Altered Brain Connectivity Research Articles

Anatomical connectivity in children with developmental dyscalculia: A graph theory study.

Current theories postulate that numerical processing depends upon a brain circuit formed by regions and their connections; specialized in the representation and manipulation of the numerical properties of stimuli. It has been suggested that the damage of these network may cause Developmental Dyscalculia (DD): a persistent neurodevelopmental disorder that significantly interferes with academic performance and daily life activities that require mastery of mathematical notions and operations. However, most of the studies on the brain foundations of DD have focused on regions of interest associated with numerical processing, and have not addressed numerical cognition as a complex network phenomenon. The present study explored DD using a Graph Theory network approach. We studied the association between topological measures of integration and segregation of information processing in the brain proposed by Graph Theory; and individual variability in numerical performance in a group of 11 school-aged children with DD (5 of which presented with comorbidity with Developmental Dyslexia, the specific learning disorder for reading) and 17 typically developing peers. A statistically significant correlation was found between the Weber fraction (a measure of numerical representations' precision) and the Clustering Index (a measure of segregation of information processing) in the whole sample. The DD group showed significantly lower Characteristic Path Length (average shortest path length among all pairs of regions in the brain network) compared to controls. Also, differences in critical regions for the brain network performance (hubs) were found between groups. The presence of limbic hubs characterized the DD brain network while right Temporal and Frontal hubs found in controls were absent in the DD group. Our results suggest that the DD may be associated with alterations in anatomical brain connectivity that hinder the capacity to integrate and segregate numerical information.

Clarifying the role of Cortico-Cortical and Amygdalo-Cortical brain dysconnectivity associated with Conduct Problems

A recent meta-analysis of resting-state functional connectivity studies revealed that individuals exhibiting antisocial behaviors or conduct problems may show disrupted brain connectivity in networks underpinning socio-affective and attentional processes. However, studies included in the meta-analysis generally rely on small sample sizes and substantially differ in terms of psychometric scales and neuroimaging methodologies. Therefore, we aimed to identify reliable functional brain connectivity alterations associated with severity of conduct problems using a large sample of adolescents and two measures of conduct problems. In a sample of 1416 children and adolescents, mass-univariate analyses of connectivity measures between 333 cortical parcels were conducted to examine the relationship between resting-state functional cortical-cortical connectome and the severity of conduct problems using the Child Behavior Checklist (CBCL) and the Strengths and Difficulties Questionnaire (SDQ). At a liberal threshold, results showed that the functional brain connectivity significantly associated with conduct problems largely differ between the two scales. Indeed, only 21 pairs of brain regions overlapped between the CBCL and SDQ. Permutation feature importance of these 21 brain connectivity measures revealed that connectivity between precentral/postcentral gyri and lateral prefrontal cortex (both ventral and dorsal) were the most important features in explaining variance in conduct problems. The current study highlights that psychometric measures may yield distinct functional connectivity results. Moreover, severity of conduct problems in children and adolescents was mainly associated with deficient functional connectivity of somatomotor and ventral attention networks indicating potential alterations in motor, cognitive and reward processes.

Effects of gastric bypass surgery on brain connectivity responses to hypoglycemia

IntroductionRoux-en-Y gastric bypass (RYGB) leads to beneficial effects on glucose homeostasis, and attenuated hormonal counterregulatory responses to hypoglycemia are likely to contribute. RYGB also induces alterations in neural activity of cortical and subcortical brain regions. We aimed to characterize RYGB-induced changes in resting-state connectivity of specific brain regions of interest for energy homeostasis and behavioral control during hypoglycemia.MethodTen patients with BMI > 35 kg/m2 were investigated with brain PET/MR imaging during a hyperinsulinemic normo- and hypoglycemic clamp, before and 4 months after RYGB. Hormonal levels were assessed throughout the clamp. Resting-state (RS) fMRI scans were acquired in the glucose-lowering phase of the clamp, and they were analyzed with a seed-to-voxel approach.ResultsRS connectivity during initiation of hypoglycemia was significantly altered after RYGB between nucleus accumbens, thalamus, caudate, hypothalamus and their crosstalk with cortical and subcortical regions. Connectivity between the nucleus accumbens and the frontal pole was increased after RYGB, and this was associated with a reduction of ACTH (r = −0.639, p = 0.047) and cortisol (r = −0.635, p = 0.048) responses. Instead, connectivity between the caudate and the frontal pole after RYGB was reduced and this was associated with less attenuation of glucagon response during the hypoglycemic clamp (r = −0.728, p = 0.017), smaller reduction in fasting glucose (r = −0.798, p = 0.007) and less excess weight loss (r = 0.753, p = 0.012). No other significant associations were found between post-RYGB changes in ROI-to-voxel regional connectivity hormonal responses and metabolic or anthropometric outcomes.ConclusionRYGB alters brain connectivity during hypoglycemia of several neural pathways involved in reward, inhibitory control, and energy homeostasis. These changes are associated with altered hormonal responses to hypoglycemia and may be involved in the glucometabolic outcome of RYGB.

