Assessment of U-Net in the segmentation of short tracts: Transferring to clinical MRI routine.

Accurately studying structural connectivity requires precise tract segmentation strategies. The U-Net network has been widely recognized for its exceptional capacity in image segmentation tasks and provides remarkable results in large tract segmentation when high-quality diffusion-weighted imaging (DWI) data are used. However, short tracts, which are associated with various neurological diseases, pose specific challenges, particularly when high-quality DWI data acquisition within clinical settings is concerned. Here, we aimed to evaluate the U-Net network ability to segment short tracts by using DWI data acquired in different experimental conditions. To this end, we conducted three types of training experiments involving 350 healthy subjects and 11 white matter tracts, including the anterior, posterior, and hippocampal commissure, fornix, and uncinated fasciculus. In the first experiment, the model was exclusively trained with high-quality data of the Human Connectome Project (HCP) dataset. The second experiment focused on images of healthy subjects acquired from a local hospital dataset, representing a typical clinical routine acquisition. In the third experiment, a hybrid training approach was employed, combining data of the HCP and local hospital datasets. Then, the best model was also tested in unseen DWIs of 10 epilepsy patients of the local hospital and 10 healthy subjects acquired on a scanner from another company. The outcomes of the third experiment demonstrated a notable enhancement in performance when contrasted with the preceding trials. Specifically, the short tracts within the local hospital dataset achieved Dice scores ranging between 0.60 and 0.65. Similar intervals were obtained with HCP data in the first experiment, and a substantial improvement compared to the scores between 0.37 and 0.50 obtained with the local hospital dataset at the same experiment. This improvement persisted when the method was applied to diverse scenarios, including different scanner acquisitions and epilepsy patients. These results indicate that combining datasets from different sources, coupled with resolution standardization strengthens the neural network ability to generalize predictions across a spectrum of datasets. Nevertheless, short tract segmentation performance is intricately linked to the training composition, to validation, and to testing data. Moreover, curved tracts have intricate structural nature, which adds complexities to their segmenting. Although the network training approach tested herein has provided promising results, caution must be taken when extrapolating its application to datasets acquired under distinct experimental conditions, even in the case of higher-quality data or analysis of long or short tracts.

Neurocognition and brain functional connectivity in a non-clinical population-based sample with psychotic experiences.

We characterized the neurocognitive profile of communed-based individuals and unaffected siblings of patients with psychosis from Brazil reporting psychotic experiences (PEs). We also analyzed associations between PEs and the intra and inter-functional connectivity (FC) in the Default Mode Network (DMN), the Fronto-Parietal Network (FPN) and the Salience Network (SN) measured by functional magnetic resonance imaging. The combined sample of communed-based individuals and unaffected siblings of patients with psychosis comprised 417 (neurocognition) and 85 (FC) volunteers who were divided as having low (<75th percentile) and high (≥75th percentile) PEs (positive, negative, and depressive dimensions) assessed by the Community Assessment of Psychic Experiences. The neurocognitive profile and the estimated current brief intellectual quotient (IQ) were assessed using the digit symbol (processing speed), arithmetic (working memory), block design (visual learning) and information (verbal learning) subtests of Wechsler Adult Intelligence Scale-third edition. Logistic regression models were performed for neurocognitive analysis. For neuroimaging, we used the CONN toolbox to assess FC between the specified regions, and ROI-to-ROI analysis. In the combined sample, high PEs (all dimensions) were related to lower processing speed performance. High negative PEs were related to poor visual learning performance and lower IQ, while high depressive PEs were associated with poor working memory performance. Those with high negative PEs presented FPN hypoconnectivity between the right and left lateral prefrontal cortex. There were no associations between PEs and the DMN and SN FC. Brazilian individuals with high PEs showed neurocognitive impairments like those living in wealthier countries. Hypoconnectivity in the FPN in a community sample with high PEs is coherent with the hypothesis of functional dysconnectivity in schizophrenia.

Viscous dynamics associated with hypoexcitation and structural disintegration in neurodegeneration via generative whole-brain modeling.

