Dynamic functional connectivity correlates of trait mindfulness in early adolescence.

Background: Trait mindfulness, the tendency to attend to present-moment experiences without judgement, is negatively correlated with adolescent anxiety and depression. Understanding the neural mechanisms underlying trait mindfulness may inform the neural basis of psychiatric disorders. However, few studies have identified brain connectivity states that correlate with trait mindfulness in adolescence, nor have they assessed the reliability of such states. Methods: To address this gap in knowledge, we rigorously assessed the reliability of brain states across 2 functional magnetic resonance imaging (fMRI) scan from 106 adolescents aged 12 to 15 (50% female). We performed both static and dynamic functional connectivity analyses and evaluated the test-retest reliability of how much time adolescents spent in each state. For the reliable states, we assessed associations with self-reported trait mindfulness. Results: Higher trait mindfulness correlated with lower anxiety and depression symptoms. Static functional connectivity (ICCs from 0.31-0.53) was unrelated to trait mindfulness. Among the dynamic brains states we identified, most were unreliable within individuals across scans. However, one state, an hyperconnected state of elevated positive connectivity between networks, showed good reliability (ICC=0.65). We found that the amount of time that adolescents spent in this hyperconnected state positively correlated with trait mindfulness. Conclusions: By applying dynamic functional connectivity analysis on over 100 resting-state fMRI scans, we identified a highly reliable brain state that correlated with trait mindfulness. The brain state may reflect a state of mindfulness, or awareness and arousal more generally, which may be more pronounced in those who are higher in trait mindfulness.

Abnormal resting-state hyperconnectivity in schizophrenia: A whole-head near-infrared spectroscopy study.

Near-infrared spectroscopy (NIRS) is a noninvasive functional neuroimaging modality that can detect changes in blood oxygenation levels by tracking cortical neural activity. We recorded the resting-state brain activity of 24 individuals with schizophrenia and 90 healthy controls for 8 min using a whole-head NIRS arrangement and then used partial correlation analysis to estimate the resting-state functional connectivity (RSFC) between 17 cortical regions. We found that the RSFC between the bilateral orbitofrontal cortices (OFCs) and between the right temporal and parietal lobes was significantly higher in patients with schizophrenia than in healthy controls. The RSFC between the bilateral OFCs was positively correlated with negative symptom severity, whereas the RSFC between the right temporal and parietal lobes was positively correlated with the chlorpromazine equivalent for antipsychotics prescribed to patients with schizophrenia. This finding was consistent with that for the RSFC calculated using the anterior 52-channel signals. Our results suggest that NIRS-based RSFC measurements have potential clinical applications.

Hippocampal Network Dysfunction in Early Psychosis: A 2-Year Longitudinal Study.

Background: Hippocampal abnormalities are among the most consistent findings in schizophrenia. Numerous studies have reported deficits in hippocampal volume, function, and connectivity in the chronic stage of illness. While hippocampal volume and function deficits are also present in the early stage of illness, there is mixed evidence of both higher and lower functional connectivity. Here, we use graph theory to test the hypothesis that hippocampal network connectivity is broadly lowered in early psychosis and progressively worsens over 2 years. Methods: We examined longitudinal resting-state functional connectivity in 140 participants (68 individuals in the early stage of psychosis, 72 demographically similar healthy control individuals). We used an anatomically driven approach to quantify hippocampal network connectivity at 2 levels: 1) a core hippocampal-medial temporal lobe cortex (MTLC) network; and 2) an extended hippocampal-cortical network. Group and time effects were tested in a linear mixed effects model. Results: Early psychosis patients showed elevated functional connectivity in the core hippocampal-MTLC network, but contrary to our hypothesis, did not show alterations within the broader hippocampal-cortical network. Hippocampal-MTLC network hyperconnectivity normalized longitudinally and predicted improvement in positive symptoms but was not associated with increasing illness duration. Conclusions: These results show abnormally elevated functional connectivity in a core hippocampal-MTLC network in early psychosis, suggesting that selectively increased hippocampal signaling within a localized cortical circuit may be a marker of the early stage of psychosis. Hippocampal-MTLC hyperconnectivity could have prognostic and therapeutic implications.

The Neurogenetics of Functional Connectivity Alterations in Autism: Insights From Subtyping in 657 Individuals.

