Modulation Of Brain Networks Research Articles

Neuromodulation and mindfulness as therapeutic treatment in detoxified patients with alcohol use disorder

BackgroundAlcohol use disorder (AUD) poses a significant global health challenge. Traditional management strategies often face high relapse rates, leading to a need for innovative approaches. Mindfulness-based relapse prevention (MBRP) has emerged as a promising intervention to enhance cognitive control, reduce cue-related craving and improve interoceptive processing. Neuroimaging studies suggest that mindfulness training can modulate brain networks associated with these factors, potentially improving treatment outcomes for AUD. Neuroimaging studies suggest that mindfulness training can modulate brain networks linked to these brain functions, potentially improving treatment outcomes for AUD. However, it is unclear how MBRP links to neurophysiological measures such as frontal midline theta oscillations (FMΘ) and whether the beneficial effects of MBRP can be increased by enhancing FMΘ. Here, we will use two different forms of neuromodulation to target and enhance these oscillations, and evaluate their impact on the effectiveness of MBRP.MethodsThis study will employ a four-arm randomized controlled trial to evaluate the synergistic effects of MBRP augmented with transcutaneous vagus nerve stimulation (tVNS) or closed-loop amplitude-modulated transcranial alternating current stimulation (CLAM-tACS) on cognitive control, cue reactivity and interoceptive processing in AUD patients. Participants will undergo six weekly group MBRP sessions and daily individual mindfulness practices. Assessments will include an inhibition task, cue-induced craving task, and heartbeat discrimination task, alongside heart rate variability and 32-channel EEG recordings. Participants will be assessed pre and post treatment, with a three-month follow-up to evaluate long-term effects on abstinence and alcohol consumption.DiscussionThis study will not only elucidate the causal link between FMΘ and efficacy of MBRP, but contribute to a better understanding of how combined psychological and neuromodulation interventions can improve treatment outcomes for AUD, potentially leading to more effective therapeutic strategies. This study also seeks to explore individual differences in response to treatment, which could inform future approaches to AUD management.Trial registrationThis study received approval by the Charité—Universitätsmedizin Berlin Institutional Review Board (EA1/030/23, 10.11.2023). It was registered on ClinicalTrials.gov (NCT06308484).

Dopamine and deep brain stimulation accelerate the neural dynamics of volitional action in Parkinson's disease.

The ability to initiate volitional action is fundamental to human behaviour. Loss of dopaminergic neurons in Parkinson's disease is associated with impaired action initiation, also termed akinesia. Both dopamine and subthalamic deep brain stimulation (DBS) can alleviate akinesia, but the underlying mechanisms are unknown. An important question is whether dopamine and DBS facilitate de novo build-up of neural dynamics for motor execution or accelerate existing cortical movement initiation signals through shared modulatory circuit effects. Answering these questions can provide the foundation for new closed-loop neurotherapies with adaptive DBS, but the objectification of neural processing delays prior to performance of volitional action remains a significant challenge. To overcome this challenge, we studied readiness potentials and trained brain signal decoders on invasive neurophysiology signals in 25 DBS patients (12 female) with Parkinson's disease during performance of self-initiated movements. Combined sensorimotor cortex electrocorticography and subthalamic local field potential recordings were performed OFF therapy (n = 22), ON dopaminergic medication (n = 18) and on subthalamic deep brain stimulation (n = 8). This allowed us to compare their therapeutic effects on neural latencies between the earliest cortical representation of movement intention as decoded by linear discriminant analysis classifiers and onset of muscle activation recorded with electromyography. In the hypodopaminergic OFF state, we observed long latencies between motor intention and motor execution for readiness potentials and machine learning classifications. Both, dopamine and DBS significantly shortened these latencies, hinting towards a shared therapeutic mechanism for alleviation of akinesia. To investigate this further, we analysed directional cortico-subthalamic oscillatory communication with multivariate granger causality. Strikingly, we found that both therapies independently shifted cortico-subthalamic oscillatory information flow from antikinetic beta (13-35 Hz) to prokinetic theta (4-10 Hz) rhythms, which was correlated with latencies in motor execution. Our study reveals a shared brain network modulation pattern of dopamine and DBS that may underlie the acceleration of neural dynamics for augmentation of movement initiation in Parkinson's disease. Instead of producing or increasing preparatory brain signals, both therapies modulate oscillatory communication. These insights provide a link between the pathophysiology of akinesia and its' therapeutic alleviation with oscillatory network changes in other non-motor and motor domains, e.g. related to hyperkinesia or effort and reward perception. In the future, our study may inspire the development of clinical brain computer interfaces based on brain signal decoders to provide temporally precise support for action initiation in patients with brain disorders.