INTRODUCTION: Alzheimer's disease (AD) and behavioral variant frontotemporal dementia (bvFTD) lack mechanistic biophysical modeling in diverse, underrepresented populations. Electroencephalography (EEG) is a high temporal resolution, cost-effective technique for studying dementia globally, but lacks mechanistic models and produces non-replicable results. METHODS: We developed a generative whole-brain model that combines EEG source-level metaconnectivity, anatomical priors, and a perturbational approach. This model was applied to Global South participants (AD, bvFTD, and healthy controls). RESULTS: Metaconnectivity outperformed pairwise connectivity and revealed more viscous dynamics in patients, with altered metaconnectivity patterns associated with multimodal disease presentation. The biophysical model showed that connectome disintegration and hypoexcitability triggered altered metaconnectivity dynamics and identified critical regions for brain stimulation. We replicated the main results in a second subset of participants for validation with unharmonized, heterogeneous recording settings. DISCUSSION: The results provide a novel agenda for developing mechanistic model-inspired characterization and therapies in clinical, translational, and computational neuroscience settings.

Intra and inter-individual variability in functional connectomes of patients with First Episode of Psychosis.

Patients with Schizophrenia may show different clinical presentations, not only regarding inter-individual comparisons but also in one specific subject over time. In fMRI studies, functional connectomes have been shown to carry valuable individual level information, which can be associated with cognitive and behavioral variables. Moreover, functional connectomes have been used to identify subjects within a group, as if they were fingerprints. For the particular case of Schizophrenia, it has been shown that there is reduced connectome stability as well as higher inter-individual variability. Here, we studied inter and intra-individual heterogeneity by exploring functional connectomes' variability and related it with clinical variables (PANSS Total scores and antipsychotic's doses). Our sample consisted of 30 patients with First Episode of Psychosis and 32 Healthy Controls, with a test-retest approach of two resting-state fMRI scanning sessions. In our patients' group, we found increased deviation from healthy functional connectomes and increased intragroup inter-subject variability, which was positively correlated to symptoms' levels in six subnetworks (visual, somatomotor, dorsal attention, ventral attention, frontoparietal and DMN). Moreover, changes in symptom severity were positively related to changes in deviation from healthy functional connectomes. Regarding intra-subject variability, we were unable to replicate previous findings of reduced connectome stability (i.e., increased intra-subject variability), but we found a trend suggesting that result. Our findings highlight the relevance of variability characterization in Schizophrenia, and they can be related to evidence of Schizophrenia patients having a noisy functional connectome.

Source space connectomics of neurodegeneration: One-metric approach does not fit all.

Brain functional connectivity in dementia has been assessed with dissimilar EEG connectivity metrics and estimation procedures, thereby increasing results' heterogeneity. In this scenario, joint analyses integrating information from different metrics may allow for a more comprehensive characterization of brain functional interactions in different dementia subtypes. To test this hypothesis, resting-state electroencephalogram (rsEEG) was recorded in individuals with Alzheimer's Disease (AD), behavioral variant frontotemporal dementia (bvFTD), and healthy controls (HCs). Whole-brain functional connectivity was estimated in the EEG source space using 101 different types of functional connectivity, capturing linear and nonlinear interactions in both time and frequency-domains. Multivariate machine learning and progressive feature elimination was run to discriminate AD from HCs, and bvFTD from HCs, based on joint analyses of i) EEG frequency bands, ii) complementary frequency-domain metrics (e.g., instantaneous, lagged, and total connectivity), and iii) time-domain metrics with different linearity assumption (e.g., Pearson correlation coefficient and mutual information). <10% of all possible connections were responsible for the differences between patients and controls, and atypical connectivity was never captured by >1/4 of all possible connectivity measures. Joint analyses revealed patterns of hypoconnectivity (patientsHCs) in both groups was mainly identified in frontotemporal regions. These atypicalities were differently captured by frequency- and time-domain connectivity metrics, in a bandwidth-specific fashion. The multi-metric representation of source space whole-brain functional connectivity evidenced the inadequacy of single-metric approaches, and resulted in a valid alternative for the selection problem in EEG connectivity. These joint analyses reveal patterns of brain functional interdependence that are overlooked with single metrics approaches, contributing to a more reliable and interpretable description of atypical functional connectivity in neurodegeneration.

Polygenic risk score for attention-deficit/hyperactivity disorder and brain functional networks segregation in a community-based sample.