BACKGROUND: There is little consensus and controversial evidence on anatomical alterations in the brains of people with autism spectrum disorder (ASD), due in part to the large heterogeneity present in ASD, which in turn is a major drawback for developing therapies. One strategy to characterize this heterogeneity in ASD is to cluster large-scale functional brain connectivity profiles. METHODS: A subtyping approach based on consensus clustering of functional brain connectivity patterns was applied to a population of 657 autistic individuals with quality-assured neuroimaging data. We then used high-resolution gene transcriptomic data to characterize the molecular mechanism behind each subtype by performing enrichment analysis of the set of genes showing a high spatial similarity with the profiles of functional connectivity alterations between each subtype and a group of typically developing control participants. RESULTS: Two major stable subtypes were found: subtype 1 exhibited hypoconnectivity (less average connectivity than typically developing control participants) and subtype 2, hyperconnectivity. The 2 subtypes did not differ in structural imaging metrics in any of the analyzed regions (68 cortical and 14 subcortical) or in any of the behavioral scores (including IQ, Autism Diagnostic Interview, and Autism Diagnostic Observation Schedule). Finally, only subtype 2, comprising about 43% of ASD participants, led to significant enrichments after multiple testing corrections. Notably, the dominant enrichment corresponded to excitation/inhibition imbalance, a leading well-known primary mechanism in the pathophysiology of ASD. CONCLUSIONS: Our results support a link between excitation/inhibition imbalance and functional connectivity alterations, but only in one ASD subtype, overall characterized by brain hyperconnectivity and major alterations in somatomotor and default mode networks.

Connectome-wide Mega-analysis Reveals Robust Patterns of Atypical Functional Connectivity in Autism.

BACKGROUND: Neuroimaging studies of functional connectivity (FC) in autism have been hampered by small sample sizes and inconsistent findings with regard to whether connectivity is increased or decreased in individuals with autism, whether these alterations affect focal systems or reflect a brain-wide pattern, and whether these are age and/or sex dependent. METHODS: The study included resting-state functional magnetic resonance imaging and clinical data from the EU-AIMS LEAP (European Autism Interventions Longitudinal European Autism Project) and the ABIDE (Autism Brain Imaging Data Exchange) 1 and 2 initiatives of 1824 (796 with autism) participants with an age range of 5-58 years. Between-group differences in FC were assessed, and associations between FC and clinical symptom ratings were investigated through canonical correlation analysis. RESULTS: Autism was associated with a brainwide pattern of hypo- and hyperconnectivity. Hypoconnectivity predominantly affected sensory and higher-order attentional networks and correlated with social impairments, restrictive and repetitive behavior, and sensory processing. Hyperconnectivity was observed primarily between the default mode network and the rest of the brain and between cortical and subcortical systems. This pattern was strongly associated with social impairments and sensory processing. Interactions between diagnosis and age or sex were not statistically significant. CONCLUSIONS: The FC alterations observed, which primarily involve hypoconnectivity of primary sensory and attention networks and hyperconnectivity of the default mode network and subcortex with the rest of the brain, do not appear to be age or sex dependent and correlate with clinical dimensions of social difficulties, restrictive and repetitive behaviors, and alterations in sensory processing. These findings suggest that the observed connectivity alterations are stable, trait-like features of autism that are related to the main symptom domains of the condition.

Altered connectivity of default mode and executive control networks among female patients with persistent post-concussion symptoms.

OBJECTIVE: To examine the roles of the default mode network (DMN) and executive control network (ECN) in prolonged recovery after mild traumatic brain injury (mTBI), and relationships with indices of white matter microstructural injury. METHODS: Seventeen mTBI patients with persistent symptoms were imaged an average of 21.5 months post-injury, along with 23 healthy controls. Resting-state functional magnetic resonance imaging (rs-fMRI) was used to evaluate functional connectivity (FC) of the DMN and ECN. Diffusion tensor imaging (DTI) quantified fractional anisotropy, along with mean, axial and radial diffusivity of white matter tracts. RESULTS: Compared to controls, patients with mTBI had increased functional connectivity of the DMN and ECN to brain regions implicated in salience and frontoparietal networks, and increased white matter diffusivity within the cerebrum and brainstem. Among the patients, FC was correlated with better neurocognitive test scores, while diffusivity was correlated with more severe self-reported symptoms. The FC and diffusivity values within abnormal brain regions were not significantly correlated. CONCLUSION: For female mTBI patients with prolonged symptoms, hyper-connectivity may represent a compensatory response that helps to mitigate the effects of mTBI on cognition. These effects are unrelated to indices of microstructural injury, which are correlated with symptom severity, suggesting that rs-fMRI and DTI may capture distinct aspects of pathophysiology.