Brain functional connectivity alterations associated with neuropsychological performance 6-9months following SARS-CoV-2 infection.

Neuropsychological deficits and brain damage following SARS-CoV-2 infection are not well understood. Then, 116 patients, with either severe, moderate, or mild disease in the acute phase underwent neuropsychological and olfactory tests, as well as completed psychiatric and respiratory questionnaires at 223 ± 42 days postinfection. Additionally, a subgroup of 50 patients underwent functional magnetic resonance imaging. Patients in the severe group displayed poorer verbal episodic memory performances, and moderate patients had reduced mental flexibility. Neuroimaging revealed patterns of hypofunctional and hyperfunctional connectivities in severe patients, while only hyperconnectivity patterns were observed for moderate. The default mode, somatosensory, dorsal attention, subcortical, and cerebellar networks were implicated. Partial least squares correlations analysis confirmed specific association between memory, executive functions performances and brain functional connectivity. The severity of the infection in the acute phase is a predictor of neuropsychological performance 6-9months following SARS-CoV-2 infection. SARS-CoV-2 infection causes long-term memory and executive dysfunctions, related to large-scale functional brain connectivity alterations.

CSF proteins involved in neuroplasticity are associated with altered whole brain connectivity in prodromal AD

AbstractBackgroundIndividuals with prodromal Alzheimer’s Disease (AD) and higher gray matter connectivity measures show slower decline, suggesting resilience to AD. However, the precise biological processes underlying preserved brain connections remains unclear. Here, we studied this question using cerebrospinal fluid (CSF) proteomics.MethodFrom the ADNI we selected 78 individuals with MCI and abnormal CSF amyloid (i.e., prodromal AD) and 34 with normal cognition and normal CSF amyloid levels for whom MRI and CSF proteomic data was available. For each T1 MRI scan we constructed gray matter networks using an automated pipeline (Tijms, Series et al. 2012) and calculated degree, connectivity density, clustering, path length and small‐world measures (i.e. gamma, lambda and small world coefficient). Linear models were used to test associations between each connectivity measure and CSF protein levels while correcting for age and gender. Proteins showing associations of p < 0.1 were selected to find groups of proteins that are associated with biological processes according to Gene Ontology (GO). Proteomic profiles for each connectivity measure were compared to investigate overlap. Finally, we tested for enrichment of potential upstream drivers with the Enrichr tool.ResultIn total, 115 proteins were associated with altered connectivity in prodromal AD subjects (figure 1). The majority of proteins (n = 93, 80%) showed higher levels with stronger (i.e., resilient) brain networks characterized by increased degree, connectivity density, clustering, path length, gamma, lambda or small world coefficient values. In controls, 80 of these 93 proteins showed an opposite relationship, with lower levels relating to higher values for these connectivity measures, which suggests that increased levels in prodromal AD may indicate a compensatory mechanism to maintain strong brain connectivity. According to a pathway analysis in GO, these proteins associated with stronger brain networks were mainly involved in neuroplasticity pathways. A subsequent Enrichr analysis suggested involvement of upstream divers SUZ12 and REST, which are known to regulate neuroplasticity processes.ConclusionWe observed that higher CSF levels of neuroplasticity associated proteins were associated with higher brain connectivity values in prodromal AD, suggesting that plasticity mechanisms may play a role in resilience to AD pathology.