Neuroimaging studies suggest that brain development mechanisms might explain at least some behavioural and cognitive attention-deficit/hyperactivity disorder (ADHD) symptoms. However, the putative mechanisms by which genetic susceptibility factors influence clinical features via alterations of brain development remain largely unknown. Here, we set out to integrate genomics and connectomics tools by investigating the associations between an ADHD polygenic risk score (ADHD-PRS) and functional segregation of large-scale brain networks. With this aim, ADHD symptoms score, genetic and rs-fMRI (resting-state functional magnetic resonance image) data obtained in a longitudinal community-based cohort of 227 children and adolescents were analysed. A follow-up was conducted approximately 3 years after the baseline, with rs-fMRI scanning and ADHD likelihood assessment in both stages. We hypothesised a negative correlation between probable ADHD and the segregation of networks involved in executive functions, and a positive correlation with the default-mode network (DMN). Our findings suggest that ADHD-PRS is correlated with ADHD at baseline, but not at follow-up. Despite not surviving for multiple comparison correction, we found significant correlations between ADHD-PRS and segregation of cingulo-opercular networks and DMN at baseline. ADHD-PRS was negatively correlated with the segregation level of cingulo-opercular networks but positively correlated with the DMN segregation. These directions of associations corroborate the proposed counter-balanced role of attentional networks and DMN in attentional processes. However, the association between ADHD-PRS and brain networks functional segregation was not found at follow-up. Our results provide evidence for specific influences of genetic factors on development of attentional networks and DMN. We found significant correlations between polygenic risk score for ADHD (ADHD-PRS) and segregation of cingulo-opercular networks and default-mode network (DMN) at baseline. ADHD-PRS was negatively correlated with the segregation level of cingulo-opercular networks but positively correlated with the DMN segregation.

Human motor sequence learning drives transient changes in network topology and hippocampal connectivity early during memory consolidation.

In the last decade, the exclusive role of the hippocampus in human declarative learning has been challenged. Recently, we have shown that gains in performance observed in motor sequence learning (MSL) during the quiet rest periods interleaved with practice are associated with increased hippocampal activity, suggesting a role of this structure in motor memory reactivation. Yet, skill also develops offline as memory stabilizes after training and overnight. To examine whether the hippocampus contributes to motor sequence memory consolidation, here we used a network neuroscience strategy to track its functional connectivity offline 30 min and 24 h post learning using resting-state functional magnetic resonance imaging. Using a graph-analytical approach we found that MSL transiently increased network modularity, reflected in an increment in local information processing at 30 min that returned to baseline at 24 h. Within the same time window, MSL decreased the connectivity of a hippocampal-sensorimotor network, and increased the connectivity of a striatal-premotor network in an antagonistic manner. Finally, a supervised classification identified a low-dimensional pattern of hippocampal connectivity that discriminated between control and MSL data with high accuracy. The fact that changes in hippocampal connectivity were detected shortly after training supports a relevant role of the hippocampus in early stages of motor memory consolidation.

Reading Argumentative Texts: Effects of the presence-absence of connectives in reading comprehension and cognitive effort / Leer Textos Argumentativos: efectos de la presencia-ausencia de conectores en la comprensión lectora y esfuerzo cognitivo

Recent studies prove a strong association between reading and eye movements. Few investigations report the role of connectors and prior knowledge during reading in Spanish, as well as their association with eye movements. The present study aims to evaluate the effects of the presence absence of connectors in two argumentative texts on cognitive effort and reading comprehension. Forty-one psychology undergraduate students participated in a reading comprehension task, while their eye movements were recorded. The condition with connectors was related to prior knowledge, the slide time fixation, the slide number fixations, and the slide return fixations. The condition without connectors was related to the return fixations, the time fixation, and the number of fixations. Prior knowledge was correlated with the total time fixation, the total return fixations, and comprehension. This suggests that during reading without connectors more cognitive effort is required, observed in the return fixations; moreover, prior knowledge has an important role in the visual strategies required to process and obtain a representation of text. But participant performance was still good as observed in the scores of the reading comprehension task

Estudios prueban una asociación entre lectura y movimientos oculares al reportar el efecto de los conectores en el procesamiento textual. Pocas investigaciones revelan el papel de los conectores, el conocimiento previo y los movimientos oculares en la lectura de textos en español. El objetivo fue evaluar los efectos de la presencia-ausencia de conectores en dos textos argumentativos sobre el esfuerzo cognitivo y la comprensión lectora. Participaron 41 universitarios en una tarea de lectura con registro de sus movimientos oculares. La comprensión en la condición con conectores estuvo relacionada con conocimiento previo, tiempo y número de fijaciones por diapositiva, además del número de regresos en la lectura por diapositiva. Mientras la comprensión de textos sin conectores estuvo relacionada con conocimiento previo, regresos, tiempo y número de fijaciones, el conocimiento previo se correlacionó con tiempo y número fijaciones, regresos en la lectura y la comprensión. Se sugiere que la lectura de un texto sin conectores requerirá mayor esfuerzo cognitivo observado en los regresos en la lectura; además, el conocimiento previo afecta las estrategias visuales para procesar y representar mentalmente un texto. Pero, el desempeño de los participantes sigue siendo bueno, según lo observado en los puntajes de la tarea de comprensión lectora.