Cerebellar Functional Dysconnectivity in Drug-Naïve Patients With First-Episode Schizophrenia.

BACKGROUND: Cerebellar functional dysconnectivity has long been implicated in schizophrenia. However, the detailed dysconnectivity pattern and its underlying biological mechanisms have not been well-charted. This study aimed to conduct an in-depth characterization of cerebellar dysconnectivity maps in early schizophrenia. STUDY DESIGN: Resting-state fMRI data were processed from 196 drug-naïve patients with first-episode schizophrenia and 167 demographically matched healthy controls. The cerebellum was parcellated into nine functional systems based on a state-of-the-art atlas, and seed-based connectivity for each cerebellar system was examined. The observed connectivity alterations were further associated with schizophrenia risk gene expressions using data from the Allen Human Brain Atlas. STUDY RESULTS: Overall, we observed significantly increased cerebellar connectivity with the sensorimotor cortex, default-mode regions, ventral part of visual cortex, insula, and striatum. In contrast, decreased connectivity was shown chiefly within the cerebellum, and between the cerebellum and the lateral prefrontal cortex, temporal lobe, and dorsal visual areas. Such dysconnectivity pattern was statistically similar across seeds, with no significant group by seed interactions identified. Moreover, connectivity strengths of hypoconnected but not hyperconnected regions were significantly correlated with schizophrenia risk gene expressions, suggesting potential genetic underpinnings for the observed hypoconnectivity. CONCLUSIONS: These findings suggest a common bidirectional dysconnectivity pattern across different cerebellar subsystems, and imply that such bidirectional alterations may relate to different biological mechanisms.

The Metaverse as a virtual form of data-driven smart urbanism: platformization and its underlying processes, institutional dimensions, and disruptive impacts.

The emerging phenomenon of platformization has given rise to what has been termed "platform society," a digitally connected world where platforms have penetrated the heart of urban societies-transforming social practices, disrupting social interactions and market relations, and affecting democratic processes. One of the recent manifestations of platformization is the Metaverse, a global platform whose data infrastructures, governance models, and economic processes are predicted to penetrate different urban sectors and spheres of urban life. The Metaverse is an idea of a hypothetical set of "parallel virtual worlds" that incarnate ways of living in believably virtual cities as an alternative to future data-driven smart cities. However, this idea has already raised concerns over what constitutes the global architecture of computer mediation underlying the Metaverse with regard to different forms of social life as well as social order. This study analyzes the core emerging trends enabling and driving data-driven smart cities and uses the outcome to devise a novel framework for the digital and computing processes underlying the Metaverse as a virtual form of data-driven smart cities. Further, it examines and discusses the risks and impacts of the Metaverse, paying particular attention to: platformization; the COVID-19 crisis and the ensuing non-spontaneous "normality" of social order; corporate-led technocratic governance; governmentality; privacy, security, and trust; and data governance. A thematic analysis approach is adopted to cope with the vast body of literature of various disciplinarities. The analysis identifies five digital and computing processes related to data-driven smart cities: digital instrumentation, digital hyper-connectivity, datafication, algorithmization, and platformization. The novelty of the framework derived based on thematic analysis lies in its essential processual digital and computing components and the way in which these are structured and integrated given their clear synergies as to enabling the functioning of the Metaverse towards potentially virtual cities. This study highlights how and why the identified digital and computing processes-as intricately interwoven with the entirety of urban ways of living-arouse contentions and controversies pertaining to society' public values. As such, it provides new insights into understanding the complex interplay between the Metaverse as a form of science and technology and the other dimensions of society. Accordingly, it contributes to the scholarly debates in the field of Science, Technology, and Society (STS) by highlighting the societal and ethical implications of the platformization of urban societies through the Metaverse.

Hyperconnected Openings Codified in a Max Tree Structure: An Application for Skull-Stripping in Brain MRI T1.

This article presents two procedures involving a maximal hyperconnected function and a hyperconnected lower leveling to segment the brain in a magnetic resonance imaging T1 weighted using new openings on a max-tree structure. The openings are hyperconnected and are viscous transformations. The first procedure considers finding the higher hyperconnected maximum by using an increasing criterion that plays a central role during segmentation. The second procedure utilizes hyperconnected lower leveling, which acts as a marker, controlling the reconstruction process into the mask. As a result, the proposal allows an efficient segmentation of the brain to be obtained. In total, 38 magnetic resonance T1-weighted images obtained from the Internet Brain Segmentation Repository are segmented. The Jaccard and Dice indices are computed, compared, and validated with the efficiency of the Brain Extraction Tool software and other algorithms provided in the literature.