Lifespan diffusion MRI reveals structural abnormalities in the 5xFAD model of Alzheimer’s Disease

AbstractBackgroundThe Model Organism Development and Evaluation for Late‐Onset Alzheimer’s Disease (MODEL‐AD) Consortium has been established to develop the next generation of Alzheimer’s disease (AD) models based on human genomic findings. As new models are developed, phenotypic data are compared to established models, including the 5xFAD mouse model. We undertook measurements of diffusion metrics to characterize the temporal alterations in brain structure in male and female 5xFAD mice, including connectivity analysis between the hippocampus and infralimbic prefrontal cortex (PFC).Method5xFAD mice were compared to age‐matched littermates (C57BL/6J, WT) at 4, 8, and 12 months (mo). Mice underwent high resolution diffusion tensor imaging (DTI) magnetic resonance imaging (MRI) at 9.4T (5 B0, 30 directions b=3000 mm2/sec) to assess regional white and gray matter. Regional tissue features were extracted from fractional anisotropy (FA), radial (RD), axial (AxD) and mean diffusivity (MD) parametric maps. Tractography was performed based on the AMBMC mouse atlas. 18F‐AV45 PET was performed in a separate cohort at 4, 6, and 12mo to quantitate amyloid β (Aβ) load.ResultElevated FA within hippocampal CA1 was first observed in 5xFAD males at 8mo, but at 12mo 5xFAD females had increased FA in bilateral CA1 compared to WT mice. Connectivity between CA1 and PFC found 12mo 5xFAD females had significantly increased RD along the tract with concomitant increases in MD and AxD. AV45‐PET uptake was increased at 6mo (∼20%) and remained elevated at 12mo (∼25%) in the PFC in male and female 5xFAD compared to WT. In the hippocampus there was a ∼20% increase in AV45 binding at 6mo, that then declines by 12mo in 5xFAD mice.ConclusionPhenotyping of mouse models using DTI identifies altered brain connectivity and regional tissue modifications that may be indicative of increasing Aβ deposition. These data support that increasing Aβ deposition results in altered DTI metrics and connectivity using clinically relevant imaging modalities.

Altered brain connectivity during visual stimulation in schizophrenia.

Schizophrenia (SCZ) can be described as a functional dysconnectivity syndrome that affects brain connectivity and circuitry. However, little is known about how sensory stimulation modulates network parameters in schizophrenia, such as their small-worldness (SW) during visual processing. To address this question, we applied graph theory algorithms to multi-electrode EEG recordings obtained during visual stimulation with a checkerboard pattern-reversal stimulus. Twenty-six volunteers participated in the study, 13 diagnosed with schizophrenia (SCZ; mean age = 38.3years; SD = 9.61years) and 13 healthy controls (HC; mean age = 28.92years; SD = 12.92years). The visually evoked potential (VEP) showed a global amplitude decrease (p < 0.05) for SCZ patients as opposed to HC but no differences in latency (p > 0.05). As a signature of functional connectivity, graph measures were obtained from the Magnitude-Squared Coherence between signals from pairs of occipital electrodes, separately for the alpha (8-13Hz) and low-gamma (36-55Hz) bands. For the alpha band, there was a significant effect of the visual stimulus on all measures (p < 0.05) but no group interaction between SCZ and HZ (p > 0.05). For the low-gamma spectrum, both groups showed a decrease of Characteristic Path Length (L) during visual stimulation (p < 0.05), but, contrary to the HC group, only SCZ significantly lowered their small-world (SW) connectivity index during visual stimulation (SCZ p < 0.05; HC p > 0.05). This indicates dysconnectivity of the functional network in the low-gamma band of SCZ during stimulation, which might indirectly reflect an altered ability to react to new sensory input in patients. These results provide novel evidence about a possible electrophysiological signature of the global deficits revealed by the application of graph theory onto electroencephalography in schizophrenia.

Graph Theory-Based Electroencephalographic Connectivity via Phase-Locking Value and Its Association with Ketogenic Diet Responsiveness in Patients with Focal Onset Seizures.