Long-Term Follow-Up on Bilateral Posterior Hypothalamic Deep Brain Stimulation for Treating Refractory Aggressive Behavior in a Patient with Cri du Chat Syndrome: Analysis of Clinical Data, Intraoperative Microdialysis, and Imaging Connectomics.

Posterior hypothalamic-deep brain stimulation (pHyp-DBS) has been reported as a successful treatment for reducing refractory aggressive behaviors in patients with distinct primary diagnoses. Here, we report on a patient with cri du chat syndrome presenting severe self-injury and aggressive behaviors toward others, who was treated with pHyp-DBS. Positive results were observed at long-term follow-up in aggressive behavior and quality of life. Intraoperative microdialysis and imaging connectomics analysis were performed to investigate possible mechanisms of action. Our results suggest the involvement of limbic and motor areas and alterations in main neurotransmitter levels in the targeted area that are associated with positive results following treatment.

High-order functional redundancy in ageing explained via alterations in the connectome in a whole-brain model.

The human brain generates a rich repertoire of spatio-temporal activity patterns, which support a wide variety of motor and cognitive functions. These patterns of activity change with age in a multi-factorial manner. One of these factors is the variations in the brain's connectomics that occurs along the lifespan. However, the precise relationship between high-order functional interactions and connnectomics, as well as their variations with age are largely unknown, in part due to the absence of mechanistic models that can efficiently map brain connnectomics to functional connectivity in aging. To investigate this issue, we have built a neurobiologically-realistic whole-brain computational model using both anatomical and functional MRI data from 161 participants ranging from 10 to 80 years old. We show that the differences in high-order functional interactions between age groups can be largely explained by variations in the connectome. Based on this finding, we propose a simple neurodegeneration model that is representative of normal physiological aging. As such, when applied to connectomes of young participant it reproduces the age-variations that occur in the high-order structure of the functional data. Overall, these results begin to disentangle the mechanisms by which structural changes in the connectome lead to functional differences in the ageing brain. Our model can also serve as a starting point for modeling more complex forms of pathological ageing or cognitive deficits.

Superficial white matter bundle atlas based on hierarchical fiber clustering over probabilistic tractography data.

The study of short association fibers is still an incomplete task due to their higher inter-subject variability and the smaller size of this kind of fibers in comparison to known long association bundles. However, their description is essential to understand human brain dysfunction and better characterize the human brain connectome. In this work, we present a multi-subject atlas of short association fibers, which was computed using a superficial white matter bundle identification method based on fiber clustering. To create the atlas, we used probabilistic tractography from one hundred subjects from the HCP database, aligned with non-linear registration. The method starts with an intra-subject clustering of short fibers (30-85 mm). Based on a cortical atlas, the intra-subject cluster centroids from all subjects are segmented to identify the centroids connecting each region of interest (ROI) of the atlas. To reduce computational load, the centroids from each ROI group are randomly separated into ten subgroups. Then, an inter-subject hierarchical clustering is applied to each centroid subgroup, followed by a second level of clustering to select the most-reproducible clusters across subjects for each ROI group. Finally, the clusters are labeled according to the regions that they connect, and clustered to create the final bundle atlas. The resulting atlas is composed of 525 bundles of superficial short association fibers along the whole brain, with 384 bundles connecting pairs of different ROIs and 141 bundles connecting portions of the same ROI. The reproducibility of the bundles was verified using automatic segmentation on three different tractogram databases. Results for deterministic and probabilistic tractography data show high reproducibility, especially for probabilistic tractography in HCP data. In comparison to previous work, our atlas features a higher number of bundles and greater cortical surface coverage.

Recovery of neural dynamics criticality in personalized whole-brain models of stroke.