Does central nervous system dysfunction underlie patellofemoral pain in young females? Examining brain functional connectivity in association with patient-reported outcomes.

Patellofemoral pain (PFP) is defined as retro- or peri-patellar knee pain without a clear structural abnormality. Unfortunately, many current treatment approaches fail to provide long-term pain relief, potentially due to an incomplete understanding of pain-disrupted sensorimotor dysfunction within the central nervous system. The purposes of this study were to evaluate brain functional connectivity in participants with and without PFP, and to determine the relationship between altered brain functional connectivity in association with patient-reported outcomes. Young female patients with PFP (n = 15; 14.3 ± 3.2 years) completed resting-state functional magnetic resonance imaging (rs-fMRI) and patient-reported outcome measures. Each patient with PFP was matched with two controls (n = 30, 15.5 ± 1.4 years) who also completed identical rs-fMRI testing. Six bilateral seeds important for pain and sensorimotor control were created, and seed-to-voxel analyses were conducted to compare functional connectivity between the two groups, as well as to determine the relationship between connectivity alterations and patient-reported outcomes. Relative to controls, patients with PFP exhibited altered functional connectivity between regions important for pain, psychological functioning, and sensorimotor control, and the connectivity alterations were related to perceived disability, dysfunction, and kinesiophobia. The present results support emergent evidence that PFP is not localized to structural knee dysfunction, but may actually be resultant to altered central neural processes. Clinical significance: These data provide potential neuro-therapeutic targets for novel therapies aimed to reorganize neural processes, improve neuromuscular function, and restore an active pain-free lifestyle in young females with PFP.

Detection of Mild Cognitive Impairment with MEG Functional Connectivity Using Wavelet-Based Neuromarkers.

Studies on developing effective neuromarkers based on magnetoencephalographic (MEG) signals have been drawing increasing attention in the neuroscience community. This study explores the idea of using source-based magnitude-squared spectral coherence as a spatial indicator for effective regions of interest (ROIs) localization, subsequently discriminating the participants with mild cognitive impairment (MCI) from a group of age-matched healthy control (HC) elderly participants. We found that the cortical regions could be divided into two distinctive groups based on their coherence indices. Compared to HC, some ROIs showed increased connectivity (hyper-connected ROIs) for MCI participants, whereas the remaining ROIs demonstrated reduced connectivity (hypo-connected ROIs). Based on these findings, a series of wavelet-based source-level neuromarkers for MCI detection are proposed and explored, with respect to the two distinctive ROI groups. It was found that the neuromarkers extracted from the hyper-connected ROIs performed significantly better for MCI detection than those from the hypo-connected ROIs. The neuromarkers were classified using support vector machine (SVM) and k-NN classifiers and evaluated through Monte Carlo cross-validation. An average recognition rate of 93.83% was obtained using source-reconstructed signals from the hyper-connected ROI group. To better conform to clinical practice settings, a leave-one-out cross-validation (LOOCV) approach was also employed to ensure that the data for testing was from a participant that the classifier has never seen. Using LOOCV, we found the best average classification accuracy was reduced to 83.80% using the same set of neuromarkers obtained from the ROI group with functional hyper-connections. This performance surpassed the results reported using wavelet-based features by approximately 15%. Overall, our work suggests that (1) certain ROIs are particularly effective for MCI detection, especially when multi-resolution wavelet biomarkers are employed for such diagnosis; (2) there exists a significant performance difference in system evaluation between research-based experimental design and clinically accepted evaluation standards.

Digital Twin for Automatic Transportation in Industry 4.0.

