Interrelations between dopamine and serotonin producing sites and regions of the default mode network

Electroconvulsive Therapy Regulates Brain Connectome Dynamics in Patients With Major Depressive Disorder

BACKGROUND

Electroconvulsive therapy (ECT) is an effective treatment for patients with major depressive disorder (MDD), but its underlying neural mechanisms remain largely unknown. The aim of this study was to identify changes in brain connectome dynamics after ECT in MDD and to explore their associations with treatment outcome.

METHODS

We collected longitudinal resting-state functional magnetic resonance imaging data from 80 patients with MDD (50 with suicidal ideation [MDD-SI] and 30 without [MDD-NSI]) before and after ECT and 37 age- and sex-matched healthy control participants. A multilayer network model was used to assess modular switching over time in functional connectomes. Support vector regression was used to assess whether pre-ECT network dynamics could predict treatment response in terms of symptom severity.

RESULTS

At baseline, patients with MDD had lower global modularity and higher modular variability in functional connectomes than control participants. Network modularity increased and network variability decreased after ECT in patients with MDD, predominantly in the default mode and somatomotor networks. Moreover, ECT was associated with decreased modular variability in the left dorsal anterior cingulate cortex of MDD-SI but not MDD-NSI patients, and pre-ECT modular variability significantly predicted symptom improvement in the MDD-SI group but not in the MDD-NSI group.

CONCLUSIONS

We highlight ECT-induced changes in MDD brain network dynamics and their predictive value for treatment outcome, particularly in patients with SI. This study advances our understanding of the neural mechanisms of ECT from a dynamic brain network perspective and suggests potential prognostic biomarkers for predicting ECT efficacy in patients with MDD.

Why is vestibular migraine associated with many comorbidities?

Vestibular migraine (VM) is a usual trigger of episodic vertigo. Patients with VM often experience spinning, shaking, or unsteady sensations, which are usually also accompanied by photophobia, phonophobia, motor intolerance, and more. VM is often associated with a number of comorbidities. Recurrent episodes of VM can affect the patient's emotions, sleep, and cognitive functioning to varying degrees. Patients with VM may be accompanied by adverse moods such as anxiety, fear, and depression, which can gradually develop into anxiety disorders or depressive disorders. Sleep disorders are also a common concomitant symptom of VM, which significantly lower patients' quality of life. The influence of anxiety disorders and sleep disorders may reduce cognitive functions of VM, such as visuospatial ability, attention, and memory decline. Clinically, it is also common to see VM comorbid with other vestibular disorders, making the diagnosis more difficult. VM episodes are relieved but lingering, in which case VM may coexist with persistent postural-perceptual dizziness (PPPD). Anxiety may be an important bridge between recurrent VM and PPPD. The clinical manifestations of VM and Meniere's disease (MD) overlap considerably, and those who meet the diagnostic criteria for both can be said to have VM/MD comorbidity. VM can also present with positional vertigo, and some patients with VM present with typical benign paroxysmal positional vertigo (BPPV) nystagmus on positional testing. In this paper, we synthesize and analyze the pathomechanisms of VM comorbidity by reviewing the literature. The results show that it may be related to the extensive connectivity of the vestibular system with different brain regions and the close connection of the trigeminovascular system with the periphery of the vestibule. Therefore, it is necessary to pay attention to the diagnosis of comorbidities in VM, synthesize its pathogenesis, and give comprehensive treatment to patients.

Catecholaminergic Modulation of Large-Scale Network Dynamics Is Tied to the Reconfiguration of Corticostriatal Connectivity