Ketogenic diets (KDs) are a promising alternative therapy for pediatric refractory epilepsy. Several predictors of KD responsiveness have been identified, including biochemical parameters, seizure types, and electroencephalography (EEG) examinations. We hypothesized that graph theory-based EEG functional connectivity could explain KD responses in patients presenting focal onset seizure (FOS). A total of 17 patients aged 0-30 years old with focal onset seizures (FOS) were recruited as a study group between January 2015 and July 2021. Twenty age-matched children presenting headache with no intracranial complications nor other medical issues were enrolled as a control group. Data were obtained at baseline and at 12 months after initiating KD therapy (KDT) using the child behavior checklist (CBCL) and brain functional connectivity parameters based on phase-locking value from 19 scalp EEG signals, including nodal strength, global efficiency, clustering coefficient, and betweenness centrality. Compared with age-matched controls, patients presenting FOS with right or bilateral EEG lateralization presented higher baseline functional connectivity, including parameters such as global efficiency, mean cluster coefficient and mean nodal strength in the delta and beta frequency bands. In patients presenting FOS with right or bilateral EEG lateralization, the global efficiency of functional connectivity parameters in the delta and theta frequency bands was significantly lower at 12 months after KDT treatment than before KDT. Those patients also presented a significantly lower mean clustering coefficient and mean nodal strength in the theta frequency band at 12 months after KDT treatment. Changes in brain functional connectivity were positively correlated with social problems, attention, and behavioral scores based on CBCL assessments completed by parents. This study provides evidence that KDT might be beneficial in the treatment of patients with FOS. Graph theoretic analysis revealed that the observed effects were related to decreased functional connectivity, particularly in terms of global efficiency. Our findings related to brain connectivity revealed lateralization to the right (non-dominant) hemisphere; however, we were unable to define the underlying mechanism. Our data revealed that in addition to altered brain connectivity, KDT improved the patient's behavior and emotional state.

Early Life Pain Experience Changes Adult Functional Pain Connectivity in the Rat Somatosensory and the Medial Prefrontal Cortex.

Early life pain (ELP) experience alters adult pain behavior and increases injury-induced pain hypersensitivity, but the effect of ELP on adult functional brain connectivity is not known. We have performed continuous local field potential (LFP) recording in the awake adult male rats to test the effect of ELP on functional cortical connectivity related to pain behavior. Primary somatosensory cortex (S1) and medial prefrontal cortex (mPFC) LFPs evoked by mechanical hindpaw stimulation were recorded simultaneously with pain reflex behavior for 10 d after adult incision injury. We show that, after adult injury, sensory evoked S1 LFP δ and γ energy and S1 LFP δ/γ frequency coupling are significantly increased in ELP rats compared with controls. Adult injury also induces increases in S1-mPFC functional connectivity, but this is significantly prolonged in ELP rats, lasting 4 d compared with 1 d in controls. Importantly, the increases in LFP energy and connectivity in ELP rats were directly correlated with increased behavioral pain hypersensitivity. Thus, ELP alters adult brain functional connectivity, both within and between cortical areas involved in sensory and affective dimensions of pain. The results reveal altered brain connectivity as a mechanism underlying the effects of ELP on adult pain perception.SIGNIFICANCE STATEMENT Pain and stress in early life has a lasting impact on pain behavior and may increase vulnerability to chronic pain in adults. Here, we record pain-related cortical activity and simultaneous pain behavior in awake adult male rats previously exposed to pain in early life. We show that functional connectivity within and between the somatosensory cortex and the medial prefrontal cortex (mPFC) is increased in these rats and that these increases are correlated with their behavioral pain hypersensitivity. The results reveal that early life pain (ELP) alters adult brain connectivity, which may explain the impact of childhood pain on adult chronic pain vulnerability.

Altered resting-state functional connectivity in hiPSCs-derived neuronal networks from schizophrenia patients.

