and anxiety disorders, to uncover how such alignment within the default mode network (DMN) is related to depression and rumination symptoms. MethodsBoth diffusion-weighted and resting-state functional magnetic resonance imaging were obtained from participants at baseline (n = 60 patients, n = 19 healthy control participants). Patients were randomized to 12 weeks of treatment with either a selective serotonin reuptake inhibitor or cognitive behavioral therapy, and symptom scales were readministered posttreatment (n = 46 patients at follow-up). Using graph signal processing methodology, we quantified the alignment of functional signals to their underlying white matter structural networks. ResultsWe found that signal alignment within the posterior DMN was decreased in patients with internalizing psychopathologies compared with healthy control participants and was inversely (negatively) correlated with baseline depression and rumination scales. Signal alignment within the posterior DMN was also correlated with the ratio of total within-DMN to extra-DMN functional connectivity for these regions. ConclusionsThese findings are consistent with previous literature regarding pathological promiscuity of posterior DMN connectivity and provide the first graph signal processing–based analyses in a transdiagnostic cohort of patients with internalizing psychopathologies.

The robustness of persistent homology of brain networks to data acquisition-related non-neural variability in resting state fMRI.

There is increasing interest in investigating brain function based on functional connectivity networks (FCN) obtained from resting-state functional magnetic resonance imaging (fMRI). FCNs, typically obtained using measures of time series association such as Pearson's correlation, are sensitive to data acquisition parameters such as sampling period. This introduces non-neural variability in data pooled from different acquisition protocols and MRI scanners, negating the advantages of larger sample sizes in pooled data. To address this, we hypothesize that the topology or shape of brain networks must be preserved irrespective of how densely it is sampled, and metrics which capture this topology may be statistically similar across sampling periods, thereby alleviating this source of non-neural variability. Accordingly, we present an end-to-end pipeline that uses persistent homology (PH), a branch of topological data analysis, to demonstrate similarity across FCNs acquired at different temporal sampling periods. PH, as a technique, extracts topological features by capturing the network organization across all continuous threshold values, as opposed to graph theoretic methods, which fix a discrete network topology by thresholding the connectivity matrix. The extracted topological features are encoded in the form of persistent diagrams that can be compared against one another using the earth-moving metric, also popularly known as the Wasserstein distance. We extract topological features from three data cohorts, each acquired at different temporal sampling periods and demonstrate that these features are statistically the same, hence, empirically showing that PH may be robust to changes in data acquisition parameters such as sampling period.

Altered topology of individual brain structural covariance networks in major depressive disorder.

The neurobiological pathogenesis of major depression disorder (MDD) remains largely controversial. Previous literatures with limited sample size utilizing group-level structural covariance networks (SCN) commonly generated mixed findings regarding the topology of brain networks. We analyzed T1 images from a high-powered multisite sample including 1173 patients with MDD and 1019 healthy controls (HCs). We used regional gray matter volume to construct individual SCN by utilizing a novel approach based on the interregional effect size difference. We further investigated MDD-related structural connectivity alterations using topological metrics. Compared to HCs, the MDD patients showed a shift toward randomization characterized by increased integration. Further subgroup analysis of patients in different stages revealed this randomization pattern was also observed in patients with recurrent MDD, while the first-episode drug naïve patients exhibited decreased segregation. Altered nodal properties in several brain regions which have a key role in both emotion regulation and executive control were also found in MDD patients compared with HCs. The abnormalities in inferior temporal gyrus were not influenced by any specific site. Moreover, antidepressants increased nodal efficiency in the anterior ventromedial prefrontal cortex. The MDD patients at different stages exhibit distinct patterns of randomization in their brain networks, with increased integration during illness progression. These findings provide valuable insights into the disruption in structural brain networks that occurs in patients with MDD and might be useful to guide future therapeutic interventions.

LOCUS: A REGULARIZED BLIND SOURCE SEPARATION METHOD WITH LOW-RANK STRUCTURE FOR INVESTIGATING BRAIN CONNECTIVITY.