Large-scale brain network function is critical for healthy cognition, yet links between such network function, neurochemistry, and smaller-scale neurocircuitry are unclear. Here, we evaluated 59 healthy individuals using resting-state fMRI to determine how network-level temporal dynamics were impacted by two well-characterized pharmacotherapies targeting catecholamines: methylphenidate (20 mg) and haloperidol (2 mg)-administered via randomized, double-blind, placebo-controlled design. Network temporal dynamic changes were tested for links with drug-induced alterations in complex corticostriatal connections as this circuit is a primary site of action for both drugs. Methylphenidate increased time in the default mode network state (DMN p < 0.001) and dorsal attention network state (DAN p < 0.001) and reduced time in the frontoparietal network state (p < 0.01). Haloperidol increased time in a sensory motor-DMN state (p < 0.01). The magnitude of change in network dynamics induced by methylphenidate vs. placebo correlated with the magnitude of methylphenidate-induced rearrangement of complex corticostriatal connectivity (R = 0.32, p = 0.014). Haloperidol did not alter complex corticostriatal connectivity. Methylphenidate enhanced time in network states involved in internal and external attention (DMN and DAN, respectively), aligning with methylphenidate's established role in attention. Methylphenidate also significantly changed complex corticostriatal connectivity by altering the relative strength between multiple corticostriatal connections, indicating that methylphenidate may shift which corticostriatal connections are prioritized relative to others. Findings show that these corticostriatal circuit changes are linked with large-scale network temporal dynamics. Collectively, these findings provide a deeper understanding of large-scale network function, set a stage for mechanistic understanding of network engagement, and provide useful information to guide medication use based on network-level effects. Trial Registration: Registry name: ClinicalTrials.gov; URL: Brain Networks and Addiction Susceptibility-Full Text View-ClinicalTrials.gov; URL Plain text: https://classic.clinicaltrials.gov/ct2/show/NCT01924468; Identifier: NCT01924468.

Effect of L-theanine on selective attention in a traffic-related reaction task in sleep-deprived young adults: a double-blind placebo-controlled, crossover study

BACKGROUND

L-theanine is a non-protein-forming amino acid found in tea. Limited evidence suggests that it improves selective attention. Sleep deprivation impairs attention and psychomotor reactions, affecting automobile driving. We aimed to determine whether L-theanine improves neurobehavioral measures of visual attention in acutely sleep-deprived healthy adults in a traffic-scene-based attention task.

METHODS

In a double-blind, placebo-controlled, counterbalanced, two-way crossover study, we compared the effects of a 200-mg dose of L-theanine with a placebo (150 ml of distilled water) on a computerised, traffic-scene-based visual recognition reaction task in 24 healthy volunteers (age 20-25 years; 13 males) sleep-deprived overnight. The participants made speeded button-presses to imminent accident scenes (i.e. hits), while ignoring safe scenes. They were tested pre-dose and 45 min post-dose, each treatment administered one week apart.

RESULTS

Hit rates were more than 90% in all sessions, and were similar in two treatments, pre- vs post-dose. L-theanine significantly reduced false alarms (i.e. responses to safe scenes) (p = 0.014) and increased A' (i.e. target-distractor discriminability) (p = 0.009), whereas placebo did not (p > 0.05). L-theanine reduced hit reaction time by 38.65 ms (p = 0.007), and placebo by 19.08 ms (p = 0.016), however reaction time changes from baseline were not significantly different between treatments (p > 0.05).

CONCLUSIONS

L-theanine in high doses appears to improve selective visual attention by concurrently improving information processing speed and target-distractor discriminability in acutely sleep-deprived individuals. This is consistent with previous functional neuroimaging findings, where L-theanine suppressed distractor-processing and default-mode-network activity in visual selective attention tasks.

Cortical and subcortical functional connectivity and cognitive impairment in Parkinson's disease

Parkinson's disease (PD) is a neurodegenerative disease with cognitive as well as motor impairments. While much is known about the brain networks leading to motor impairments in PD, less is known about the brain networks contributing to cognitive impairments. Here, we leveraged resting-state functional magnetic resonance imaging (rs-fMRI) data from the Parkinson's Progression Marker Initiative (PPMI) to examine network dysfunction in PD patients with cognitive impairment. We focus on canonical cortical networks linked to cognition, including the salience network (SAL), frontoparietal network (FPN), and default mode network (DMN), as well as a subcortical basal ganglia network (BGN). We used the Montreal Cognitive Assessment (MoCA) as a continuous index of coarse cognitive function in PD. In 82 PD patients, we found that lower MoCA scores were linked with lower intra-network connectivity of the FPN. We also found that lower MoCA scores were linked with lower inter-network connectivity between the SAL and the BGN, the SAL and the DMN, as well as the FPN and the DMN. These data elucidate the relationship of cortical and subcortical functional connectivity with cognitive impairments in PD.

