Changes In Network Connectivity Research Articles

Neural effects of childhood maltreatment on dynamic large-scale brain networks in major depressive disorder

Emerging evidence suggests that childhood maltreatment (CM) alters trajectories of brain development to affect network architecture and is a risk factor for the development and maintenance of depression. The current study aimed to explore the association between CM and depressive severity on the large-scale resting-state networks (RSNs) level in major depressive disorder (MDD) patients and explored the network basis of clinical symptoms. 42 healthy controls without childhood maltreatment, 13 healthy controls with CM, 35 MDD without CM and 50 MDD with CM were included in the study population. Group differences in ten large-scale RSNs, associations between CM and depressive symptom dimensions and network variables were tested. And we explored whether symptom-related networks might discriminate between the four groups. We found one-versus-all-others-network showed an inverted U-shaped curve across groups. Network variables were significantly associated with Hamilton Depression Scale subscores and Childhood Trauma Questionnaire subscores. Different symptoms showed different imaging patterns, and overlapping connections of patterns had better ability to distinguish groups. Our findings suggest that CM could lead to significant changes in both network measures and connections in healthy individuals and MDD. These results deepen our understanding of the neuroimaging mechanisms of CM and MDD.

Modulation of resting-state functional connectivity in default mode network is associated with the long-term treatment outcome in major depressive disorder.

Treatment non-response and recurrence are the main sources of disease burden in major depressive disorder (MDD). However, little is known about its neurobiological mechanism concerning the brain network changes accompanying pharmacotherapy. The present study investigated the changes in the intrinsic brain networks during 6-month antidepressant treatment phase associated with the treatment response and recurrence in MDD. Resting-state functional magnetic resonance imaging was acquired from untreated patients with MDD and healthy controls at baseline. The patients' depressive symptoms were monitored by using the Hamilton Rating Scale for Depression (HAMD). After 6 months of antidepressant treatment, patients were re-scanned and followed up every 6 months over 2 years. Traditional statistical analysis as well as machine learning approaches were conducted to investigate the longitudinal changes in macro-scale resting-state functional network connectivity (rsFNC) strength and micro-scale resting-state functional connectivity (rsFC) associated with long-term treatment outcome in MDD. Repeated measures of the general linear model demonstrated a significant difference in the default mode network (DMN) rsFNC change before and after the 6-month antidepressant treatment between remitters and non-remitters. The difference in the rsFNC change over the 6-month antidepressant treatment between recurring and stable MDD was also specific to DMN. Machine learning analysis results revealed that only the DMN rsFC change successfully distinguished non-remitters from the remitters at 6 months and recurring from stable MDD during the 2-year follow-up. Our findings demonstrated that the intrinsic DMN connectivity could be a unique and important target for treatment and recurrence prevention in MDD.

Dynamic connectivity patterns of resting-state brain functional networks in healthy individuals after acute alcohol intake.

AimsCurrently, there are only a few studies concerning brain functional alterations after acute alcohol exposure, and the majority of existing studies attach more importance to the spatial properties of brain function without considering the temporal properties. The current study adopted sliding window to investigate the resting-state brain networks in healthy volunteers after acute alcohol intake and to explore the dynamic changes in network connectivity.Materials and methodsTwenty healthy volunteers were enrolled in this study. Blood-oxygen-level-dependent (BOLD) data prior to drinking were obtained as control, while that 0.5 and 1 h after drinking were obtained as the experimental group. Reoccurring functional connectivity patterns (states) were determined following group independent component analysis (ICA), sliding window analysis and k-means clustering. Between-group comparisons were performed with respect to the functional connectivity states fractional windows, mean dwell time, and the number of transitions.ResultsThree optimal functional connectivity states were identified. The fractional windows and mean dwell time of 0.5 h group and 1 h group increased in state 3, while the fraction window and mean dwell time of 1 h group decreased in state 1. State 1 is characterized by strong inter-network connections between basal ganglia network (BGN) and sensorimotor network (SMN), BGN and cognitive executive network (CEN), and default mode network (DMN) and visual network (VN). However, state 3 is distinguished by relatively weak intra-network connections in SMN, VN, CEN, and DMN. State 3 was thought to be a characteristic connectivity pattern of the drunk brain. State 1 was believed to represent the brain’s main connection pattern when awake. Such dynamic changes in brain network connectivity were consistent with participants’ subjective feelings after drinking.ConclusionThe current study reveals the dynamic change in resting-state brain functional network connectivity before and after acute alcohol intake. It was discovered that there might be relatively independent characteristic functional network connection patterns under intoxication, and the corresponding patterns characterize the clinical manifestations of volunteers. As a valuable imaging biomarker, dynamic functional network connectivity (dFNC) offers a new approach and basis for further explorations on brain network alterations after alcohol consumption and the alcohol-related mechanisms for neurological damage.