Network-oriented research has been increasingly popular in many scientific areas. In neuroscience research, imaging-based network connectivity measures have become the key for understanding brain organizations, potentially serving as individual neural fingerprints. There are major challenges in analyzing connectivity matrices, including the high dimensionality of brain networks, unknown latent sources underlying the observed connectivity, and the large number of brain connections leading to spurious findings. In this paper we propose a novel blind source separation method with low-rank structure and uniform sparsity (LOCUS) as a fully data-driven decomposition method for network measures. Compared with the existing method that vectorizes connectivity matrices ignoring brain network topology, LOCUS achieves more efficient and accurate source separation for connectivity matrices using low-rank structure. We propose a novel angle-based uniform sparsity regularization that demonstrates better performance than the existing sparsity controls for low-rank tensor methods. We propose a highly efficient iterative node-rotation algorithm that exploits the block multiconvexity of the objective function to solve the nonconvex optimization problem for learning LOCUS. We illustrate the advantage of LOCUS through extensive simulation studies. Application of LOCUS to Philadelphia Neurodevelopmental Cohort neuroimaging study reveals biologically insightful connectivity traits which are not found using the existing method.

Global excitability and network structure in the human brain.

We utilize a model of Wilson-Cowan oscillators to investigate structure-function relationships in the human brain by means of simulations of the spontaneous dynamics of brain networks generated through human connectome data. This allows us to establish relationships between the global excitability of such networks and global structural network quantities for connectomes of two different sizes for a number of individual subjects. We compare the qualitative behavior of such correlations between biological networks and shuffled networks, the latter generated by shuffling the pairwise connectivities of the former while preserving their distribution. Our results point towards a remarkable propensity of the brain to achieve a trade-off between low network wiring cost and strong functionality, and highlight the unique capacity of brain network topologies to exhibit a strong transition from an inactive state to a globally excited one.

From nose to brain: The effect of lemon inhalation observed by whole brain voxel to voxel functional connectivity

Lemon fragrance is known for its stimulating properties, but its mechanisms of action are not well known yet. This study aimed to examine the effect of lemon essential oil inhalation on healthy participants’ alertness level and their neural correlates using magnetic resonance imaging (MRI). Twenty-one healthy men underwent functional MRI scans in different conditions: a resting state condition, a condition where they were exposed to passive lemon smelling (alternating exposure to lemon and breathing fresh air), and a control condition without lemon fragrance diffusion –the order of the last two conditions being randomized. Alertness levels were assessed immediately after each condition using the Karolinska Sleepiness Scale. Voxel-wise whole-brain global functional connectivity and graph theory analyses were computed to investigate brain functional connectivity and network topology alterations. After lemon fragrance inhalation, we observed a higher level of alertness as compared to resting state –but not compared to control condition. During lemon fragrance inhalation, we found increased global functional connectivity in the thalamus, paralleled by decreased global connectivity in several cortical regions such as precuneus, postcentral and precentral gyrus, lateral occipital cortex and paracingulate gyrus. Graph theory analysis revealed increased network integration in cortical regions typically involved in olfaction and emotion processing such as olfactory bulb, hypothalamus and thalamus, while decreased network segregation in several regions of the posterior part of the brain during olfaction as compared to resting state. The present findings suggest that lemon essential oil inhalation could increase the level of alertness.

Brain Network Topology in Deficit and Non-Deficit Schizophrenia: Application of Graph Theory to Local and Global Indices.

Patients with deficit schizophrenia (SZD) suffer from primary and enduring negative symptoms. Limited pieces of evidence and neuroimaging studies indicate they differ from patients with non-deficit schizophrenia (SZND) in neurobiological aspects, but the results are far from conclusive. We applied for the first time, graph theory analyses to discriminate local and global indices of brain network topology in SZD and SZND patients compared with healthy controls (HC). High-resolution T1-weighted images were acquired for 21 SZD patients, 21 SZND patients, and 21 HC to measure cortical thickness from 68 brain regions. Graph-based metrics (i.e., centrality, segregation, and integration) were computed and compared among groups, at both global and regional networks. When compared to HC, at the regional level, SZND were characterized by temporoparietal segregation and integration differences, while SZD showed widespread alterations in all network measures. SZD also showed less segregated network topology at the global level in comparison to HC. SZD and SZND differed in terms of centrality and integration measures in nodes belonging to the left temporoparietal cortex and to the limbic system. SZD is characterized by topological features in the network architecture of brain regions involved in negative symptomatology. Such results help to better define the neurobiology of SZD (SZD: Deficit Schizophrenia; SZND: Non-Deficit Schizophrenia; SZ: Schizophrenia; HC: healthy controls; CC: clustering coefficient; L: characteristic path length; E: efficiency; D: degree; CCnode: CC of a node; CCglob: the global CC of the network; Eloc: efficiency of the information transfer flow either within segregated subgraphs or neighborhoods nodes; Eglob: efficiency of the information transfer flow among the global network; FDA: Functional Data Analysis; and Dmin: estimated minimum densities).