Schizophrenia (SZ) is a severe mental disorder that arises from abnormal neurodevelopment, caused by genetic and environmental factors. SZ often involves distortions in reality perception and it is widely associated with alterations in brain connectivity. In the present work, we used Human Induced Pluripotent Stem Cells (hiPSCs)-derived neuronal cultures to study neural communicational dynamics during early development in SZ. We conducted gene and protein expression profiling, calcium imaging recordings, and applied a mathematical model to quantify the dynamism of functional connectivity (FC) in hiPSCs-derived neuronal networks. Along the neurodifferentiation process, SZ networks displayed altered gene expression of the glutamate receptor-related proteins HOMER1 and GRIN1 compared to healthy control (HC) networks, suggesting a possible tendency to develop hyperexcitability. Resting-state FC in neuronal networks derived from HC and SZ patients emerged as a dynamic phenomenon exhibiting connectivity configurations reoccurring in time (hub states). Compared to HC, SZ networks were less thorough in exploring different FC configurations, changed configurations less often, presented a reduced repertoire of hub states and spent longer uninterrupted time intervals in this less diverse universe of hubs. Our results suggest that alterations in the communicational dynamics of SZ emerging neuronal networks might contribute to the previously described brain FC anomalies in SZ patients, by compromising the ability of their neuronal networks for rapid and efficient reorganization through different activity patterns.

Sex-related differences in adult attention-deficit hyperactivity disorder patients – An analysis of external globus pallidus functional connectivity in resting-state functional MRI

In the last two decades, there has been a growing body of research that identified sex-related differences in attention-deficit hyperactivity disorder (ADHD). Our objective was to quantify whether these sex differences are based on altered functional brain connectivity profiles. In addition, we investigated whether the presence of comorbid disorders, including depression, substance use disorder (SUD) and overweight, influenced these sex differences. A seed-based connectivity analysis of the external globus pallidus (GPe), an important inhibitory relay hub of the fronto-thalamo-striatal-loop, was performed. In a first step, we searched for sex-related differences in ADHD patients (N = 137) and separately in healthy controls (HC) (N = 45), after that, we compared an equal group of HC and ADHD patients to compare sex-related differences in ADHD patients and HC. In a second step, we studied whether the neural basis of comorbidity patterns is different between male and female patients. We observed that male ADHD patients demonstrated a decrease in functional connectivity (FC) from the GPe to the left middle temporal gyrus compared to female ADHD patients. Moreover, within the full ADHD group (N = 137), there was a lower FC in male patients from GPe to the right frontal pole/middle frontal gyrus compared to female patients. Male ADHD patients with depression demonstrated decreased FC from the GPe to parts of the occipital cortex compared to female ADHD patients with depression. No such effect was demonstrated for overweight or SUD. The current study reveals different FC profiles in males and females with ADHD, which are centered around altered connectivity with the GPe. An improved understanding of sex-differences in ADHD, and the role of comorbid disorders, therein can result in improved diagnostic and therapeutic opportunities for ADHD patients.

Why do mothers never stop grieving for their deceased children? Enduring alterations of brain connectivity and function.

A child’s death is a profound loss for mothers and affects hundreds of thousands of women. Mothers report inconsolable and progressive grief that is distinct from depression and impacts daily emotions and functions. The brain mechanisms responsible for this relatively common and profound mental health problem are unclear, hampering its clinical recognition and care. In an initial exploration of this condition, we used resting state functional MRI (fMRI) scans to examine functional connectivity in key circuits, and task-based fMRI to examine brain network activity in grieving mothers in response to pictures of their deceased child and as well as recognizable deceased celebrities and unfamiliar individuals. We compared nine mothers who had lost an adult child and aged-matched control mothers with a living child of a similar age. Additionally, we collected diffusion imaging scans to probe structural connectivity and complemented the imaging studies with neuropsychological assessments. Increased functional activation in Ventral Attention/Salience Networks accompanied by a reduced activation in the medial prefrontal cortex in response to the deceased child’s picture robustly distinguished the grieving mothers from controls. Heightened resting-state functional connectivity between the paraventricular thalamic nucleus (PVT) and the amygdala distinguished the grieving mothers from the controls and correlated with subjective grief severity. Structurally, maternal grief and its severity were associated with alterations in corticolimbic white matter tracts. Finally, grieving mothers performed worse than controls on neuropsychological tests of learning, memory, and executive function, linked with grief severity. Reduced activation in cortical regions inhibiting emotions and changes in the PVT circuitry—a region involved in long-term emotional memories and decision making under conflict—distinguish grieving mothers from controls. Notably, the magnitude of neurobiological changes correlates with the subjective severity of grief. Together, these new discoveries delineate a prevalent and under-recognized mental health syndrome and chart a path for its appreciation and care.

