Plasticity Of Brain Networks Research Articles

Repeatedly experiencing the McGurk effect induces long-lasting changes in auditory speech perception

In the McGurk effect, presentation of incongruent auditory and visual speech evokes a fusion percept different than either component modality. We show that repeatedly experiencing the McGurk effect for 14 days induces a change in auditory-only speech perception: the auditory component of the McGurk stimulus begins to evoke the fusion percept, even when presented on its own without accompanying visual speech. This perceptual change, termed fusion-induced recalibration (FIR), was talker-specific and syllable-specific and persisted for a year or more in some participants without any additional McGurk exposure. Participants who did not experience the McGurk effect did not experience FIR, showing that recalibration was driven by multisensory prediction error. A causal inference model of speech perception incorporating multisensory cue conflict accurately predicted individual differences in FIR. Just as the McGurk effect demonstrates that visual speech can alter the perception of auditory speech, FIR shows that these alterations can persist for months or years. The ability to induce seemingly permanent changes in auditory speech perception will be useful for studying plasticity in brain networks for language and may provide new strategies for improving language learning.

Older adults with lower working memory capacity benefit from transcranial direct current stimulation when combined with working memory training: A preliminary study.

Aging is a very diverse process: successful agers retain most cognitive functioning, while others experience mild to severe cognitive decline. This decline may eventually negatively impact one’s everyday activities. Therefore, scientists must develop approaches to counteract or, at least, slow down the negative change in cognitive performance of aging individuals. Combining cognitive training and transcranial direct current stimulation (tDCS) is a promising approach that capitalizes on the plasticity of brain networks. However, the efficacy of combined methods depends on individual characteristics, such as the cognitive and emotional state of the individual entering the training program. In this report, we explored the effectiveness of working memory training, combined with tDCS to the right dorsolateral prefrontal cortex (DLPFC), to manipulate working memory performance in older individuals. We hypothesized that individuals with lower working memory capacity would benefit the most from the combined regimen. Thirty older adults took part in a 5-day combined regimen. Before and after the training, we evaluated participants’ working memory performance with five working memory tasks. We found that individual characteristics influenced the outcome of combined cognitive training and tDCS regimens, with the intervention selectively benefiting old-old adults with lower working memory capacity. Future work should consider developing individualized treatments by considering individual differences in cognitive profiles.

Altered Effective Connectivity of Resting-State Networks by Tai Chi Chuan in Chronic Fatigue Syndrome Patients: A Multivariate Granger Causality Study.

Numerous evidence has shown that patients with chronic fatigue syndrome (CFS) have changes in resting brain functional connectivity, but there is no study on the brain network effect of Tai Chi Chuan intervention in CFS. To explore the influence of Tai Chi Chuan exercise on the causal relationship between brain functional networks in patients with CFS, 21 patients with CFS and 19 healthy controls were recruited for resting-state functional magnetic resonance imaging (rs-fMRI) scanning and 36-item Short-Form Health Survey (SF-36) scale assessment before and after 1month-long training in Tai Chi Chuan. We extracted the resting brain networks using the independent component analysis (ICA) method, analyzed the changes of FC in these networks, conducted Granger causality analysis (GCA) on it, and analyzed the correlation between the difference causality value and the SF-36 scale. Compared to the healthy control group, the SF-36 scale scores of patients with CFS were lower at baseline. Meanwhile, the causal relationship between sensorimotor network (SMN) and default mode network (DMN) was weakened. The above abnormalities could be improved by Tai Chi Chuan training for 1 month. In addition, the correlation analyses showed that the causal relationship between SMN and DMN was positively correlated with the scores of Role Physical (RP) and Bodily Pain (BP) in CFS patients, and the change of causal relationship between SMN and DMN before and after training was positively correlated with the change of BP score. The findings suggest that Tai Chi Chuan is helpful to improve the quality of life for patients with CFS. The change of Granger causality between SMN and DMN may be a readout parameter of CFS. Tai Chi Chuan may promote the functional plasticity of brain networks in patients with CFS by regulating the information transmission between them.

Altered intra- and inter-network brain functional connectivity in upper-limb amputees revealed through independent component analysis.