A method for estimating dynamic functional network connectivity gradients (dFNG) from ICA captures smooth inter-network modulation.

Dynamic functional network connectivity (dFNC) analysis is a widely used approach for studying brain function and offering insight into how brain networks evolve over time. Typically, dFNC studies utilized fixed spatial maps and evaluate transient changes in coupling among time courses estimated from independent component analysis (ICA). This manuscript presents a complementary approach that relaxes this assumption by spatially reordering the components dynamically at each timepoint to optimize for a smooth gradient in the FNC (i.e., a smooth gradient among ICA connectivity values). Several methods are presented to summarize dynamic FNC gradients (dFNGs) over time, starting with static FNC gradients (sFNGs), then exploring the reordering properties as well as the dynamics of the gradients themselves. We then apply this approach to a dataset of schizophrenia (SZ) patients and healthy controls (HC). Functional dysconnectivity between different brain regions has been reported in schizophrenia, yet the neural mechanisms behind it remain elusive. Using resting state fMRI and ICA on a dataset consisting of 151 schizophrenia patients and 160 age and gender-matched healthy controls, we extracted 53 intrinsic connectivity networks (ICNs) for each subject using a fully automated spatially constrained ICA approach. We develop several summaries of our functional network connectivity gradient analysis, both in a static sense, computed as the Pearson correlation coefficient between full time series, and a dynamic sense, computed using a sliding window approach followed by reordering based on the computed gradient, and evaluate group differences. Static connectivity analysis revealed significantly stronger connectivity between subcortical (SC), auditory (AUD) and visual (VIS) networks in patients, as well as hypoconnectivity in sensorimotor (SM) network relative to controls. sFNG analysis highlighted distinctive clustering patterns in patients and HCs along cognitive control (CC)/ default mode network (DMN), as well as SC/ AUD/ SM/ cerebellar (CB), and VIS gradients. Furthermore, we observed significant differences in the sFNGs between groups in SC and CB domains. dFNG analysis suggested that SZ patients spend significantly more time in a SC/ CB state based on the first gradient, while HCs favor the SM/DMN state. For the second gradient, however, patients exhibited significantly higher activity in CB domains, contrasting with HCs' DMN engagement. The gradient synchrony analysis conveyed more shifts between SM/ SC networks and transmodal CC/ DMN networks in patients. In addition, the dFNG coupling revealed distinct connectivity patterns between SC, SM and CB domains in SZ patients compared to HCs. To recap, our results advance our understanding of brain network modulation by examining smooth connectivity trajectories. This provides a more complete spatiotemporal summary of the data, contributing to the growing body of current literature regarding the functional dysconnectivity in schizophrenia patients. By employing dFNG, we highlight a new perspective to capture large scale fluctuations across the brain while maintaining the convenience of brain networks and low dimensional summary measures.

Relationships between Serotonin Transporter Availability and the Global Efficiency of the Executive Control Brain Network.

The diverse effects of serotonin on cognition may emerge from the modulation of large-scale brain networks that support distinct cognitive processes. Yet, the specific effect of serotoninergic modulation on the properties of these networks remains elusive. Here, we used a simultaneous PET-fMRI scanner combined with graph theory analyses to investigate the modulation of network properties by the Serotonin Transporter (SERT) availability measured in the dorsal raphe nucleus (DRN). We defined global efficiency as the average mean of efficiencies over all pairs of distinct nodes of specific brain networks, and determined whether SERT levels correlated with the global efficiency of each network. SERT availability in the DRN correlated negatively with the global efficiency of the executive control brain network, which is engaged in cognitive control and directed attention. No relationship was observed between SERT availability and the global efficiency of the default mode or the salience brain networks. These findings indicate a specific role of serotoninergic modulation in the executive control brain network via a change in its global efficiency.