Connectome-guided initiation of dopamine agonists facilitates cognitive recovery after frontal lobe resection: A case report

Abulia is a common problem that manifests following various brain conditions, including brain surgeries. Abulia is felt to be related to dysfunction with the brain's dopamine-dependent circuitry. The role of default mode network (DMN) in its pathogenesis is crucial. In this case report, we detail the presentation of abulia in an elderly woman following surgical resection of a right frontal glioblastoma involving the DMN. Connectomic imaging was used pre-operatively and post-operatively, demonstrating disruption of regions integral to the DMN and the central executive network. We observed a significant cognitive improvement following the administration of levodopa and carbidopa. Preoperative assessment of both anatomical and functional networks can help ensure surgical safety and predict postoperative deficits. This evaluation not only enhances preparedness and facilitates early case diagnosis but also expedites the initiation of prompt and potentially targeted treatments. This case highlights the potential efficacy of levodopa and carbidopa in addressing DMN dysfunction and broadly suggests the potential for connectomics-guided post-operative therapies.

A new causal centrality measure reveals the prominent role of subcortical structures in the causal architecture of the extended default mode network

Network representation has been an incredibly useful concept for understanding the behavior of complex systems in social sciences, biology, neuroscience, and beyond. Network science is mathematically founded on graph theory, where nodal importance is gauged using measures of centrality. Notably, recent work suggests that the topological centrality of a node should not be over-interpreted as its dynamical or causal importance in the network. Hence, identifying the influential nodes in dynamic causal models (DCM) remains an open question. This paper introduces causal centrality for DCM, a dynamics-sensitive and causally-founded centrality measure based on the notion of intervention in graphical models. Operationally, this measure simplifies to an identifiable expression using Bayesian model reduction. As a proof of concept, the average DCM of the extended default mode network (eDMN) was computed in 74 healthy subjects. Next, causal centralities of different regions were computed for this causal graph, and compared against several graph-theoretical centralities. The results showed that the subcortical structures of the eDMN were more causally central than the cortical regions, even though the graph-theoretical centralities unanimously favored the latter. Importantly, model comparison revealed that only the pattern of causal centrality was causally relevant. These results are consistent with the crucial role of the subcortical structures in the neuromodulatory systems of the brain, and highlight their contribution to the organization of large-scale networks. Potential applications of causal centrality-to study causal models of other neurotypical and pathological functional networks-are discussed, and some future lines of research are outlined.

Salience network and cognitive impairment in Parkinson's disease

Parkinson's disease (PD) is a neurodegenerative disease with cognitive as well as motor impairments. While much is known about the brain networks leading to motor impairments in PD, less is known about the brain networks contributing to cognitive impairments. Here, we leveraged resting-state functional magnetic resonance imaging (rs-fMRI) data from the Parkinson's Progression Marker Initiative (PPMI) to examine network dysfunction in PD patients with cognitive impairment. We tested the hypothesis that cognitive impairments in PD involve altered connectivity of the salience network (SN), a key cortical network that detects and integrates responses to salient stimuli. We used the Montreal Cognitive Assessment (MoCA) as a continuous index of coarse cognitive function in PD. We report two major results. First, in 82 PD patients we found significant relationships between lower intra-network connectivity of the frontoparietal network (FPN; comprising the dorsolateral prefrontal and posterior parietal cortices bilaterally) with lower MoCA scores. Second, we found significant relationships between lower inter-network connectivity between the SN and the basal ganglia network (BGN) and the default mode network (DMN) with lower MoCA scores. These data support our hypothesis about the SN and provide new insights into the brain networks contributing to cognitive impairments in PD.