Although cerebral neuroplasticity following amputation has been observed, little is understood about how network-level functional reorganization occurs in the brain following upper-limb amputation. The objective of this study was to analyze alterations in brain network functional connectivity (FC) in upper-limb amputees (ULAs). This observational study included 40 ULAs and 40 healthy control subjects; all participants underwent resting-state functional magnetic resonance imaging. Changes in intra- and inter-network FC in ULAs were quantified using independent component analysis and brain network FC analysis. We also analyzed the correlation between FC and clinical manifestations, such as pain. We identified 11 independent components using independent component analysis from all subjects. In ULAs, intra-network FC was decreased in the left precuneus (precuneus gyrus) within the dorsal attention network and left precentral (precentral gyrus) within the auditory network; but increased in the left Parietal_Inf (inferior parietal, but supramarginal and angular gyri) within the ventral sensorimotor network, right Cerebelum_Crus2 (crus II of cerebellum) and left Temporal_Mid (middle temporal gyrus) within the ventral attention network, and left Rolandic_Oper (rolandic operculum) within the auditory network. ULAs also showed decreased inter-network FCs between the dorsal sensorimotor network and ventral sensorimotor network, the dorsal sensorimotor network and right frontoparietal network, and the dorsal sensorimotor network and dorsal attention network. Correlation analyses revealed negative correlations between inter-network FC changes and residual limb pain and phantom limb pain scores, but positive correlations between inter-network FC changes and daily activity hours of stump limb. These results show that post-amputation plasticity in ULAs is not restricted to local remapping; rather, it also occurs at a network level across several cortical regions. This observation provides additional insights into the plasticity of brain networks after upper-limb amputation, and could contribute to identification of the mechanisms underlying post-amputation pain.

A state-informed stimulation approach with real-time estimation of the instantaneous phase of neural oscillations by a Kalman filter

Objective. We propose a novel method to estimate the instantaneous oscillatory phase to implement a real-time system for state-informed sensory stimulation in electroencephalography (EEG) experiments. Approach. The method uses Kalman filter-based prediction to estimate current and future EEG signals. We tested the performance of our method in a real-time situation. Main results. Our method showed higher accuracy in predicting the EEG phase than the conventional autoregressive (AR) model-based method. Significance. A Kalman filter allows us to easily estimate the instantaneous phase of EEG oscillations based on the automatically estimated AR model implemented in a real-time signal processing machine. The proposed method has a potential for versatile applications targeting the modulation of EEG phase dynamics and the plasticity of brain networks in relation to perceptual or cognitive functions.

Receptor-Receptor Interactions and Glial Cell Functions with a Special Focus on G Protein-Coupled Receptors.

The discovery that receptors from all families can establish allosteric receptor–receptor interactions and variably associate to form receptor complexes operating as integrative input units endowed with a high functional and structural plasticity has expanded our understanding of intercellular communication. Regarding the nervous system, most research in the field has focused on neuronal populations and has led to the identification of many receptor complexes representing an important mechanism to fine-tune synaptic efficiency. Receptor–receptor interactions, however, also modulate glia–neuron and glia–glia intercellular communication, with significant consequences on synaptic activity and brain network plasticity. The research on this topic is probably still at the beginning and, here, available evidence will be reviewed and discussed. It may also be of potential interest from a pharmacological standpoint, opening the possibility to explore, inter alia, glia-based neuroprotective therapeutic strategies.

Motor skill learning induces brain network plasticity: A diffusion-tensor imaging study

Motor skills and the acquisition of brain plasticity are important topics in current research. The development of non-invasive white matter imaging technology, such as diffusion-tensor imaging and the introduction of graph theory make it possible to study the effects of learning skills on the connection patterns of brain networks. However, few studies have characterized the brain network topological features of motor skill learning, especially open skill. Given the need to interact with environmental changes in real time, we hypothesized that the brain network of high-level open-skilled athletes had higher transmission efficiency and stronger interaction in attention, visual and sensorimotor networks. We selected 21 high-level basketball players and 25 ordinary individuals as control subjects, collected their DTI data, built a network of brain structures, and used graph theory to analyze and compare the network properties of the two groups at global and regional levels. In addition, we conducted a correlation analysis on the training years of high-level athletes and brain network nodal parameters on the regional level to assess the relationship between brain network topological characteristics and skills learning. We found that on the global-level, the brain network of high-level basketball players had a shorter path length, small-worldness, and higher global efficiency. On the regional level, the brain nodes of the high-level athletes had nodal parameters that were significantly higher than those of control groups, and were mainly distributed in the visual network, the default mode network, and the attention network. The changes in brain node parameters were significantly related to the number of training years.