Industry 4.0 is the fourth industrial revolution consisting of the digitalization of processes facilitating an incremental value chain. Smart Manufacturing (SM) is one of the branches of the Industry 4.0 regarding logistics, visual inspection of pieces, optimal organization of processes, machine sensorization, real-time data adquisition and treatment and virtualization of industrial activities. Among these tecniques, Digital Twin (DT) is attracting the research interest of the scientific community in the last few years due to the cost reduction through the simulation of the dynamic behaviour of the industrial plant predicting potential problems in the SM paradigm. In this paper, we propose a new DT design concept based on external service for the transportation of the Automatic Guided Vehicles (AGVs) which are being recently introduced for the Material Requirement Planning satisfaction in the collaborative industrial plant. We have performed real experimentation in two different scenarios through the definition of an Industrial Ethernet platform for the real validation of the DT results obtained. Results show the correlation between the virtual and real experiments carried out in the two scenarios defined in this paper with an accuracy of 97.95% and 98.82% in the total time of the missions analysed in the DT. Therefore, these results validate the model created for the AGV navigation, thus fulfilling the objectives of this paper.

Ventromedial Prefrontal-Anterior Cingulate Hyperconnectivity and Resilience to Apathy in Traumatic Brain Injury.

Apathy is a common and impairing sequela of traumatic brain injury (TBI). Yet, little is known about the neural mechanisms determining in which patients apathy does or does not develop post-TBI. We aimed to elucidate the impact of TBI on motivational neural circuits and how this shapes apathy over the course of TBI recovery. Resting-state functional magnetic resonance imaging data were collected in patients with subacute mild TBI (n = 44), chronic mild-to-moderate TBI (n = 26), and nonbrain-injured control participants (CTRL; n = 28). We measured ventromedial prefrontal cortex (vmPFC) functional connectivity (FC) as a function of apathy, using an a priori vmPFC seed adopted from a motivated decision-making study in an independent TBI study cohort. Patients reported apathy using a well-validated tool for assaying apathy in TBI. The vmPFC-to-wholebrain FC was contrasted between groups, and we fit regression models with apathy predicting vmPFC FC. Subacute and chronic TBI caused increased apathy relative to CTRL, replicating previous work suggesting that apathy has an enduring impact in TBI. The vmPFC was functionally connected to the canonical default network, and this architecture did not differ between subacute TBI, chronic TBI, and CTRL groups. Critically, in TBI, increased apathy scores predicted decreased vmPFC-dorsal anterior cingulate cortex (dACC) FC. Last, we subdivided the TBI group based on patients above versus below the threshold for "clinically significant apathy," finding that TBI patients with clinically significant apathy demonstrated comparable vmPFC-dACC FC to CTRLs, whereas TBI patients with subthreshold apathy scores demonstrated vmPFC-dACC hyperconnectivity relative to both CTRLs and patients with clinically significant apathy. Post-TBI vmPFC-dACC hyperconnectivity may represent an adaptive compensatory response, helping to maintain motivation and enabling resilience to the development of apathy after neurotrauma. Given the role of vmPFC-dACC circuits in value-based decision making, rehabilitation strategies designed to improve this ability may help to reduce apathy and improve functional outcomes in TBI.

Hyperconnectivity of Self-Referential Network as a Predictive Biomarker of the Progression of Alzheimer's Disease.

BACKGROUND: Self-referential processing is associated with the progression of Alzheimer's disease (AD), and cerebrospinal fluid (CSF) proteins have become accepted biomarkers of AD. OBJECTIVE: Our objective in this study was to focus on the relationships between the self-referential network (SRN) and CSF pathology in AD-spectrum patients. METHODS: A total of 80 participants, including 20 cognitively normal, 20 early mild cognitive impairment (EMCI), 20 late MCI (LMCI), and 20 AD, were recruited for this study. Independent component analysis was used to explore the topological SRN patterns, and the abnormalities of this network were identified at different stages of AD. Finally, CSF pathological characteristics (i.e., CSF Aß, t-tau, and p-tau) that affected the abnormalities of the SRN were further determined during the progression of AD. RESULTS: Compared to cognitively normal subjects, AD-spectrum patients (i.e., EMCI, LMCI, and AD) showed a reversing trend toward an association between CSF pathological markers and the abnormal SRN occurring during the progression of AD. However, a certain disease state (i.e., the present LMCI) with a low concentration of CSF tau could evoke more hyperconnectivity of the SRN than other patients with progressively increasing concentrations of CSF tau (i.e., EMCI and AD), and this fluctuation of CSF tau was more sensitive to the hyperconnectivity of the SRN than the dynamic changes of CSF Aß. CONCLUSION: The integrity of the SRN was closely associated with CSF pathological characteristics, and these findings support the view that the hyperconnectivity of the SRN will play an important role in monitoring the progression of the pre-dementia state to AD.