Ability of an altered functional coupling between resting-state networks to predict behavioral outcomes in subcortical ischemic stroke: A longitudinal study.

Stroke can be viewed as an acute disruption of an individual’s connectome caused by a focal or widespread loss of blood flow. Although individuals exhibit connectivity changes in multiple functional networks after stroke, the neural mechanisms that underlie the longitudinal reorganization of the connectivity patterns are still unclear. The study aimed to determine whether brain network connectivity patterns after stroke can predict longitudinal behavioral outcomes. Nineteen patients with stroke with subcortical lesions underwent two sessions of resting-state functional magnetic resonance imaging scanning at a 1-month interval. By independent component analysis, the functional connectivity within and between multiple brain networks (including the default mode network, the dorsal attention network, the limbic network, the visual network, and the frontoparietal network) was disrupted after stroke and partial recovery at the second time point. Additionally, regression analyses revealed that the connectivity between the limbic and dorsal attention networks at the first time point showed sufficient reliability in predicting the clinical scores (Fugl-Meyer Assessment and Neurological Deficit Scores) at the second time point. The overall findings suggest that functional coupling between the dorsal attention and limbic networks after stroke can be regarded as a biomarker to predict longitudinal clinical outcomes in motor function and the degree of neurological functional deficit. Overall, the present study provided a novel opportunity to improve prognostic ability after subcortical strokes.

Variations in sexual network connectivity may explain dramatic variations in sexually transmitted infection prevalence between populations and over time: a selected four-country analysis.

Background: The incidence of sexually transmitted infections (STIs) has been noted to vary dramatically between population groups and over time. Here, the hypothesis that changes in network connectivity underpin these changes is explored. Methods: The incidence/prevalence estimates of HIV, herpes simplex virus-2, syphilis, chlamydia, and gonorrhoea, as well as two markers of sexual network connectivity (partner concurrency and multiple partnering) by ethnic group and sexual orientation in Kenya, South Africa, the United Kingdom (UK) and the United States (USA) were extracted from published studies. Pearson's correlation was used to test the association between the markers of network connectivity and the incidence/prevalence of these five STIs. A literature review was performed to evaluate the possible causes of the increases and decreases in syphilis incidence over the past 60 years. Results: In each country, the five STIs were found to cluster in particular ethnic groups and sexual orientations and to be positively associated with the two markers of network connectivity. Syphilis incidence in the UK and USA was found to increase dramatically in the 1960s/1970s, decline in the 1980s and again increase in the late 1990s. These changes took place predominantly in men who have sex with men, and were preceded by corresponding changes in network connectivity. The large decline in antenatal syphilis prevalence in Kenya and South Africa in the 1990s were likewise preceded by declines in network connectivity. Conclusions: Although other explanatory variables are not controlled for, the present analysis is compatible with the hypothesis that differential network connectivity is a parsimonious explanation for variations in STI incidence over time and between populations.

Variations in sexual network connectivity may explain dramatic variations in sexually transmitted infection prevalence between populations and over time: a selected four-country analysis

Background: The incidence of sexually transmitted infections (STIs) has been noted to vary dramatically between population groups and over time. Here, the hypothesis that changes in network connectivity underpin these changes is explored. Methods: The incidence/prevalence estimates of HIV, herpes simplex virus-2, syphilis, chlamydia, and gonorrhoea, as well as two markers of sexual network connectivity (partner concurrency and multiple partnering) by ethnic group and sexual orientation in Kenya, South Africa, the United Kingdom (UK) and the United States (USA) were extracted from published studies. Pearson’s correlation was used to test the association between the markers of network connectivity and the incidence/prevalence of these five STIs. A literature review was performed to evaluate the possible causes of the increases and decreases in syphilis incidence over the past 60 years. Results: In each country, the five STIs were found to cluster in particular ethnic groups and sexual orientations and to be positively associated with the two markers of network connectivity. Syphilis incidence in the UK and USA was found to increase dramatically in the 1960s/1970s, decline in the 1980s and again increase in the late 1990s. These changes took place predominantly in men who have sex with men, and were preceded by corresponding changes in network connectivity. The large decline in antenatal syphilis prevalence in Kenya and South Africa in the 1990s were likewise preceded by declines in network connectivity. Conclusions: Although other explanatory variables are not controlled for, the present analysis is compatible with the hypothesis that differential network connectivity is a parsimonious explanation for variations in STI incidence over time and between populations.