Electronic-Sports Experience Related to Functional Enhancement in Central Executive and Default Mode Areas.

Electronic-sports (e-sports) is a form of organized, online, multiplayer video game competition, which requires both action skills and the ability and process of forming and adapting a strategy (referred to as strategization hereafter) to achieve goals. Over the past few decades, research has shown that video gaming experience has an important impact on the plasticity of the sensorimotor, attentional, and executive brain areas. However, little research has examined the relationship between e-sports experience and the plasticity of brain networks that are related to strategization. Using resting-state fMRI data and the local functional connectivity density (lFCD) analysis, this study investigated the relationship between e-sports experience (League of Legends [LOL] in this study) and brain plasticity by comparing between top-ranking LOL players and lower-ranking (yet experienced) LOL players. Results showed that the top-ranking LOL players had superior local functional integration in the executive areas compared to lower-ranking players. Furthermore, the top-ranking players had higher lFCD in the default mode areas, which have been found related to various subprocesses (e.g., memory and planning) essential for strategization. Finally, the top-ranking players' lFCD was related to their LOL expertise rank level, as indicated by a comprehensive score assigned by the gaming software based on players' gaming experience and expertise. Thus, the result showed that the local functional connectivity in central executive and default mode brain areas was enhanced in the top-ranking e-sports players, suggesting that e-sports experience is related to the plasticity of the central executive and default mode areas.

Initial Evidence for Brain Plasticity Following a Digital Therapeutic Intervention for Depression

Background Digital therapeutics such as cognitive–emotional training have begun to show promise for the treatment of major depressive disorder. Available clinical trial data suggest that monotherapy with cognitive–emotional training using the Emotional Faces Memory Task is beneficial in reducing depressive symptoms in patients with major depressive disorder. The aim of this study was to investigate whether Emotional Faces Memory Task training for major depressive disorder is associated with changes in brain connectivity and whether changes in connectivity parameters are related to symptomatic improvement. Methods Fourteen major depressive disorder patients received Emotional Faces Memory Task training as monotherapy over a six-week period. Patients were scanned at baseline and posttreatment to identify changes in resting-state functional connectivity and effective connectivity during emotional working memory processing. Results Compared to baseline, patients showed posttreatment reduced connectivity within resting-state networks involved in self-referential and salience processing and greater integration across the functional connectome at rest. Moreover, we observed a posttreatment increase in the Emotional Faces Memory Task-induced modulation of connectivity between cortical control and limbic brain regions, which was associated with clinical improvement. Discussion These findings provide initial evidence that cognitive–emotional training may be associated with changes in short-term plasticity of brain networks implicated in major depressive disorder. Conclusion Our findings pave the way for the principled design of large clinical and neuroimaging studies.

The plasticity of resting-state brain networks associated with motor imagery training in chronic stroke patients

Motor imagery training (MIT) is a widely used non-invasive treatment technique in stroke patients. Despite its clinical efficacy, little is known regarding the neural substrates underlying MIT. The aim of this study was to investigate the plasticity of resting-state brain networks associated with MIT in chronic stroke patients. A total of 34 chronic stroke patients with subcortical lesions were randomly assigned to the conventional rehabilitation therapy (CRT) group and the MIT group. Before and after the 4-week treatment, motor function of each patient was blindly assessed with Fugl–Meyer assessment Scale (FM-UL) and resting-state fMRI was administered. Functional connectivity (FC) analyses of the ipsilesional primary motor cortex (M1), measurements of the lateralization index (LI) and graph-theory based analysis were also performed in both group to investigate the altered whole-brain networks after treatment. Both groups showed significant improvement in FM-UL scores and the change in MIT group was significantly greater than that of the CRT group. Similar changes patterns of FC were seen between the MIT and CRT group: increased FC of the ipsilesional M1 with the contralesional precentral gyrus and decreased FC within ipsilesional hemisphere were found in both group. Additional increased FC of contralesional SMA and postcentral gyrus and restored LI were observed in MIT group. Moreover, a significant positive correlation between increase of clustering coefficient and improvement of FM-UL score were identified in MIT group as well ( P < 0.05). Our results indicate that the impact of MIT on the plasticity of brain networks was measurable on resting-state fMRI, and widely recruitment of the brain areas, restoring symmetry of the FC reflected by LI and increased clustering coefficients after treatment on a broader level of brain network analysis might be related to the neural mechanisms of motor recovery in stroke patients after MIT.