The critical brain hypothesis states that biological neuronal networks, because of their structural and functional architecture, work near phase transitions for optimal response to internal and external inputs. Criticality thus provides optimal function and behavioral capabilities. We test this hypothesis by examining the influence of brain injury (strokes) on the criticality of neural dynamics estimated at the level of single participants using directly measured individual structural connectomes and whole-brain models. Lesions engender a sub-critical state that recovers over time in parallel with behavior. The improvement of criticality is associated with the re-modeling of specific white-matter connections. We show that personalized whole-brain dynamical models poised at criticality track neural dynamics, alteration post-stroke, and behavior at the level of single participants.

Functional Connectome Analysis in Mild Cognitive Impairment: Comparing Alzheimer's Disease Continuum and Suspected Non-Alzheimer Pathology.

Introduction: Research in brain resting-state functional connectivity (FC) analysis in mild cognitive impairment (MCI) has conflicting results. This work intends to find differences in resting-state FC of 2 groups of MCI subjects due to Alzheimer's disease (MCI-AD) continuum or to suspected non-Alzheimer pathology (MCI-SNAP). Materials and Methods: Ninety-two subjects older than 55 years were enrolled. MCI and controls were grouped using clinical dementia rating and neuropsychological data. Cerebrospinal fluid biomarkers were collected from MCI subjects, resulting in 32 MCI-AD, 25 MCI-SNAP, and 35 controls. A region of interest (ROI)-to-ROI analysis was carried out looking at inter- and intranetwork interactions selecting the following networks: default mode network (DMN), salience network (SN), visuospatial network (VN), and executive network. Pearson correlation coefficients, converted to Z-scores, were compared by T-tests with alpha set to 0.05, and false discovery rate corrected. Results: Groups were similar in age, education, and demographic measures, there were no differences in neuropsychological data between the MCI groups. The ROI-to-ROI analysis of MCI-AD versus MCI-SNAP showed no differences. MCI-AD versus controls showed decreased FC between ROIs of the SN and between ROIs from SN and VN. MCI-SNAP versus controls showed increased FC between an ROI of DMN and VN. Discussion: SN, DMN, and VN are multimodal networks with high value/high cost and may be more vulnerable to AD pathogenic processes. SN and VN were affected in the MCI-AD group, with maintained anticorrelation between DMN and VN. This may indicate subthreshold DMN dysfunction. The result in MCI-SNAP, although discrete, reflects a rearrangement of brain FC, as DMN and VN are expected to be anticorrelated. More research is necessary to confirm these findings.

[The brain. An analogic machine with quantum functioning?] / El cerebro ¿una máquina analógica con funcionamiento cuántico?

Modern neuroscience addresses the problem of the global functioning of the brain in order to understand the neurobiological processes that underlie mental functions, and especially, consciousness. Brain activity is based on the exchange of information between neurons through contacts or synapses. Neurons form networks of connection between them (circuits), which are dedicated to processing a specific type of information (visual, auditory, motor ...). The circuits establish networks among themselves, combining different modalities of information to generate what we know as mental activity. The study of connections between cortical regions, which has been called connectome, is being approached through neuroimaging techniques such as nuclear magnetic resonance that provide data on the density of connections in the brain. The brain's ability to create new connections based on experience (brain plasticity) suggests that the connectome is a dynamic structure in constant interaction with external and internal stimuli. The question about whether knowledge of an individual's connectome would allow us to predict his or her behavior seems to have no clear answer yet, because we do not know the physical parameters that link the complexity of the brain's connections with the appearance of mental functions and consciousness. At the moment, it seems that the complex and unpredictable behavior is not the simple result of linear processes of neuronal interaction. Uncertainty prevails over determinism, which opens the door to the possibility of a quantum mechanism to explain consciousness.

La neurociencia moderna aborda el problema de funcionamiento global del cerebro para poder comprender los procesos neurobiológicos que subyacen a las funciones mentales, y especialmente, a la consciencia. La actividad cerebral está basada en el intercambio de información entre neuronas a través de contactos llamados sinapsis. Las neuronas forman redes de conexión entre ellas (circuitos), que están dedicados a procesar una parcela específica de información (visual, auditiva, motora ...). Los circuitos establecen redes entre ellos, combinando diferentes modalidades de información para generar lo que conocemos como actividad mental. El estudio de las conexiones entre regiones corticales, que se ha llamado conectoma, está siendo abordado mediante técnicas de neuroimagen como la resonancia magnética nuclear, que aportan datos sobre la densidad de conexiones del cerebro. La capacidad del cerebro de crear nuevas conexiones en función de la experiencia (plasticidad cerebral), sugiere que el conectoma es una estructura dinámica en constante interacción con estímulos externos e internos. La pregunta sobre si el conocimiento del conectoma de un individuo nos permitiría predecir su conducta parece que todavía no tiene respuesta clara, porque no conocemos los parámetros físicos que ligan la complejidad de las conexiones del cerebro con la aparición de las funciones mentales y de la consciencia. Por el momento, parece que la compleja e impredecible conducta no es el simple resultado de procesos lineales de interacción neuronal. La incertidumbre prima al determinismo, lo que abre la puerta a la posibilidad de un mecanismo cuántico para explicar la consciencia.