Exploring neural heterogeneity in inattention and hyperactivity

Inattention and hyperactivity are cardinal symptoms of Attention Deficit Hyperactivity Disorder (ADHD). These characteristics have also been observed across a range of other neurodevelopmental conditions, such as autism and dyspraxia, suggesting that they might best be studied across diagnostic categories. Here, we evaluated the associations between inattention and hyperactivity behaviours and features of the structural brain network (connectome) in a large transdiagnostic sample of children (Centre for Attention, Learning, and Memory; n = 383). In our sample, we found that a single latent factor explains 77.6% of variance in scores across multiple questionnaires measuring inattention and hyperactivity. Partial Least-Squares (PLS) regression revealed that variability in this latent factor could not be explained by a linear component representing nodewise properties of connectomes. We then investigated the type and extent of neural heterogeneity in a subset of our sample with clinically-elevated levels of inattention and hyperactivity. Multidimensional scaling combined with k-means clustering revealed two neural subtypes in children with elevated levels of inattention and hyperactivity (n = 232), differentiated primarily by nodal communicability—a measure which demarcates the extent to which neural signals propagate through specific brain regions. These different clusters had similar behavioural profiles, which included high levels of inattention and hyperactivity. However, one of the clusters scored higher on multiple cognitive assessment measures of executive function. We conclude that inattention and hyperactivity are so common in children with neurodevelopmental difficulties because they emerge through multiple different trajectories of brain development. In our own data, we can identify two of these possible trajectories, which are reflected by measures of structural brain network topology and cognition.

Desire and cravingmeasured using behavioral ratings and brain network topology differ significantly among moderate to heavy alcohol consumers.

"Craving" is a central concept in alcohol research, but the semantic interpretation of craving as a concept varies. Multiple studies that have investigated differences in operational definitions of craving have demonstrated a lack of agreement among them. This study investigated whether moderate to heavy drinkers would rate craving and "desire" for alcohol similarly and explored potential neurobiological differences underpinning feelings of craving and desire. Thirty-nine individuals who consumed an average of at least 7 drinks/week for females and 14 drinks/week for males were studied across 3-day periods of their typical alcohol consumption and imposed abstinence. Ratings of desire and craving for alcohol were collected approximately every three hours during waking periods across the two experimental periods (n = 35, 17 males). At the end of each period, participants underwent functional MRI scanning during neutral and alcohol image viewing (n = 39, 17 males) followed by ratings of desire and craving for alcohol (n = 32, 16 males). Survey responses were analyzed using 2-level nested hierarchical modeling, image ratings were compared using a hierarchical mixed-effects regression, and brain networks constructed from fMRI data were assessed with a two-part mixed-effect regression (α = 0.05 in all analyses). Ratings of desire and craving differed significantly from one another in the survey data and in the ratings collected during image viewing. The strength of the desire experience was higher overall than craving, but the fluctuations over time were similar. Results for desire and craving differed on brain network attributes associated with distributed processing and those regional specific within the default mode network. Significant associations were found between ratings of desire and connection strength and between ratings of craving and connection probability. These results demonstrate that the difference between ratings of craving for alcohol and desire for alcohol is not trivial. The different ratings and their association with alcohol consumption or abstinence experiences may have significant biological and clinical implications.

Local Brain Network Alterations and Olfactory Impairment in Alzheimer’s Disease: An fMRI and Graph-Based Study

Alzheimer's disease (AD) is associated with the abnormal connection of functional networks. Olfactory impairment occurs in early AD; therefore, exploring alterations in olfactory-related regions is useful for early AD diagnosis. We combined the graph theory of local brain network topology with olfactory performance to analyze the differences in AD brain network characteristics. A total of 23 patients with AD and 18 normal controls were recruited for resting-state functional magnetic resonance imaging (fMRI), clinical neuropsychological examinations and the University of Pennsylvania Smell Identification Test (UPSIT). Between-group differences in the topological properties of the local network were compared. Pearson correlations were explored based on differential brain regions and olfactory performance. Statistical analysis revealed a correlation of the degree of cognitive impairment with olfactory recognition function. Local node topological properties were significantly altered in many local brain regions in the AD group. The nodal clustering coefficients of the bilateral temporal pole: middle temporal gyrus (TPOmid), degree centrality of the left insula (INS.L), degree centrality of the right middle temporal gyrus (MTG.R), and betweenness centrality of the left middle temporal gyrus (MTG.L) were related to olfactory performance. Alterations in local topological properties combined with the olfactory impairment can allow early identification of abnormal olfactory-related regions, facilitating early AD screening.