Cortical Networks Relating to Arousal Are Differentially Coupled to Neural Activity and Hemodynamics.

Even in the absence of specific sensory input or a behavioral task, the brain produces structured patterns of activity. This organized activity is modulated by changes in arousal. Here, we use wide-field voltage imaging to establish how arousal relates to cortical network voltage and hemodynamic activity in spontaneously behaving head-fixed male and female mice expressing the voltage-sensitive fluorescent FRET sensor Butterfly 1.2. We find that global voltage and hemodynamic signals are both positively correlated with changes in arousal with a maximum correlation of 0.5 and 0.25, respectively, at a time lag of 0 s. We next show that arousal influences distinct cortical regions for both voltage and hemodynamic signals. These include a broad positive correlation across most sensory-motor cortices extending posteriorly to the primary visual cortex observed in both signals. In contrast, activity in the prefrontal cortex is positively correlated to changes in arousal for the voltage signal while it is a slight net negative correlation observed in the hemodynamic signal. Additionally, we show that coherence between voltage and hemodynamic signals relative to arousal is strongest for slow frequencies below 0.15 Hz and is near zero for frequencies >1 Hz. We finally show that coupling patterns are dependent on the behavioral state of the animal with correlations being driven by periods of increased orofacial movement. Our results indicate that while hemodynamic signals show strong relations to behavior and arousal, these relations are distinct from those observed by voltage activity.

Whole-brain dynamics across the menstrual cycle: the role of hormonal fluctuations and age in healthy women

Recent neuroimaging research suggests that female sex hormone fluctuations modulate brain activity. Nevertheless, how brain network dynamics change across the female menstrual cycle remains largely unknown. Here, we investigated the dynamical complexity underlying three menstrual cycle phases (i.e., early follicular, pre-ovulatory, and mid-luteal) in 60 healthy naturally-cycling women scanned using resting-state fMRI. Our results revealed that the pre-ovulatory phase exhibited the highest dynamical complexity (variability over time) across the whole-brain functional network compared to the early follicular and mid-luteal phases, while the early follicular showed the lowest. Furthermore, we found that large-scale resting-state networks reconfigure along menstrual cycle phases. Multilevel mixed-effects models revealed age-related changes in the whole-brain, control, and dorsal attention networks, while estradiol and progesterone influenced the whole-brain, DMN, limbic, dorsal attention, somatomotor, and subcortical networks. Overall, these findings evidence that age and ovarian hormones modulate brain network dynamics along the menstrual cycle.

Neuroprotective effects of repetitive transcranial magnetic stimulation on Alzheimer's disease: Undetermined therapeutic protocols and mechanisms

AbstractAlzheimer's disease (AD) is a prevalent neurodegenerative disorder characterized by gradual deterioration of cognitive functions, for which an effective treatment is currently unavailable. Repetitive transcranial magnetic stimulation (rTMS), a well‐established noninvasive brain stimulation method, is utilized in clinical settings to address various neuropsychiatric conditions, such as depression, neuropathic pain, and poststroke dysfunction. Increasing evidence suggests that rTMS may enhance cognitive abilities in individuals with AD. However, its optimal therapeutic protocols and precise mechanisms are currently unknown, impeding its clinical implementation. In the present review, we aimed to summarize and discuss the efficacy‐related parameters in rTMS treatment, encompassing stimulus frequency, stimulus pattern, stimulus intensity, and the configuration of the stimulus coil. Furthermore, we reviewed promising rTMS therapeutic protocols involving various combinations of these factors, that were examined in clinical studies. Based on our analysis, we propose that a multisite high‐frequency rTMS (HF‐rTMS) regimen has value in AD therapy, and that promising single‐site protocols, such as HF‐rTMS, applied over the left dorsolateral prefrontal cortex, precuneus, or cerebellum are required to be validated in larger clinical studies. Lastly, we provide a comprehensive review of the potential mechanisms underlying the neuroprotective effects of rTMS on cognition in AD in terms of brain network modulation as well as cellular and molecular reactions. In conclusion, the interaction of diverse mechanisms may be responsible for the total therapeutic effect of rTMS on AD. This review provides theoretical and practical evidence for the future clinical application and scientific research of rTMS in AD.