The correlations between kinematic profiles and cerebral hemodynamics suggest changes of motor coordination in single and bilateral finger movement.

ObjectiveThe correlation between the performance of coordination movement and brain activity is still not fully understood. The current study aimed to identify activated brain regions and brain network connectivity changes for several coordinated finger movements with different difficulty levels and to correlate the brain hemodynamics and connectivity with kinematic performance.MethodsTwenty-one right-dominant-handed subjects were recruited and asked to complete circular motions of single and bilateral fingers in the same direction (in-phase, IP) and in opposite directions (anti-phase, AP) on a plane. Kinematic data including radius and angular velocity at each task and synchronized blood oxygen concentration data using functional near-infrared spectroscopy (fNIRS) were recorded covering six brain regions including the prefrontal cortex, motor cortex, and occipital lobes. A general linear model was used to locate activated brain regions, and changes compared with baseline in blood oxygen concentration were used to evaluate the degree of brain region activation. Small-world properties, clustering coefficients, and efficiency were used to measure information interaction in brain activity during the movement.ResultIt was found that the radius error of the dominant hand was significantly lower than that of the non-dominant hand (p < 0.001) in both clockwise and counterclockwise movements. The fNIRS results confirmed that the contralateral brain region was activated during single finger movement and the dominant motor area was activated in IP movement, while both motor areas were activated simultaneously in AP movement. The Δhbo were weakly correlated with radius errors (p = 0.002). Brain information interaction in IP movement was significantly larger than that from AP movement in the brain network (p < 0.02) in the right prefrontal cortex. Brain activity in the right motor cortex reduces motor performance (p < 0.001), while the right prefrontal cortex region promotes it (p < 0.05).ConclusionOur results suggest there was a significant correlation between motion performance and brain activation level, as well as between motion deviation and brain functional connectivity. The findings may provide a basis for further exploration of the operation of complex brain networks.

The role of attentional control over interference in minor hallucinations in Parkinson’s disease

BackgroundMinor hallucinations in Parkinson's disease are associated with connectivity changes in attentional networks and increased risk of structured hallucinations. However, the clinical translation of these abnormalities in attention processes is not well-defined, and commonly used neuropsychological tests are not able to detect significant deficits in Parkinson's disease patients with isolated minor hallucinations. ObjectivesTo analyze the behavioral and electrophysiological correlates of minor hallucinations in Parkinson's disease during an attentional task assessing response inhibition and interference control. MethodsFifty-five non-demented Parkinson's disease patients with (PD-mH; n = 27) and without minor hallucinations (PD-NH; n = 28) were included in the analysis. An Ericksen flanker task was performed to compare the effect of presenting congruent and incongruent stimuli on accuracy, reaction times and stimulus-locked event-related potentials morphology. ResultsAlthough both groups showed equivalent performance in a standard neuropsychological assessment, in the flanker task accuracy rates were lower in the PD-mH group in incongruent trials (p = 0.005). In the event-related potentials, PD-mH patients showed increased amplitude of the N2 at Fz [t(53); p < 0.05] and decreased amplitude of the P300 at Pz [t(53); p < 0.05] for the incongruent trials. ConclusionsParkinson's disease patients with isolated minor hallucinations were more susceptible to interference mediated by irrelevant stimuli and had less cognitive control for suppressing these interferences. The failure of these systems could precipitate the intrusion and overrepresentation of peripheral irrelevant stimuli perceived as minor hallucinations. The Ericksen flanker task could be used as a sensitive clinical marker of the attentional defects leading to hallucinations in Parkinson's disease.

Multiple spatial scale mapping of time-resolved brain network reconfiguration during evoked pain in patients with rheumatoid arthritis.