Altered intra- and inter-network functional coupling of resting-state networks associated with motor dysfunction in stroke.

Motor functions are supported through functional integration across the extended motor system network. Individuals following stroke often show deficits on motor performance requiring coordination of multiple brain networks; however, the assessment of connectivity patterns after stroke was still unclear. This study aimed to investigate the changes in intra- and inter-network functional connectivity (FC) of multiple networks following stroke and further correlate FC with motor performance. Thirty-three left subcortical chronic stroke patients and 34 healthy controls underwent resting-state functional magnetic resonance imaging. Eleven resting-state networks were identified via independent component analysis (ICA). Compared with healthy controls, the stroke group showed abnormal FC within the motor network (MN), visual network (VN), dorsal attention network (DAN), and executive control network (ECN). Additionally, the FC values of the ipsilesional inferior parietal lobule (IPL) within the ECN were negatively correlated with the Fugl-Meyer Assessment (FMA) scores (hand + wrist). With respect to inter-network interactions, the ipsilesional frontoparietal network (FPN) decreased FC with the MN and DAN; the contralesional FPN decreased FC with the ECN, but it increased FC with the default mode network (DMN); and the posterior DMN decreased FC with the VN. In sum, this study demonstrated the coexistence of intra- and inter-network alterations associated with motor-visual attention and high-order cognitive control function in chronic stroke, which might provide insights into brain network plasticity following stroke.

P.1.013 - Optical and pharmacological activation of TrkB receptors in parvalbumin interneurons induces plasticity in the adult visual cortex

Brain network plasticity occurs principally during critical periods in early postnatal life [1]. Chronic treatment with the antidepressant fluoxetine, however, can induce critical period-like plasticity (iPlasticity), such as shift of ocular dominance after monocular deprivation [2]. Fluoxetine activates the neurotrophic BDNF/TrkB pathway and is thought to be a crucial modulator of iPlasticity [3]. We aim to identify the mechanisms of iPlasticity and the neuronal subpopulations expressing TrkB that are responsible for the profound events observed in that process. Especially, Parvalbumin-positive GABAergic interneurons play a major role in regulating synaptic plasticity in the visual cortex [2]. TrkB receptors are highly enriched in Parvalbumin interneurons [4] and closure of critical periods is promoted by maturation of the intracortical Parvalbumin GABAergic network inhibiting critical period and ocular dominance plasticity. It is widely accepted that visual cortex plasticity requires a reduction in Parvalbumin GABAergic interneuron-mediated inhibition resulting in the activation of a large population of pyramidal neurons and subsequent synchronization of the brain network [1]. Given the importance of Parvalbumin in regulating plasticity events, we hypothesize that fluoxetine-induced plasticity in the adult visual cortex is mediated through TrkB receptors expressed in Parvalbumin GABAergic interneurons. We used the validated shift of ocular dominance paradigm as previously described [5] to study visual cortex plasticity, analyzed the data with two-way ANOVA and demonstrate that Parvalbumin GABAergic interneurons are key regulators in ocular dominance plasticity. Using an inhibition approach, we show that in heterozygous Parvalbumin-specific TrkB knockout mice (hPV-TrkB cKO) the fluoxetine-induced shift in ocular dominance is absent (p = 0.6185) while wild-type mice show a robust shift induced by chronic fluoxetine treatment (p = 0.0002). Also, we have developed DIO-optoTrkB, a double-inverted open reading frame lentivirus construct that is dependent on cre-recombinase, for cell type-specific expression in vivo and optogenetic stimulation promotes the dimerization and autophosphorylation of optoTrkB in Parvalbumin interneurons. During the shift in ocular dominance paradigm, specific optogenetic activation of optoTrkB induces the shift in ocular dominance in a stimulation-dependent manner when combined with monocular deprivation of the stronger eye (p 0.03). These data show a strikingly new and opposing role of intracortical inhibition by Parvalbumin GABAergic interneurons in ocular dominance and induction of iPlasticity, offering a new idea about the underlying mechanisms of plasticity during the critical period and adulthood. We propose a new tool to investigate neuropsychological disorders, such as amblyopia and post-traumatic stress disorder, by optically controlling neuronal plasticity in a neuron-specific manner.