Inhaled Cannabis Suppresses Chemotherapy-Induced Neuropathic Nociception by Decoupling the Raphe Nucleus: A Functional Imaging Study in Rats.

BACKGROUND: Efficacy of inhaled cannabis for treating pain is controversial. Effective treatment for chemotherapy-induced neuropathy represents an unmet medical need. We hypothesized that cannabis reduces neuropathic pain by reducing functional coupling in the raphe nuclei. METHODS: We assessed the impact of inhalation of vaporized cannabis plant (containing 10.3% Δ9-tetrahydrocannabinol/0.05% cannabidiol) or placebo cannabis on brain resting-state blood oxygen level-dependent functional connectivity and pain behavior induced by paclitaxel in rats. Rats received paclitaxel to produce chemotherapy-induced peripheral neuropathy or its vehicle. Behavioral and imaging experiments were performed after neuropathy was established and stable. Images were registered to, and analyzed using, a 3D magnetic resonance imaging rat atlas providing site-specific data on more than 168 different brain areas. RESULTS: Prior to vaporization, paclitaxel produced cold allodynia. Inhaled vaporized cannabis increased cold withdrawal latencies relative to prevaporization or placebo cannabis, consistent with Δ9-tetrahydrocannabinol-induced antinociception. In paclitaxel-treated rats, the midbrain serotonergic system, comprising the dorsal and median raphe, showed hyperconnectivity to cortical, brainstem, and hippocampal areas, consistent with nociceptive processing. Inhalation of vaporized cannabis uncoupled paclitaxel-induced hyperconnectivity patterns. No such changes in connectivity or cold responsiveness were observed following placebo cannabis vaporization. CONCLUSIONS: Inhaled vaporized cannabis plant uncoupled brain resting-state connectivity in the raphe nuclei, normalizing paclitaxel-induced hyperconnectivity to levels observed in vehicle-treated rats. Inhaled vaporized cannabis produced antinociception in both paclitaxel- and vehicle-treated rats. Our study elucidates neural circuitry implicated in the therapeutic effects of Δ9-tetrahydrocannabinol and supports a role for functional imaging studies in animals in guiding indications for future clinical trials.

Cortico-Cerebellar Hyper-Connections and Reduced Purkinje Cells Behind Abnormal Eyeblink Conditioning in a Computational Model of Autism Spectrum Disorder.

Empirical evidence suggests that children with autism spectrum disorder (ASD) show abnormal behavior during delay eyeblink conditioning. They show a higher conditioned response learning rate and earlier peak latency of the conditioned response signal. The neuronal mechanisms underlying this autistic behavioral phenotype are still unclear. Here, we use a physiologically constrained spiking neuron model of the cerebellar-cortical system to investigate which features are critical to explaining atypical learning in ASD. Significantly, the computer simulations run with the model suggest that the higher conditioned responses learning rate mainly depends on the reduced number of Purkinje cells. In contrast, the earlier peak latency mainly depends on the hyper-connections of the cerebellum with sensory and motor cortex. Notably, the model has been validated by reproducing the behavioral data collected from studies with real children. Overall, this article is a starting point to understanding the link between the behavioral and neurobiological basis in ASD learning. At the end of the paper, we discuss how this knowledge could be critical for devising new treatments.

Flattened Structural Network Changes and Association of Hyperconnectivity With Symptom Severity in 2-7-Year-Old Children With Autism.

Understanding the brain differences present at the earliest possible diagnostic age for autism spectrum disorder (ASD) is crucial for delineating the underlying neuropathology of the disorder. However, knowledge of brain structural network changes in the early important developmental period between 2 and 7 years of age is limited in children with ASD. In this study, we aimed to fill the knowledge gap by characterizing age-related brain structural network changes in ASD from 2 to 7 years of age, and identify sensitive network-based imaging biomarkers that are significantly correlated with the symptom severity. Diffusion MRI was acquired in 30 children with ASD and 21 typically developmental (TD) children. With diffusion MRI and quantified clinical assessment, we conducted network-based analysis and correlation between graph-theory-based measurements and symptom severity. Significant age-by-group interaction was found in global network measures and nodal efficiencies during the developmental period of 2-7 years old. Compared with significant age-related growth of the structural network in TD, relatively flattened maturational trends were observed in ASD. Hyper-connectivity in the structural network with higher global efficiency, global network strength, and nodal efficiency were observed in children with ASD. Network edge strength in ASD also demonstrated hyper-connectivity in widespread anatomical connections, including those in default-mode, frontoparietal, and sensorimotor networks. Importantly, identified higher nodal efficiencies and higher network edge strengths were significantly correlated with symptom severity in ASD. Collectively, structural networks in ASD during this early developmental period of 2-7 years of age are characterized by hyper-connectivity and slower maturation, with aberrant hyper-connectivity significantly correlated with symptom severity. These aberrant network measures may serve as imaging biomarkers for ASD from 2 to 7 years of age.