Functional brain networks and the perception of pain can fluctuate over time. However, how the time-dependent reconfiguration of functional brain networks contributes to chronic pain remains largely unexplained. Here, we explored time-varying changes in brain network integration and segregation during pain over a disease-affected area (joint) compared to a neutral site (thumbnail) in 28 patients with rheumatoid arthritis (RA) in comparison with 22 healthy controls (HC). During functional magnetic resonance imaging, all subjects received individually calibrated pain pressures corresponding to visual analog scale 50 mm at joint and thumbnail. We implemented a novel approach to track changes of task-based network connectivity over time. Within this framework, we quantified measures of integration (participation coefficient, PC) and segregation (within-module degree z-score). Using these network measures at multiple spatial scales, both at the level of single nodes (brain regions) and communities (clusters of nodes), we found that PC at the community level was generally higher in RA patients compared to HC during and after painful pressure over the inflamed joint and corresponding site in HC. This shows that all brain communities integrate more in RA patients than in HC for time points following painful stimulation to a disease-relevant body site. However, the elevated community-related integration seen in patients appeared to not pertain uniquely to painful stimulation at the inflamed joint, but also at the neutral thumbnail, as integration and segregation at the community level did not differ across body sites in patients. Moreover, there was no specific nodal contribution to brain network integration or segregation. Altogether, our findings indicate widespread and persistent changes in network interaction in RA patients compared to HC in response to painful stimulation.

Network analysis of preictal iEEG reveals changes in network structure preceding seizure onset

Seizures likely result from aberrant network activity and synchronization. Changes in brain network connectivity may underlie seizure onset. We used a novel method of rapid network model estimation from intracranial electroencephalography (iEEG) data to characterize pre-ictal changes in network structure prior to seizure onset. We analyzed iEEG data from 20 patients from the iEEG.org database. Using 10 s epochs sliding by 1 s intervals, a multiple input, single output (MISO) state space model was estimated for each output channel and time point with all other channels as inputs, generating sequential directed network graphs of channel connectivity. These networks were assessed using degree and betweenness centrality. Both degree and betweenness increased at seizure onset zone (SOZ) channels 37.0 ± 2.8 s before seizure onset. Degree rose in all channels 8.2 ± 2.2 s prior to seizure onset, with increasing connections between the SOZ and surrounding channels. Interictal networks showed low and stable connectivity. A novel MISO model-based network estimation method identified changes in brain network structure just prior to seizure onset. Increased connectivity was initially isolated within the SOZ and spread to non-SOZ channels before electrographic seizure onset. Such models could help confirm localization of SOZ regions.

Mindfulness Training Changes Brain Dynamics During Depressive Rumination: A Randomized Controlled Trial

BackgroundDepression is a leading cause of disability worldwide and its prevalence is on the rise. One of the most debilitating aspects of depression is the dominance and persistence of depressive rumination, a state of mind that is linked to onset and recurrence of depression. Mindfulness meditation trains adaptive attention regulation and present-moment embodied awareness, skills that may be particularly useful during depressive mind states characterized by negative ruminative thoughts. MethodsIn a randomized controlled functional magnetic resonance imaging study (N = 80), we looked at the neurocognitive mechanisms behind mindfulness-based cognitive therapy (n = 50) for recurrent depression compared with treatment as usual (n = 30) across experimentally induced states of rest, mindfulness practice and rumination, and the relationship with dispositional psychological processes. ResultsMindfulness-based cognitive therapy compared with treatment as usual led to decreased salience network connectivity to the lingual gyrus during a ruminative state, and this change in salience network connectivity mediated improvements in the ability to sustain and control attention to body sensations. ConclusionsThese findings showed that a clinically effective mindfulness intervention modulates neurocognitive functioning during depressive rumination and the ability to sustain attention to the body.