Genetic Heterogeneity Shapes Brain Connectivity in Psychiatry

BackgroundPolygenicity and genetic heterogeneity pose great challenges for studying psychiatric conditions. Genetically informed approaches have been implemented in neuroimaging studies to address this issue. However, the effects on functional connectivity of rare and common genetic risks for psychiatric disorders are largely unknown. Our objectives were to estimate and compare the effect sizes on brain connectivity of psychiatric genomic risk factors with various levels of complexity: oligogenic copy number variants (CNVs), multigenic CNVs, and polygenic risk scores (PRSs) as well as idiopathic psychiatric conditions and traits. MethodsResting-state functional magnetic resonance imaging data were processed using the same pipeline across 9 datasets. Twenty-nine connectome-wide association studies were performed to characterize the effects of 15 CNVs (1003 carriers), 7 PRSs, 4 idiopathic psychiatric conditions (1022 individuals with autism, schizophrenia, bipolar conditions, or attention-deficit/hyperactivity disorder), and 2 traits (31,424 unaffected control subjects). ResultsEffect sizes on connectivity were largest for psychiatric CNVs (estimates: 0.2–0.65 z score), followed by psychiatric conditions (0.15–0.42), neuroticism and fluid intelligence (0.02–0.03), and PRSs (0.01–0.02). Effect sizes of CNVs on connectivity were correlated to their effects on cognition and risk for disease (r = 0.9, p = 5.93 × 10−6). However, effect sizes of CNVs adjusted for the number of genes significantly decreased from small oligogenic to large multigenic CNVs (r = −0.88, p = 8.78 × 10−6). PRSs had disproportionately low effect sizes on connectivity compared with CNVs conferring similar risk for disease. ConclusionsHeterogeneity and polygenicity affect our ability to detect brain connectivity alterations underlying psychiatric manifestations.

Coherence analysis in the brain network of ASD children using connectivity model and graph theory

Autism Spectrum Disorder (ASD) belongs with the category of neuro-developmental disorders, which can be majorly categorized under decreased social relationships, communication and thought processes. Various studies in the field of biological networks prove that one of the defining features of ASD is altered brain connectivity. Hence, the understanding of the brain networks can pave the way to delve deeper into the underlying behaviour of the Autistic brains. Moreover, many studies also reveal that human brains exhibit small-world characteristics which are usually seen in simple model neural networks that emerge spontaneously upon adaptive rewiring according to the dynamical functional connectivity. Graph theory-based approaches are finding their way into the understanding of the altered connectivity in various neurological disorders. For that matter, the study focuses on implementing a graph theory-based approach to investigate on the small-world network of Autistic as well as typically developing brains and understand the behavioural changes for an Audio and Video Stimuli. The graphically generated data is then measured for functional connectivity using a symmetrical parameter known as the coherence measure.

Sparse Hierarchical Representation Learning on Functional Brain Networks for Prediction of Autism Severity Levels.

Machine learning algorithms have been widely applied in diagnostic tools for autism spectrum disorder (ASD), revealing an altered brain connectivity. However, little is known about whether an magnetic resonance imaging (MRI)-based brain network is related to the severity of ASD symptoms in a large-scale cohort. We propose a graph convolution neural network-based framework that can generate sparse hierarchical graph representations for functional brain connectivity. Instead of assigning initial features for each node, we utilized a feature extractor to derive node features and the extracted representations can be fed to a hierarchical graph self-attention framework to effectively represent the entire graph. By incorporating connectivity embeddings in the feature extractor, we propose adjacency embedding networks to characterize the heterogeneous representations of the brain connectivity. Our proposed model variants outperform the benchmarking model with different configurations of adjacency embedding networks and types of functional connectivity matrices. Using this approach with the best configuration (SHEN atlas for node definition, Tikhonov correlation for connectivity estimation, and identity-adjacency embedding), we were able to predict individual ASD severity levels with a meaningful accuracy: the mean absolute error (MAE) and correlation between predicted and observed ASD severity scores resulted in 0.96, and r = 0.61 (P < 0.0001), respectively. To obtain a better understanding on how to generate better representations, we investigate the relationships between the extracted feature embeddings and the graph theory-based nodal measurements using canonical correlation analysis. Finally, we visualized the model to identify the most contributive functional connections for predicting ASD severity scores.