Modulating brain networks in space and time: Multi-locus transcranial magnetic stimulation

Modulating brain networks in space and time: Multi-locus transcranial magnetic stimulation

Prognostic prediction of the longitudinal cognitive trajectory of amyloid‐positive patients with mild dementia

AbstractBackgroundPrediction of longitudinal cognitive decline for patients with mild dementia using baseline characteristics will be useful for designing optimal clinical trials and real‐world patient monitoring. This can be accomplished via machine‐learning models using baseline clinical characteristics. Adding plasma pTau181 and brain region volumes may improve the predictions.MethodThe training cohort (TC) included 905 amyloid‐positive (A+) patients with mild dementia from two clinical trials. The validation cohort (VC) included 230 A+ patients from another clinical trial. Over 85% of these patients had MCI. The longitudinal cognitive decline was defined by the change from baseline in CDR‐SB at months 3, 6, 9, 12, 15, and 18. Plasma pTau181 was measured using Simoa assay in a subset of 159 patients in TC.Structural brain network (SBN) modules and hubs were derived using 207 regional volumetric MRI measures in TC via an algorithm from genomics called “multiscale embedded gene co‐expression network analysis”. A signature for predicting longitudinal cognitive trajectory for each patient was first derived using baseline cognitive assessments and demographics within TC via the Stochastic Gradient Boosting algorithm. The added value of SBNs and plasma pTau181 was then evaluated within this framework. Prediction performance was evaluated in VC via the correlation between predicted versus observed cognitive trajectory.ResultPredictions of the longitudinal cognitive trajectory using baseline cognitive assessments achieved 43.4% and 42.5% correlation with observed values at months 12 and 18 respectively. Key baseline cognitive function predictors were ADAS‐Cog‐14, CDR‐SB, and sub‐scores such as ideational praxis and word recall.Adding baseline SBNs significantly improved the correlation between predicted versus observed cognitive trajectory to 46.3% and 49.7% at months 12 and 18 respectively (p<0.05). Key baseline SBN predictors were middle and inferior temporal, inferior parietal, and superior frontal cortex, along with a module comprising the entorhinal cortex and temporal pole. Adding baseline plasma pTau181 to cognitive assessments yielded similar improvements, but adding both pTau181 and SBNs did not improve the predictions further.ConclusionLongitudinal cognitive decline of A+ patients with mild dementia can be predicted using baseline cognitive assessments. Adding specific structural brain networks or plasma pTau181 significantly improves the predictions.

Stereo-EEG-guided network modulation for psychiatric disorders: Interactive holographic planning

BackgroundConnectomic modeling studies are expanding understanding of the brain networks that are modulated by deep brain stimulation (DBS) therapies. However, explicit integration of these modeling results into prospective neurosurgical planning is only beginning to evolve. One challenge of employing connectomic models in patient-specific surgical planning is the inherent 3D nature of the results, which can make clinically useful data integration and visualization difficult. MethodsWe developed a holographic stereotactic neurosurgery research tool (HoloSNS) that integrates patient-specific brain models into a group-based visualization environment for interactive surgical planning using connectomic hypotheses. HoloSNS currently runs on the HoloLens 2 platform and it enables remote networking between headsets. This allowed us to perform surgical planning group meetings with study co-investigators distributed across the country. ResultsWe used HoloSNS to plan stereo-EEG and DBS electrode placements for each patient participating in a clinical trial (NCT03437928) that is targeting both the subcallosal cingulate and ventral capsule for the treatment of depression. Each patient model consisted of multiple components of scientific data and anatomical reconstructions of the head and brain (both patient-specific and atlas-based), which far exceed the data integration capabilities of traditional neurosurgical planning workstations. This allowed us to prospectively discuss and evaluate the positioning of the electrodes based on novel connectomic hypotheses. ConclusionsThe 3D nature of the surgical procedure, brain imaging data, and connectomic modeling results all highlighted the utility of employing holographic visualization to support the design of unique clinical experiments to explore brain network modulation with DBS.

Brain Response to Interferential Current Compared with Alternating Current Stimulation.