Evidence for preserved insulin responsiveness in the aging rat brain

Insulin appears to exert salutary effects in the central nervous system (CNS). Thus, brain insulin resistance has been proposed to play a role in brain aging and dementia but is conceptually complex and unlikely to fit classic definitions established in peripheral tissues. Thus, we sought to characterize brain insulin responsiveness in young (4–5 months) and old (24 months) FBN male rats using a diverse set of assays to determine the extent to which insulin effects in the CNS are impaired with age. When performing hyperinsulinemic-euglycemic clamps in rats, intracerebroventricular (ICV) infusion of insulin in old animals improved peripheral insulin sensitivity by nearly two-fold over old controls and comparable to young rats, suggesting preservation of this insulin-triggered response in aging per se (p < 0.05). We next used an imaging-based approach by comparing ICV vehicle versus insulin and performed resting state functional magnetic resonance imaging (rs-fMRI) to evaluate age- and insulin-related changes in network connectivity within the default mode network. In aging, lower connectivity between the mesial temporal (MT) region and other areas, as well as reduced MT signal complexity, was observed in old rats, which correlated with greater cognitive deficits in old. Despite these stark differences, ICV insulin failed to elicit any significant alteration to the BOLD signal in young rats, while a significant deviation of the BOLD signal was observed in older animals, characterized by augmentation in regions of the septal nucleus and hypothalamus, and reduction in thalamus and nucleus accumbens. In contrast, ex vivo stimulation of hippocampus with 10 nM insulin revealed increased Akt activation in young (p < 0.05), but not old rats. Despite similar circulating levels of insulin and IGF-1, cerebrospinal fluid concentrations of these ligands were reduced with age. Thus, these data highlight the complexity of capturing brain insulin action and demonstrate preserved or heightened brain responses to insulin with age, despite dampened canonical signaling, thereby suggesting impaired CNS input of these ligands may be a feature of reduced brain insulin action, providing further rationale for CNS replacement strategies.

Critical Percolation Threshold for Solvation Site Connectivity in Polymer Electrolytes Mixtures

To address the tradeoff between mechanical strength and Li+ conductivity in Poly(Ethylene Oxide) (PEO)-based electrolytes, a rigid nonconductive polymer is frequently added to the electrolyte via blending or copolymerization. The ionic conductivity of mixed PEO electrolytes is generally lower than that of unmixed PEO electrolytes. The suppressed ionic conductivity is attributed to the reduced segmental mobility and connectivity of the conductive PEO cites. Most experimental systems make it difficult to decouple the two mechanisms and accurately examine their impact on conductivity. We compare two symmetric polymer mixtures (50:50 wt%): a miscible polymer blend PEO/PMMA and a disordered block copolymer (BCP) PEO-b-PMMA, both with the same amount of Li salt. Because their chemical and physical properties are the same, changes in ionic conductivity can be attributed solely to local changes in PEO network connectivity. We discover that the mixtures' immediate Li+ solvation sites (<5 Å) are identical to those of unmixed PEO electrolytes. The presence of non-conducting PMMA near the PEO, on the other hand, causes local concentration changes at longer range scales. The BCP is more mixed than the blend electrolyte at these length scales, resulting in a factor of two drop in conductivity. To that end, we propose a quantitative computational model that considers Li+ transport within and across PEO clusters at the appropriate length scales. This new understanding of network connectivity in polymer electrolyte mixtures is critical for the design of future multiphase polymer electrolyte systems.

Implanting intracranial electrodes does not affect spikes or network connectivity in nearby or connected brain regions.

To determine the effect of implanting electrodes on electrographic features of nearby and connected brain regions in patients with drug-resistant epilepsy, we analyzed intracranial EEG recordings from 10 patients with drug-resistant epilepsy who underwent implant revision (placement of additional electrodes) during their hospitalization. We performed automated spike detection and measured EEG functional networks. We analyzed the original electrodes that remained in place throughout the full EEG recording, and we measured the change in spike rates and network connectivity in these original electrodes in response to implanting new electrodes. There was no change in overall spike rate pre- to post-implant revision (t(9) = 0.1, p = 0.95). The peri-revision change in the distribution of spike rate and connectivity across electrodes was no greater than chance (Monte Carlo method, spikes: p = 0.40, connectivity: p = 0.42). Electrodes closer to or more functionally connected to the revision site had no greater change in spike rate or connectivity than more distant or less connected electrodes. Changes in electrographic features surrounding electrode implantation are no greater than baseline fluctuations occurring throughout the intracranial recording. These findings argue against an implant effect on spikes or network connectivity in nearby or connected brain regions.