Dissociable brainstem functional connectivity changes correlate with objective and subjective vigilance decline after total sleep deprivation in healthy male subjects

The maintenance of vigilance relies on the activation of the cerebral cortex by the arousal system centered on the brainstem. Previous studies have suggested that both objective and subjective vigilance are susceptible to sleep deprivation. This study aims to explore the alterations in brainstem arousal system functional connectivity (FC) and its involvement in these two types of vigilance decline following total sleep deprivation (TSD). Thirty-seven healthy male subjects underwent two counterbalanced resting-state fMRI scans, once in rested wakefulness (RW) and once after 36 h of TSD. The pontine tegmental area and caudal midbrain (PTA-cMidbrain), the core regions of the brainstem arousal system, were chosen as the seeds for FC analysis. The difference in PTA-cMidbrain FC between RW and TSD conditions was then investigated, as well as its associations with objective vigilance measured by psychomotor vigilance task (PVT) and subjective vigilance measured by Stanford Sleepiness Scale. The sleep-deprived subjects showed increased PTA-cMidbrain FC with the thalamus and cerebellum and decreased PTA-cMidbrain FC with the occipital, parietal, and sensorimotor regions. TSD-induced increases in PVT reaction time were negatively correlated with altered PTA-cMidbrain FC in the dorsolateral prefrontal cortex, extrastriate visual cortex, and precuneus. TSD-induced increases in subjective sleepiness were positively correlated with altered PTA-cMidbrain FC in default mode regions including the medial prefrontal cortex and precuneus. Our results suggest that different brainstem FC patterns underlie the objective and subjective vigilance declines induced by TSD.

Different Features of a Metabolic Connectivity Map and the Granger Causality Method in Revealing Directed Dopamine Pathways: A Study Based on Integrated PET/MR Imaging

BACKGROUND AND PURPOSE

Exploring the directionality of neural information in the brain is important for understanding brain mechanisms and neurodisease development. Granger causality analysis and the metabolic connectivity map can be used to investigate directional transmission of information between brain regions, but their differences in depicting functional effective connectivity are not clear.

MATERIALS AND METHODS

Using the Monash rs-PET/MR imaging data set, we conducted Granger causality and metabolic connectivity map analyses of the dopamine reward circuit in the brain. The dopamine reward circuit is a well-known system consisting primarily of the bilateral orbital frontal cortex, caudate, nucleus accumbens, thalamus, and substantia nigra. We validated these circuit pathways using Granger causality and the metabolic connectivity map for identifying effective connectivities against a priori knowledge by testing the significance of directed pathways (P < .05, false discovery rate-corrected).

RESULTS

We found 3 types of effective connectivities in the dopamine reward circuit: long-range, neighborhood, and symmetric. Granger causality analysis revealed long-range connections in the orbital frontal cortex-caudate and orbital frontal cortex-nucleus accumbens regions. Metabolic connectivity map analysis revealed neighborhood connections in the nucleus accumbens-caudate, substantia nigra-thalamus, and thalamus-caudate regions. Metabolic connectivity map analysis also found symmetric connections in each of the bilateral nucleus accumbens, caudate, thalamus, and orbital frontal cortex-caudate regions. Different patterns in directional networks of the dopamine reward circuit were revealed by Granger causality and metabolic connectivity map analyses.

CONCLUSIONS

Granger causality analysis primarily identified bidirectional cortico-nucleus connections, while the metabolic connectivity map primarily identified direct connections among neighborhood and symmetric regions. The results of this study indicated that investigations of effective connectivities should use an appropriate analysis method depending on the purpose of the study.

The Atypical Effective Connectivity of Right Temporoparietal Junction in Autism Spectrum Disorder: A Multi-Site Study

Social function impairment is the core deficit of autism spectrum disorder (ASD). Although many studies have investigated ASD through a variety of neuroimaging tools, its brain mechanism of social function remains unclear due to its complex and heterogeneous symptoms. The present study aimed to use resting-state functional magnetic imaging data to explore effective connectivity between the right temporoparietal junction (RTPJ), one of the key brain regions associated with social impairment of individuals with ASD, and the whole brain to further deepen our understanding of the neuropathological mechanism of ASD. This study involved 1,454 participants from 23 sites from the Autism Brain Imaging Data Exchange (ABIDE) public dataset, which included 618 individuals with ASD and 836 with typical development (TD). First, a voxel-wise Granger causality analysis (GCA) was conducted with the RTPJ selected as the region of interest (ROI) to investigate the differences in effective connectivity between the ASD and TD groups in every site. Next, to obtain further accurate and representative results, an image-based meta-analysis was implemented to further analyze the GCA results of each site. Our results demonstrated abnormal causal connectivity between the RTPJ and the widely distributed brain regions and that the connectivity has been associated with social impairment in individuals with ASD. The current study could help to further elucidate the pathological mechanisms of ASD and provides a new perspective for future research.