Temporal interference (TI) stimulation, which utilizes multiple external electric fields with amplitude modulation for neural modulation, has emerged as a potential noninvasive brain stimulation methodology. However, the clinical application of TI stimulation is inhibited by its uncertain fundamental mechanisms, and research has previously been restricted to numerical simulations and immunohistology without considering the acute in vivo response of the neural circuit. To address the characterization and understanding of the mechanisms underlying the approach, we investigated instantaneous brainwide activation patterns in response to invasive interferential current (IFC) stimulation compared with low-frequency alternative current stimulation (ACS). Results demonstrated that IFC stimulation is capable of inducing regional neural responses and modulating brain networks; however, the activation threshold for significantly recruiting a neural response using IFC was higher (at least twofold) than stimulation via alternating current, and the spatial distribution of the activation signal was restricted. A distinct blood oxygenation level-dependent (BOLD) response pattern was observed, which could be accounted for by the activation of distinct types of cells, such as inhibitory cells, by IFC. These results suggest that IFC stimulation might not be as efficient as conventional brain modulation methods, especially when considering TI stimulation as a potential alternative for stimulating subcortical brain areas. Therefore, we argue that a future transcranial application of TI on human subjects should take these implications into account and consider other stimulation effects using this technique.

Gait-combined closed-loop brain stimulation can improve walking dynamics in Parkinsonian gait disturbances: a randomised-control trial

ObjectiveGait disturbance lowers activities of daily living in patients with Parkinson’s disease (PD) and related disorders. However, the effectiveness of pharmacological, surgical and rehabilitative treatments is limited. We recently developed...

211 Subthalamic Nucleus Deep Brain Stimulation Engages Memory Networks in Patients With Parkinson’s Disease

INTRODUCTION: Parkinson’s disease (PD) patients with deep brain stimulation (DBS) implantation to the subthalamic nucleus (STN) often have neurocognitive side effects, but the modulated networks associated with their occurrence are largely unknown. METHODS: We localized electrodes and investigated brain connectivity seeding from stimulation volumes in patients that underwent subthalamic deep brain stimulation using a normative resting state functional connectome atlas composed of 1000 healthy subjects. Pre- and post-operative neuropsychological testing data was acquired before and after surgery using two similar memory paradigms (list recall and figure recall). The connectivity profiles associated with changes in these tests were calculated. Results were compared with automated meta-analysis maps calculated using neurosynth and neuroquery. RESULTS: 41 patients were included in the analysis. Positive functional connections between stimulation sites and medial temporal lobe, medial prefrontal cortex, and parieto-occipital region were associated with improvement in list recall. This connectivity profile showed robustness in a leave-one-out cross-validation (R = 0.32, p = 0.027). A similar connectivity profile was associated with figure recall improvement, also robust to cross-validation (R = 0.30, p = 0.019). The profiles associated with the two tasks were able to cross-estimate performance on the respective other task (R = 0.45, p = 0.008; R = 0.26, p = 0.045). When comparing these connectivity profiles with meta-analytic maps in the neurosynth database, the cognitive terms involved with memory showed highest improvement. The neuroquery database areas of predicted activation overlapped with the connectivity maps in the midbody and posterior hippocampal region. CONCLUSIONS: This retrospective study relates modulation of brain networks associated with memory processing with improvements in two separate memory tasks.

Enhancing the connectivity in a 2-node motor network using reinforcement learning-based closed-loop rTMS

Abstract TMS has been extensively used to investigate the physiology of the central nervous system in health and disease. However, there is a strong need for a personalized, multi-locus, real-time stimulation procedure, which can be adaptively fine-tuned based on case-specific feedback. Here, we modulate the effective connectivity of the 2-node brain network from supplementary motor area (SMA) to primary motor cortex (M1) by closed-loop stimulation, optimized by application of an online reinforcement learning algorithm. This algorithm learns to identify the individually optimal phase of the ongoing μ-rhythm to be targeted by paired SMA-M1 TMS for maximized long-term enhancement of facilitatory effective connectivity between SMA and M1. This is one of the first demonstrations of true closed-loop stimulation, a crucially important step towards individualized highly-effective brain stimulation for therapeutic modulation of dysfunctional brain networks, e.g., the deficient SMA-M1 connection in motor stroke. Research Category and Technology and Methods Translational Research: 7. Responsive (Closed-Loop) Stimulation Keywords: EEG-TMS, Machine Learning, Closed-loop stimulation, Motor cortex

Transcranial magnetic stimulation to the angular gyrus modulates the temporal dynamics of the hippocampus and entorhinal cortex.