Static Analysis and Optimization of Voltage and Reactive Power Regulation Systems in the HV/MV Substation with Electronic Transformer Tap-Changers

The quality of electricity is a very important indicator. The durability and reliable operation of all connected devices depend on the quality of the network voltage. Rapid changes in loads, changes in network connections and the presence of uncontrolled energy sources require the development of new voltage regulation systems. This requires voltage regulation systems capable of responding quickly to sudden voltage changes. In substations with control transformers, it is possible thanks to the use of semiconductor tap changers. Moreover, voltage regulation and reactive power compensation systems should be built as one system. This is due to the close dependence of voltage and reactive power in the network node. Therefore, it was proposed to use artificial intelligence methods to build a new voltage regulation and reactive power compensation system using all measurement voltages of network nodes. In the first stage of the research, active and reactive powers, as well as the voltage of the reference node, were selected for 6420 periods of the mains voltage. The simulation results were compared for the classic voltage regulation system with semiconductor tap changers and the evolution algorithm based on voltage measurements from the entire MV network. A significant improvement in the quality of voltage regulation with the use of an evolutionary algorithm was demonstrated. Then, a second set of input data with increased values of reactive power was generated. The results of the evolutionary algorithm after the application of the classic, independent reactive power compensation system and two-criteria optimization were compared. It has been shown that only the two-criteria optimization algorithm keeps both |tgφ| within the acceptable range and the quality of voltage regulation is the best. The article compares different working algorithms for semiconductor tap changers.

Neurofilament light-associated connectivity in young-adult Huntington's disease is related to neuronal genes.

Upregulation of functional network connectivity in the presence of structural degeneration is seen in the premanifest stages of Huntington's disease (preHD) 10-15 years from clinical diagnosis. However, whether widespread network connectivity changes are seen in gene carriers much further from onset has yet to be explored. We characterized functional network connectivity throughout the brain and related it to a measure of disease pathology burden (CSF neurofilament light, NfL) and measures of structural connectivity in asymptomatic gene carriers, on average 24 years from onset. We related these measurements to estimates of cortical and subcortical gene expression. We found no overall differences in functional (or structural) connectivity anywhere in the brain comparing control and preHD participants. However, increased functional connectivity, particularly between posterior cortical areas, correlated with increasing CSF NfL level in preHD participants. Using the Allen Human Brain Atlas and expression-weighted cell-type enrichment analysis, we demonstrated that this functional connectivity upregulation occurred in cortical regions associated with regional expression of genes specific to neuronal cells. This relationship was validated using single-nucleus RNAseq data from post-mortem Huntington's disease and control brains showing enrichment of neuronal-specific genes that are differentially expressed in Huntington's disease. Functional brain networks in asymptomatic preHD gene carriers very far from disease onset show evidence of upregulated connectivity correlating with increased disease burden. These changes occur among brain areas that show regional expression of genes specific to neuronal GABAergic and glutamatergic cells.

Effect of Rehabilitation on Brain Functional Connectivity in a Stroke Patient Affected by Conduction Aphasia

Stroke is a medical condition that affects the brain and represents a leading cause of death and disability. Associated with drug therapy, rehabilitative treatment is essential for promoting recovery. In the present work, we report an EEG-based study concerning a left ischemic stroke patient affected by conduction aphasia. Specifically, the objective is to compare the brain functional connectivity before and after an intensive rehabilitative treatment. The analysis was performed by means of local and global efficiency measures related to the execution of three tasks: naming, repetition and reading. As expected, the results showed that the treatment led to a balancing of the values of both parameters between the two hemispheres since the rehabilitation contributed to the creation of new neural patterns to compensate for the disrupted ones. Moreover, we observed that for both name and repetition tasks, shortly after the stroke, the global and local connectivity are lower in the affected lobe (left hemisphere) than in the unaffected one (right hemisphere). Conversely, for the reading task, global and local connectivity are higher in the impaired lobe. This apparently contrasting trend can be due to the effects of stroke, which affect not only the site of structural damage but also brain regions belonging to a functional network. Moreover, changes in network connectivity can be task-dependent. This work can be considered a first step for future EEG-based studies to establish the most suitable connectivity measures for supporting the treatment of stroke and monitoring the recovery process.

Unraveling the Mysteries of Mental Illness With Psilocybin.