A Graph Theory Study of Resting-State Functional MRI Connectivity in Children With Carbon Monoxide Poisoning.

The effect of carbon monoxide (CO) poisoning on the topology of brain functional networks is unclear, especially in children whose brains are still developing. To investigate the topological alterations of the whole-brain functional connectome in children with CO poisoning and characterize its relationship with disease severity. Cross-sectional and prospective study. A total of 26 patients with CO poisoning and 26 healthy controls. A 3.0 T MRI system/echo planar imaging (EPI) and 3D brain volume imaging (BRAVO) sequences. We used the network-based statistics (NBS) method to explore between-group differences in functional connectivity strength and a graph-theoretical-based analytic method to explore the topology of brain networks. Student's t-test, chi-square test, NBS, Pearson correlation coefficient, and false discovery rate correction. The statistical significance threshold was set at P < 0.05. The case group's brain functional network topology was impaired in comparison to the control group (reduced global efficiency and small-worldness, increased characteristic path length). According to node and edge analyses, the case group showed topologically damaged regions in the frontal lobe and basal ganglia, as well as neuronal circuits with weaker connections. Also, there was a significant correlation between the patients' coma time and the degree (r = -0.4564), efficiency (r = -0.4625), and characteristic path length (r = 0.4383) of the nodes in the left orbital inferior frontal gyrus. Carbon monoxide hemoglobin content (COHb) concentration and right rolandic operculum node characteristic path length (r = -0.3894) were significantly correlated. The node efficiency and node degree of the right middle frontal gyrus (r = 0.4447 and 0.4539) and right pallidum (r = 0.4136 and 0.4501) significantly correlated with the MMSE score. The brain network topology of CO poisoned children is damaged, which is manifested by reduced network integration and may lead to a series of clinical symptoms in patients. 2. Stage 2.

The distribution and heterogeneity of excitability in focal epileptic network potentially contribute to the seizure propagation.

Existing dynamical models can explain the transmigration mechanisms involved in seizures but are limited to a single modality. Combining models with networks can reproduce scaled epileptic dynamics. And the structure and coupling interactions of the network, as well as the heterogeneity of both the node and network activities, may influence the final state of the network model. We built a fully connected network with focal nodes prominently interacting and established a timescale separated epileptic network model. The factors affecting epileptic network seizure were explored by varying the connectivity patterns of focal network nodes and modulating the distribution of network excitability. The whole brain network topology as the brain activity foundation affects the consistent delayed clustering seizure propagation. In addition, the network size and distribution heterogeneity of the focal excitatory nodes can influence seizure frequency. With the increasing of the network size and averaged excitability level of focal network, the seizure period decreases. In contrast, the larger heterogeneity of excitability for focal network nodes can lower the functional activity level (average degree) of focal network. There are also subtle effects of focal network topologies (connection patterns of excitatory nodes) that cannot be ignored along with non-focal nodes. Unraveling the role of excitatory factors in seizure onset and propagation can be used to understand the dynamic mechanisms and neuromodulation of epilepsy, with profound implications for the treatment of epilepsy and even for the understanding of the brain.

Age and sex differentially shape brain networks in Parkinson's disease.

Age and sex are important individual factors modifying the clinical symptoms of patients with Parkinson's disease (PD). Our goal is to evaluate the effects of age and sex on brain networks and clinical manifestations of PD patients. Parkinson's disease participants (n=198) receiving functional magnetic resonance imaging from Parkinson's Progression Markers Initiative database were investigated. Participants were classified into lower quartile group (age rank: 0%~25%), interquartile group (age rank: 26%~75%), and upper quartile group (age rank: 76%~100%) according to their age quartiles to examine how age shapes brain network topology. The differences of brain network topological properties between male and female participants were also investigated. Parkinson's disease patients in the upper quartile age group exhibited disrupted network topology of white matter networks and impaired integrity of white matter fibers compared to lower quartile age group. In contrast, sex preferentially shaped the small-world topology of gray matter covariance network. Differential network metrics mediated the effects of age and sex on cognitive function of PD patients. Age and sex have diverse effects on brain structural networks and cognitive function of PD patients, highlighting their roles in the clinical management of PD.