Rich-Club Analysis of Structural Brain Network Alterations in HIV Positive Patients With Fully Suppressed Plasma Viral Loads

ObjectiveEven with successful combination antiretroviral therapy (cART), patients with human immunodeficiency virus positive (HIV+) continue to present structural alterations and neuropsychological impairments. The purpose of this study is to investigate structural brain connectivity alterations and identify the hub regions in HIV+ patients with fully suppressed plasma viral loads.MethodsIn this study, we compared the brain structural connectivity in 48 patients with HIV+ treated with a combination of antiretroviral therapy and 48 healthy controls, using diffusion tensor imaging. Further comparisons were made in 24 patients with asymptomatic neurocognitive impairment (ANI) and 24 individuals with non-HIV-associated neurocognitive disorders forming a subset of HIV+ patients. The graph theory model was used to establish the topological metrics. Rich-club analysis was used to identify hub nodes across groups and abnormal rich-club connections. Correlations of connectivity metrics with cognitive performance and clinical variables were investigated as well.ResultsAt the regional level, HIV+ patients demonstrated lower degree centrality (DC), betweenness centrality (BC), and nodal efficiency (NE) at the occipital lobe and the limbic cortex; and increased BC and nodal cluster coefficient (NCC) in the occipital lobe, the frontal lobe, the insula, and the thalamus. The ANI group demonstrated a significant reduction in the DC, NCC, and NE in widespread brain regions encompassing the occipital lobe, the frontal lobe, the temporal pole, and the limbic system. These results did not survive the Bonferroni correction. HIV+ patients and the ANI group had similar hub nodes that were mainly located in the occipital lobe and subcortical regions. The abnormal connections were mainly located in the occipital lobe in the HIV+ group and in the parietal lobe in the ANI group. The BC in the calcarine fissure was positively correlated with complex motor skills. The disease course was negatively correlated with NE in the middle occipital gyrus.ConclusionThe results suggest that the occipital lobe and the subcortical regions may be important in structural connectivity alterations and cognitive impairment. Rich-club analysis may contribute to our understanding of the neuropathology of HIV-associated neurocognitive disorders.

Shared and Specific Patterns of Structural Brain Connectivity Across Affective and Psychotic Disorders

BackgroundAltered brain structural connectivity has been implicated in the pathophysiology of psychiatric disorders including schizophrenia (SZ), bipolar disorder (BD), and major depressive disorder (MDD). However, it is unknown which part of these connectivity abnormalities are disorder specific and which are shared across the spectrum of psychotic and affective disorders. We investigated common and distinct brain connectivity alterations in a large sample (N = 1743) of patients with SZ, BD, or MDD and healthy control (HC) subjects. MethodsThis study examined diffusion-weighted imaging-based structural connectome topology in 720 patients with MDD, 112 patients with BD, 69 patients with SZ, and 842 HC subjects (mean age of all subjects: 35.7 years). Graph theory–based network analysis was used to investigate connectome organization. Machine learning algorithms were trained to classify groups based on their structural connectivity matrices. ResultsGroups differed significantly in the network metrics global efficiency, clustering, present edges, and global connectivity strength with a converging pattern of alterations between diagnoses (e.g., efficiency: HC > MDD > BD > SZ, false discovery rate–corrected p = .028). Subnetwork analysis revealed a common core of edges that were affected across all 3 disorders, but also revealed differences between disorders. Machine learning algorithms could not discriminate between disorders but could discriminate each diagnosis from HC. Furthermore, dysconnectivity patterns were found most pronounced in patients with an early disease onset irrespective of diagnosis. ConclusionsWe found shared and specific signatures of structural white matter dysconnectivity in SZ, BD, and MDD, leading to commonly reduced network efficiency. These results showed a compromised brain communication across a spectrum of major psychiatric disorders.