Transcranial magnetic stimulation (TMS) delivered to the angular gyrus (AG) affects hippocampal function and associated behaviors (Thakral PP, Madore KP, Kalinowski SE, Schacter DL. Modulation of hippocampal brain networks produces changes in episodic simulation and divergent thinking. 2020a. Proc Natl Acad Sci U S A. 117:12729-12740). Here, we examine if functional magnetic resonance imaging (fMRI)-guided TMS disrupts the gradient organization of temporal signal properties, known as the temporal organization, in the hippocampus (HPC) and entorhinal cortex (ERC). For each of 2 TMS sessions, TMS was applied to either a control site (vertex) or to a left AG target region (N = 18; 14 females). Behavioral measures were then administered, and resting-state scans were acquired. Temporal dynamics were measured by tracking change in the fMRI signal (i) "within" single voxels over time, termed single-voxel autocorrelation and (ii) "between" different voxels over time, termed intervoxel similarity. TMS reduced AG connectivity with the hippocampal target and induced more rapid shifting of activity in single voxels between successive time points, lowering the single-voxel autocorrelation, within the left anteromedial HPC and posteromedial ERC. Intervoxel similarity was only marginally affected by TMS. Our findings suggest that hippocampal-targeted TMS disrupts the functional properties of the target site along the anterior/posterior axis. Further studies should examine the consequences of altering the temporal dynamics of these medial temporal areas to the successful processing of episodic information under task demand.

Deep brain stimulation of the nucleus basalis of Meynert modulates hippocampal–frontoparietal networks in patients with advanced Alzheimer’s disease

BackgroundDeep brain stimulation (DBS) of the nucleus basalis of Meynert (NBM) has shown potential for the treatment of mild-to-moderate Alzheimer’s disease (AD). However, there is little evidence of whether NBM-DBS can improve cognitive functioning in patients with advanced AD. In addition, the mechanisms underlying the modulation of brain networks remain unclear. This study was aimed to assess the cognitive function and the resting-state connectivity following NBM-DBS in patients with advanced AD.MethodsEight patients with advanced AD underwent bilateral NBM-DBS and were followed up for 12 months. Clinical outcomes were assessed by neuropsychological examinations using the Mini-Mental State Examination (MMSE) and Alzheimer’s Disease Assessment Scale. Resting-state functional magnetic resonance imaging and positron emission tomography data were also collected.ResultsThe cognitive functioning of AD patients did not change from baseline to the 12-month follow-up. Interestingly, the MMSE score indicated clinical efficacy at 1 month of follow-up. At this time point, the connectivity between the hippocampal network and frontoparietal network tended to increase in the DBS-on state compared to the DBS-off state. Additionally, the increased functional connectivity between the parahippocampal gyrus (PHG) and the parietal cortex was associated with cognitive improvement. Further dynamic functional network analysis showed that NBM-DBS increased the proportion of the PHG-related connections, which was related to improved cognitive performance.ConclusionThe results indicated that NBM-DBS improves short-term cognitive performance in patients with advanced AD, which may be related to the modulation of multi-network connectivity patterns, and the hippocampus plays an important role within these networks.Trial registrationChiCTR, ChiCTR1900022324. Registered 5 April 2019—Prospective registration. https://www.chictr.org.cn/showproj.aspx?proj=37712

Stimulating learning: A functional MRI and behavioral investigation of the effects of transcranial direct current stimulation on stochastic learning in schizophrenia

Transcranial direct current stimulation (tDCS) of the medial prefrontal cortex (mPFC) is under clinical investigation as a treatment for cognitive deficits. We investigate the effects of tDCS over the mPFC on performance SSLT in individuals with schizophrenia, and the underlying neurophysiological effect in regions associated with learning values and stimulus-outcome relationships. In this parallel-design double-blind pilot study, 49 individuals with schizophrenia, of whom 28 completed a fMRI, were randomized into active or sham tDCS stimulation groups. Subjects participated in 4 days of SSLT training (days 1, 2, 14, 56) with tDCS applied at day-1, and during a concurrent MRI scan at day-14. The SSLT demonstrated a significant mean difference in performance in the tDCS treatment group: at day-2 and at day-56. Active tDCS was associated with increased insular activity, and reduced amygdala activation. tDCS may offer an important novel approach to modulating brain networks to ameliorate cognitive deficits in schizophrenia, with this study being the first to show a longer-term effect on SSLT.