Current medications have not been effective in reducing the prevalence of mental illness worldwide. The prevalence of illnesses such as treatment-resistant depression has increased despite the widespread use of a broad set of psychopharmaceuticals. Transcranial magnetic stimulation and ketamine therapy are making great strides in improving treatment-resistant depression outcomes but they have limitations. New psychotherapeutics are required that specifically target the underlying cellular pathologies leading to neuronal atrophy. This neuronal atrophy model is supplanting the long-held neurotransmitter deficit hypothesis to explain mental illness. Interest in psychedelics as therapeutic molecules to treat mental illness is experiencing a 21st-century reawakening that is on the cusp of a transformation. Psilocybin is a pro-drug, found in various naturally occurring mushrooms, that is dephosphorylated to produce psilocin, a classic tryptamine psychedelic functional as a 5-hydroxytryptamine 2A receptor agonist. We have focused this review to include studies in the last two years that suggest psilocybin promotes neuronal plasticity, which may lead to changes in brain network connectivity. Recent advancements in clinical trials using pure psilocybin in therapy suggest that it may effectively relieve the symptoms of depression in patients diagnosed with major depressive disorder and treatment-resistant depression. Sophisticated cellular and molecular experiments at the systems level have produced evidence that demonstrates psilocybin promotes neuritogenesis in the mouse brain - a mechanism that may address the root cause of depression at the cellular level. Finally, studies with psilocybin therapy for major depressive disorder suggest that this ancient molecule can promote functionally connected intrinsic networks in the human brain, resulting in durable improvements in the severity of depressive symptoms. Although further research is necessary, the prospect of using psilocybin for the treatment of mental illness is an enticing possibility.

Abnormal Dynamic Functional Network Connectivity in Adults with Autism Spectrum Disorder.

This study sought to explore changes of brain dynamic functional network connectivity (dFNC) in adults with autism spectrum disorder (ASD) and investigate their relationship with clinical manifestations. Resting-state functional magnetic resonance imaging (rs-fMRI) data were acquired from 78adult ASD patients from autism brain imaging data exchange datasets, and 65age-matched healthy controls subjects from the local community. Independent component analysis was conducted to evaluate dFNC patterns on the basis of 13independent components (ICs) within 11resting-state networks (RSN), namely, auditory network (AUDN), basal ganglia network (BGN), language network (LN), sensorimotor network (SMN), precuneus network (PUCN), salience network (SN), visuospatial network (VSN), dorsal default mode network (dDMN), high visual network (hVIS), primary visual network (pVIS), ventral default mode network (vDMN). Fraction time, mean dwell time, number of transitions, and RSN connectivity were calculated for group comparisons. Correlation analyses were performed between abnormal metrics and autism diagnostic observation schedule (ADOS) scores. Compared with controls, ASD patients had higher fraction time and mean dwell time in state2 (P = 0.017, P = 0.014). Reduced dFNC was found in the SMN with PUCN, SMN with hVIS, and increased dFNC was observed in the dDMN with SN in state2 in the ASD group. Fraction time and mean dwell time was positively correlated with stereotyped behavior scores of ADOS. The findings demonstrated the importance of evaluating transient alterations in specific neural networks of adult ASD patients. The abnormal metrics and connectivity may be related to symptoms such as stereotyped behavior.

Altered brain functional connectivity in the frontoparietal network following an ice hockey season

ABSTRACT Sustaining sports-related head impacts has been reported to result in neurological changes that potentially lead to later-life neurological disease. Advanced neuroimaging techniques have been used to detect subtle neurological effects resulting from head impacts, even after a single competitive season. The current study used resting-state functional magnetic resonance imaging to assess changes in functional connectivity of the frontoparietal network, a brain network responsible for executive functioning, in collegiate club ice hockey players over one season. Each player was scanned before and after the season and wore accelerometers to measure head impacts at practices and home games throughout the season. We examined pre- to post-season differences in connectivity within the frontoparietal and default mode networks, as well as the relationship between the total number of head impacts sustained and changes in connectivity. We found a significant interaction between network region of interest and time point (p = .016), in which connectivity between the left and right posterior parietal cortex seed regions increased over the season (p < .01). Number of impacts had a significant effect on frontoparietal network connectivity, such that more impacts were related to greater connectivity differences over the season (p = .042). Overall, functional connectivity increased in ice hockey athletes over a season between regions involved in executive functioning, and sensory integration, in particular. Furthermore, those who sustained more impacts had the greatest changes in connectivity. Consistent with prior findings in resting-state sports-related head impact literature, these findings have been suggested to represent brain injury. Highlights Functional connectivity of the frontoparietal network significantly increased between the pre- and post-season, which may be a compensatory mechanism driven by neural tissue injury caused by repetitive head impacts. Changes in frontoparietal network connectivity are related to head impact exposure, measured as the number of head impacts sustained in a single season. Functional connectivity of the default mode network did not change over an ice hockey season.