Norepinephrine May Oppose Other Neuromodulators to Impact Alzheimer's Disease

While much of biomedical research since the middle of the twentieth century has focused on molecular pathways inside the cell, there is increasing evidence that extracellular signaling pathways are also critically important in health and disease. The neuromodulators norepinephrine (NE), serotonin (5-hydroxytryptamine, 5HT), dopamine (DA), acetylcholine (ACH), and melatonin (MT) are extracellular signaling molecules that are distributed throughout the brain and modulate many disease processes. The effects of these five neuromodulators on Alzheimer's disease (AD) are briefly examined in this paper, and it is hypothesized that each of the five molecules has a u-shaped (or Janus-faced) dose-response curve, wherein too little or too much signaling is pathological in AD and possibly other diseases. In particular it is suggested that NE is largely functionally opposed to 5HT, ACH, MT, and possibly DA in AD. In this scenario, physiological "balance" between the noradrenergic tone and that of the other three or four modulators is most healthy. If NE is largely functionally opposed to other prominent neuromodulators in AD, this may suggest novel combinations of pharmacological agents to counteract this disease. It is also suggested that the majority of cases of AD and possibly other diseases involve an excess of noradrenergic tone and a collective deficit of the other four modulators.

Interrelations between dopamine and serotonin producing sites and regions of the default mode network

Recent functional magnetic resonance imaging (fMRI) studies showed that blood oxygenation level-dependent (BOLD) signal fluctuations in the default mode network (DMN) are functionally tightly connected to those in monoaminergic nuclei, producing dopamine (DA), and serotonin (5-HT) transmitters, in the midbrain/brainstem. We combined accelerated fMRI acquisition with spectral Granger causality and coherence analysis to investigate causal relationships between these areas. Both methods independently lead to similar results and confirm the existence of a top-down information flow in the resting-state condition, where activity in core DMN areas influences activity in the neuromodulatory centers producing DA/5-HT. We found that latencies range from milliseconds to seconds with high inter-subject variability, likely attributable to the resting condition. Our novel findings provide new insights into the functional organization of the human brain.

Substance-Specific and Shared Gray Matter Signatures in Alcohol, Opioid, and Polysubstance Use Disorder

Substance use disorders (SUD) have been shown to be associated with gray matter (GM) loss, particularly in the frontal cortex. However, unclear is to what degree these regional GM alterations are substance-specific or shared across different substances, and if these regional GM alterations are independent of each other or the result of system-level processes at the intrinsic connectivity network level. The T1 weighted MRI data of 65 treated patients with alcohol use disorder (AUD), 27 patients with opioid use disorder (OUD) on maintenance therapy, 21 treated patients with stimulant use disorder comorbid with alcohol use disorder (polysubstance use disorder patients, PSU), and 21 healthy controls were examined via data-driven vertex-wise and voxel-wise GM analyses. Then, structural covariance analyses and open-access fMRI database analyses were used to map the cortical thinning patterns found in the three SUD groups onto intrinsic functional systems. Among AUD and OUD, we identified both common cortical thinning in right anterior brain regions as well as SUD-specific regional GM alterations that were not present in the PSU group. Furthermore, AUD patients had not only the most extended regional thinning but also significantly smaller subcortical structures and cerebellum relative to controls, OUD and PSU individuals. The system-level analyses revealed that AUD and OUD showed cortical thinning in several functional systems. In the AUD group the default mode network was clearly most affected, followed by the salience and executive control networks, whereas the salience and somatomotor network were highlighted as critical for understanding OUD. Structural brain alterations in groups with different SUDs are largely unique in their spatial extent and functional network correlates.