Stationary EEG pattern relates to large-scale resting state networks - An EEG-fMRI study connecting brain networks across time-scales.

Relating brain dynamics acting on time scales that differ by at least an order of magnitude is a fundamental issue in brain research. The same is true for the observation of stable dynamical structures in otherwise highly non-stationary signals. The present study addresses both problems by the analysis of simultaneous resting state EEG-fMRI recordings of 53 patients with epilepsy. Confirming previous findings, we observe a generic and temporally stable average correlation pattern in EEG recordings. We design a predictor for the General Linear Model describing fluctuations around the stationary EEG correlation pattern and detect resting state networks in fMRI data. The acquired statistical maps are contrasted to several surrogate tests and compared with maps derived by spatial Independent Component Analysis of the fMRI data. By means of the proposed EEG-predictor we observe core nodes of known fMRI resting state networks with high specificity in the default mode, the executive control and the salience network. Our results suggest that both, the stationary EEG pattern as well as resting state fMRI networks are different expressions of the same brain activity. This activity is interpreted as the dynamics on (or close to) a stable attractor in phase space that is necessary to maintain the brain in an efficient operational mode. We discuss that this interpretation is congruent with the theoretical framework of complex systems as well as with the brain's energy balance.

Type 1 diabetes affects the brain functional connectivity underlying working memory processing.

Visuospatial working memory (VSWM) deficits have been demonstrated to occur during the development of type-1-diabetes (T1D). Despite confirming the early appearance of distinct task-related brain activation patterns in T1D patients compared to healthy controls, the effect of VSWM load on functional brain connectivity during task performance is still unknown. Using electroencephalographic methods, the present study evaluated this topic in clinically well-controlled T1D young patients and healthy individuals, while they performed a VSWM task with different memory load levels during two main VSWM processing phases: encoding and maintenance. The results showed a significantly lower number of correct responses and longer reaction times in T1D while performing the task. Besides, higher and progressively increasing functional connectivity indices were found for T1D patients in response to cumulative degrees of VSWM load, from the beginning of the VSWM encoding phase, without notably affecting the VSWM maintenance phase. In contrast, healthy controls managed to solve the task, showing lower functional brain connectivity during the initial VSWM processing steps with more gradual task-related adjustments. Present results suggest that T1D patients anticipate high VSWM load demands by early recruiting supplementary processing resources as the probable expression of a more inefficient, though paradoxically better adjusted to task demands cognitive strategy.

Abnormal nodal and global network organization in resting state functional MRI from subjects with the 22q11 deletion syndrome.

The 22q11 deletion syndrome is a genetic disorder associated with a high risk of developing psychosis, and is therefore considered a neurodevelopmental model for studying the pathogenesis of schizophrenia. Studies have shown that localized abnormal functional brain connectivity is present in 22q11 deletion syndrome like in schizophrenia. However, it is less clear whether these abnormal cortical interactions lead to global or regional network disorganization as seen in schizophrenia. We analyzed from a graph-theory perspective fMRI data from 40 22q11 deletion syndrome patients and 67 healthy controls, and reconstructed functional networks from 105 brain regions. Between-group differences were examined by evaluating edge-wise strength and graph theoretical metrics of local (weighted degree, nodal efficiency, nodal local efficiency) and global topological properties (modularity, local and global efficiency). Connectivity strength was globally reduced in patients, driven by a large network comprising 147 reduced connections. The 22q11 deletion syndrome network presented with abnormal local topological properties, with decreased local efficiency and reductions in weighted degree particularly in hub nodes. We found evidence for abnormal integration but intact segregation of the 22q11 deletion syndrome network. Results suggest that 22q11 deletion syndrome patients present with similar aberrant local network organization as seen in schizophrenia, and this network configuration might represent a vulnerability factor to psychosis.