Genetic Underpinnings of Brain Structural Connectome for Young Adults

With distinct advantages in power over behavioral phenotypes, brain imaging traits have become emerging endophenotypes to dissect molecular contributions to behaviors and neuropsychiatric illnesses. Among different imaging features, brain structural connectivity (i.e., structural connectome) which summarizes the anatomical connections between different brain regions is one of the most cutting edge while under-investigated traits; and the genetic influence on the structural connectome variation remains highly elusive. Relying on a landmark imaging genetics study for young adults, we develop a biologically plausible brain network response shrinkage model to comprehensively characterize the relationship between high dimensional genetic variants and the structural connectome phenotype. Under a unified Bayesian framework, we accommodate the topology of brain network and biological architecture within the genome; and eventually establish a mechanistic mapping between genetic biomarkers and the associated brain sub-network units. An efficient expectation-maximization algorithm is developed to estimate the model and ensure computing feasibility. In the application to the Human Connectome Project Young Adult (HCP-YA) data, we establish the genetic underpinnings which are highly interpretable under functional annotation and brain tissue eQTL analysis, for the brain white matter tracts connecting the hippocampus and two cerebral hemispheres. We also show the superiority of our method in extensive simulations. Supplementary materials for this article are available online.

Motor imagery classification method based on relative wavelet packet entropy brain network and improved lasso.

Motor imagery (MI) electroencephalogram (EEG) signals have a low signal-to-noise ratio, which brings challenges in feature extraction and feature selection with high classification accuracy. In this study, we proposed an approach that combined an improved lasso with relief-f to extract the wavelet packet entropy features and the topological features of the brain function network. For signal denoising and channel filtering, raw MI EEG was filtered based on an R2 map, and then the wavelet soft threshold and one-to-one multi-class score common spatial pattern algorithms were used. Subsequently, the relative wavelet packet entropy and corresponding topological features of the brain network were extracted. After feature fusion, mutcorLasso and the relief-f method were applied for feature selection, followed by three classifiers and an ensemble classifier, respectively. The experiments were conducted on two public EEG datasets (BCI Competition III dataset IIIa and BCI Competition IV dataset IIa) to verify this proposed method. The results showed that the brain network topology features and feature selection methods can retain the information of EEG more effectively and reduce the computational complexity, and the average classification accuracy for both public datasets was above 90%; hence, this algorithms is suitable in MI-BCI and has potential applications in rehabilitation and other fields.

Development of the Functional Connectome Topology in Adolescence: Evidence from Topological Data Analysis.

Adolescence is a crucial developmental period in terms of behavior and mental health. Therefore, understanding how the brain develops during this stage is a fundamental challenge for neuroscience. Recent studies have modeled the brain as a network or connectome, mainly applying measures from graph theory, showing a change in its functional organization, such as an increase in its segregation and integration. Topological Data Analysis (TDA) complements such modeling by extracting high-dimensional features across the whole range of connectivity values instead of exploring a fixed set of connections. This study inquires into the developmental trajectories of such properties using a longitudinal sample of typically developing human participants (N = 98; 53/45 female/male; 6.7-18.1 years), applying TDA to their functional connectomes. In addition, we explore the effect of puberty on individual developmental trajectories. Results showed that the adolescent brain has a more distributed topology structure compared with random networks but is more densely connected at the local level. Furthermore, developmental effects showed nonlinear trajectories for the topology of the whole brain and fronto-parietal networks, with an inflection point and increasing trajectories after puberty onset. These results add to the insights into the development of the functional organization of the adolescent brain.

Reduced neural functional connectivity during working memory performance in methamphetamine use disorder

BackgroundMethamphetamine misuse, a surging cause of morbidity and mortality worldwide, identifies Methamphetamine Use Disorder (MUD) as a critical public health problem. Treatment for MUD typically is sought during early abstinence when patients are experiencing cognitive difficulties that may hamper their engagement in treatment and recovery. Cognitive difficulties, particularly those that involve executive functions, likely reflect disruptions in neural functioning involving multiple brain areas and circuits. MethodsTo extend knowledge in this area, we compared individuals with MUD (MUD group, n = 30) in early abstinence (3–11 days abstinent) with a healthy control group (HC, n = 33) on brain activation and network connectivity and topology, using functional magnetic resonance imaging (fMRI) during performance on an N-back working memory task. The N-back task involves the maintenance and manipulation of information in short-term memory and engages multiple neural processes related to executive functioning. The task was administered at two working-memory difficulty loads (1-back and 2-back). ResultsCompared with the HC group, the MUD group had worse task performance but no differences in task-related brain activation. Network-based statistics analyses, however, revealed that the MUD group exhibited less functional network connectivity at both difficulty loads of the N-back task than the HC group. Additional graph theory analyses showed that path lengths were longer, and clustering was lower across these networks, which also exhibited disrupted small-world properties in the MUD group. ConclusionThese results suggest a decoupling in network dynamics that may underlie deficits in cognition during early abstinence in MUD patients.