Hyperconnectivity in Dementia Is Early and Focal and Wanes with Progression.

We investigated in a longitudinal multicenter cohort study functional cortical connectivity changes along the course of frontotemporal dementia (FTD) and Alzheimer's disease (AD) from the prodromal stage of the diseases. Electroencephalography (EEG) was recorded in 18 FTD and 18 AD patients at the prodromal stage of dementia, at dementia onset, and 3 years after dementia onset. Twenty healthy controls (HC) underwent EEG recordings at the same time interval as the patients. Mutual information (MI) analysis measured the strength of functional network connectivity. FTD and AD patients showed greater MI at the prodromal stage of dementia (FTD vs. HC P = 2 × 10-8; AD vs. HC P = 4 × 10-3). Local connectivity was higher in left and right frontal areas of FTD (P = 7 × 10-5 and 0.03) and in left and right posterior areas in AD (P = 3 × 10-5 and 5 × 10-5) versus HC. We showed cortical hyperconnectivity at the prodromal stage of dementia in areas involved in the specific pathological process of FTD (frontal regions) and AD (posterior regions). Hyperconnectivity disappeared during follow-up, thus suggesting that it is an early electrophysiological feature of dementia, potentially useful to identify prodromal FTD and AD.

Acting Before; A Combined Strategy to Counteract the Onset and Progression of Dementia.

Brain aging and aging-related neurodegenerative disorders are posing a significant challenge for health systems worldwide. To date, most of the therapeutic efforts aimed at counteracting dementiarelated behavioral and cognitive impairment have been focused on addressing putative determinants of the disease, such as ß-amyloid or tau. In contrast, relatively little attention has been paid to pharmacological interventions aimed at restoring or promoting the synaptic plasticity of the aging brain. The review will explore and discuss the most recent molecular, structural/functional, and behavioral evidence that supports the use of non-pharmacological approaches as well as cognitive-enhancing drugs to counteract brain aging and early-stage dementia.

Human Neural Stem Cell Induced Functional Network Stabilization After Cortical Stroke: A Longitudinal Resting-State fMRI Study in Mice.

Most stroke studies dealing with functional deficits and assessing stem cell therapy produce extensive hemispheric damage and can be seen as a model for severe clinical strokes. However, mild strokes have a better prospect for functional recovery. Recently, anatomic and behavioral changes have been reported for distal occlusion of the middle cerebral artery (MCA), generating a well-circumscribed and small cortical lesion, which can thus be proposed as mild to moderate cortical stroke. Using this cortical stroke model of moderate severity in the nude mouse, we have studied the functional networks with resting-state functional magnetic resonance imaging (fMRI) for 12 weeks following stroke induction. Further, human neural stem cells (hNSCs) were implanted adjacent to the ischemic lesion, and the stable graft vitality was monitored with bioluminescence imaging (BLI). Differentiation of the grafted neural stem cells was analyzed by immunohistochemistry and by patch-clamp electrophysiology. Following stroke induction, we found a pronounced and continuously rising hypersynchronicity of the sensorimotor networks including both hemispheres, in contrast to the severe stroke filament model where profound reduction of the functional connectivity had been reported by us earlier. The vitality of grafted neural stem cells remained stable throughout the whole 12 weeks observation period. In the stem cell treated animals, functional connectivity did not show hypersynchronicity but was globally slightly reduced below baseline at 2 weeks post-stroke, normalizing thereafter completely. Our resting-state fMRI (rsfMRI) studies on cortical stroke reveal for the first time a hypersynchronicity of the functional brain networks. This hypersynchronicity appears as a hallmark of mild cortical strokes, in contrast to severe strokes with striatal involvement where exclusively hyposynchronicity has been reported. The effect of the stem cell graft was an early and persistent normalization of the functional sensorimotor networks across the whole brain. These novel functional results may help interpret future outcome investigations after stroke and demonstrate the highly promising potential of stem cell treatment for functional outcome improvement after stroke.