An fMRI examination of the neural basis of suicide attempts: The role of mentalizing in the context of mood.

Facial emotion recognition (FER) deficits in depressed mood disorder patients contribute to suicidality. Prior research shows that intrinsic brain activity patterns are altered by attempting suicide. Therefore, we investigated in depressed patients whether differences in FER contribute to their clinical symptoms of suicide. Neural activity in response to an FER task was compared across three groups: healthy controls (HCs, N=66), suicide non-attempter (SNA, N=50), suicide attempter (SA, N=25). Modulation of brain networks by the task and functional connectivity (FC) within (using spatial map, spectral power) and between (using functional network connectivity; FNC) were examined. The contribution of these differences to suicidal symptoms in each group was also examined. Patient groups displayed impaired FC both within and between networks but differed in nature and networks involved. They also showed differential modulation of networks by task, such that compared with both HC and SNA, SA displayed impaired FC within the default-mode network (DMN) and also its task modulation. In the SA group, FC within the DMN and FNC between two lateral prefrontal networks, and its interaction with the basal ganglia network contributed significantly to the clinical symptoms of suicide. This study affirms differences between SA and SNA brain activity patterns and suggests that suicidal activity probably emanates via different mechanisms in these patient groups. Perhaps, over-attribution of emotion impairs one's self-referential thought processes and coupled with diminished emotional control this makes depressed individuals vulnerable to suicide.

Sex Differentially Alters Secretion of Brain Extracellular Vesicles During Aging: A Potential Mechanism for Maintaining Brain Homeostasis

Extracellular vesicles (EVs) in the brain play a role in neuronal homeostasis by removing intracellular material and regulating cell-to-cell communication. Given that sex and aging differentially modulate brain networks, we investigated sex-dependent differences in EV levels and content in the brain during aging. EVs were isolated from the brains of 3, 6, 12, 18, and 24 month-old female and male C57BL/6 J mice, and the levels of different EV species determined. While the number of plasma membrane-derived microvesicles and a subset of late endosomes-derived exosomes increased with age in the brain of female mice, no significant changes were seen in males. Mitochondria-derived mitovesicles in the brain increased during aging in both sexes, a change that may reflect aging-dependent alterations in mitochondrial function. These findings reveal enhanced turnover during aging in female brains, suggesting a mechanism for advantageous successful female brain aging and sex-depending different susceptibility to age-related neurodegenerative diseases.

Network alterations underlying anxiety symptoms in early multiple sclerosis

BackgroundAnxiety, often seen as comorbidity in multiple sclerosis (MS), is a frequent neuropsychiatric symptom and essentially affects the overall disease burden. Here, we aimed to decipher anxiety-related networks functionally connected to atrophied areas in patients suffering from MS.MethodsUsing 3-T MRI, anxiety-related atrophy maps were generated by correlating longitudinal cortical thinning with the severity of anxiety symptoms in MS patients. To determine brain regions functionally connected to these maps, we applied a technique termed “atrophy network mapping”. Thereby, the anxiety-related atrophy maps were projected onto a large normative connectome (n = 1000) performing seed‐based functional connectivity. Finally, an instructed threat paradigm was conducted with regard to neural excitability and effective connectivity, using transcranial magnetic stimulation combined with high-density electroencephalography.ResultsThinning of the left dorsal prefrontal cortex was the only region that was associated with higher anxiety levels. Atrophy network mapping identified functional involvement of bilateral prefrontal cortex as well as amygdala and hippocampus. Structural equation modeling confirmed that the volumes of these brain regions were significant determinants that influence anxiety symptoms in MS. We additionally identified reduced information flow between the prefrontal cortex and the amygdala at rest, and pathologically increased excitability in the prefrontal cortex in MS patients as compared to controls.ConclusionAnxiety-related prefrontal cortical atrophy in MS leads to a specific network alteration involving structures that resemble known neurobiological anxiety circuits. These findings elucidate the emergence of anxiety as part of the disease pathology and might ultimately enable targeted treatment approaches modulating brain networks in MS.