Altered dynamic functional network connectivity within default mode network of epileptic children with generalized tonic-clonic seizures

ObjectiveGeneralized tonic-clonic seizures (GTCS) is a group of epileptic disorders characterized by widespread generalized spike-and-waves discharges along with unresponsiveness and convulsions. Abnormal connectivity in the DMN is the common findings in children with generalized epilepsy. However, the neural mechanisms underlying the altered brain connectivity of DMN in children with GTCS remain unclear. The aim of the current study was to explore the temporal properties of functional connectivity states by dynamic functional connectivity (dFC) within the DMN of GTCS children. MethodsWe collected resting-state functional MRI data from 22 GTCS children and 29 age-matched healthy controls. Sliding window approach and k-mean clustering analysis were applied to analyze the dFC and identify transient states of the DMN. Furthermore, the relationship between the dynamic properties and clinical features was assessed. ResultsThe dFC analyses identified two reoccurring states: a more frequent and weak connected state (State 1) and a less frequent and strong connected state (State 2). Relative to the normal control, GTCS children spent more time in State 1 showing weak connections and spent less time in State 2 showing strong connections. Dynamic functional network connectivity strength within the DMN showed both increase and decrease in patient group. In addition, the changes of dynamic metric were found to be correlated with epilepsy duration. SignificantOur findings imply abnormal interactions and the state dynamics in DMN of the children with GTCS. These disruptions of temporal dynamic in DMN may provide significance for understanding the neural mechanism underlying the GTCS in children and suggest that dFC method can be considered as a valuable tool in children with epilepsy.

Altered resting state dynamic functional connectivity of amygdala subregions in patients with autism spectrum disorder: A multi-site fMRI study

BackgroundAutism spectrum disorder (ASD) is associated with altered brain connectivity. Previous studies have focused on the static functional connectivity pattern from amygdala subregions in ASD while ignoring its dynamics. Considering that dynamic functional connectivity (dFC) can provide different perspectives, the present study aims to investigate the dFC pattern of the amygdala subregions in ASD patients. MethodsData of 618 ASD patients and 836 typical controls (TCs) of 30 sites were obtained from the Autism Brain Imaging Data Exchange (ABIDE) database. The sliding window approach was applied to conduct seed-based dFC analysis. The seed regions were bilateral basolateral (BLA) and centromedial-superficial amygdala (CSA). A two-sample t-test was done at each site. Image-based meta-analysis (IBMA) based on the results from all sites was performed. Correlation analysis was conducted between the dFC values and the clinical scores. ResultsThe ASD patients showed lower dFC between the left BLA and the bilateral inferior temporal (ITG)/left superior frontal gyrus, between the right BLA and right ITG/right thalamus/left superior temporal gyrus, and between the right CSA and middle temporal gyrus. The ASD patients showed higher dFC between the left BLA and temporal lobe/right supramarginal gyrus, between the right BLA and left calcarine gyrus, and between the left CSA and left calcarine gyrus. Correlation analysis revealed that the symptom severity was positively correlated with the dFC between the bilateral BLA and ITG in ASD. ConclusionsAbnormal dFC of the specific amygdala subregions may provide new insights into the pathological mechanisms of ASD.

P.095 Smoking behaviour change is associated with altered functional brain connectivity in older adults

Background: Smoking is the leading cause of preventable morbidity worldwide and therefore developing effective smoking cessation strategies is a public health priority. However, what brain networks support maintenance of smoking cessation in the long term remains unexplored. Methods: We analyzed the baseline resting-state fMRI data acquired in 23 smokers (Mage = 61.52 ± 3.7) who were followed longitudinally in a cohort of cognitively normal older adults. Self-reported smoking status and amount were recorded at baseline and repeated after 4 years. We investigated the effect of smoking behaviour change on functional brain connectivity using seed-to-voxel approach. We examined a-priori regions of interest (ROIs) including the reward network (ventromedial prefrontal cortex (vMPFC) and ventral striatum) and the right insula. These ROIs are promising target mechanisms given prior behavioural research linking it to smoking cessation. Results: Our results revealed that reduced smoking was associated with reduced connectivity between ventral striatum and middle frontal gyrus and enhanced connectivity between right insula and middle temporal gyrus (voxel p &lt;0.001, cluster p&lt;0.05 FDR corrected). However, change in smoking did not reveal any significant effects in the vMPFC. Conclusions: Our findings suggest that successful smoking behaviour change is associated with altered reward network and insular functional connectivity in the long term.