Fingolimod reduces the clinical expression of active demyelinating lesions in MS

BackgroundFingolimod is a modulator of Central and peripheral sphingosine pathways, which is currently approved for treatment of Multiple Sclerosis (MS). In animal models it reduces inflammation, but it is also able to potentiate glutamatergic transmission and synaptic plasticity. We aimed to explore whether Fingolimod is able to modify the clinical expression of new demyelinating lesions with respect to IFNβ−1a in relapsing remitting MS (RRMS) patients suboptimal responders to IFNβ−1a. Methods103 patients with RRMS switching for inefficacy from IFNβ−1a to Fingolimod and treated for at least 12 months were included. Annualised Relapse Rate (ARR), EDSS and the number of new brain and spinal gadolinium enhancing (Gd +) and T2 lesions were retrospectively assessed in the whole group during each treatment period. The likelihood of co-occurrence of new Gd + lesions and clinical relapses during IFNβ−1a and Fingolimod treatment was analysed. ResultsThe mean duration of treatment with IFNβ−1a and Fingolimod was 3.14 (SD 1.6) and 3.22 years (SD 1.1) respectively. Significant reduction of ARR (p < .001), total number of Gd + and T2 lesions (p < .001) was found switching from IFNβ−1a to Fingolimod. Gd + lesions occurring during treatment with Fingolimod were more likely to be asymptomatic compared with IFNβ−1a (88% vs 30.9%, p = < .025). ConclusionFingolimod reduces clinical and radiological inflammation in MS. Additionally, it limits the clinical expression of new Gd + lesions, possibly reducing local inflammatory processes and improving brain network plasticity in patients with suboptimal response to IFNβ−1a.

Machine learning\u2013XGBoost analysis of language networks to classify patients with epilepsy

Our goal was to apply a statistical approach to allow the identification of atypical language patterns and to differentiate patients with epilepsy from healthy subjects, based on their cerebral activity, as assessed by functional MRI (fMRI). Patients with focal epilepsy show reorganization or plasticity of brain networks involved in cognitive functions, inducing ‘atypical’ (compared to ‘typical’ in healthy people) brain profiles. Moreover, some of these patients suffer from drug-resistant epilepsy, and they undergo surgery to stop seizures. The neurosurgeon should only remove the zone generating seizures and must preserve cognitive functions to avoid deficits. To preserve functions, one should know how they are represented in the patient’s brain, which is in general different from that of healthy subjects. For this purpose, in the pre-surgical stage, robust and efficient methods are required to identify atypical from typical representations. Given the frequent location of regions generating seizures in the vicinity of language networks, one important function to be considered is language. The risk of language impairment after surgery is determined pre-surgically by mapping language networks. In clinical settings, cognitive mapping is classically performed with fMRI. The fMRI analyses allowing the identification of atypical patterns of language networks in patients are not sufficiently robust and require additional statistic approaches. In this study, we report the use of a statistical nonlinear machine learning classification, the Extreme Gradient Boosting (XGBoost) algorithm, to identify atypical patterns and classify 55 participants as healthy subjects or patients with epilepsy. XGBoost analyses were based on neurophysiological features in five language regions (three frontal and two temporal) in both hemispheres and activated with fMRI for a phonological (PHONO) and a semantic (SEM) language task. These features were combined into 135 cognitively plausible subsets and further submitted to selection and binary classification. Classification performance was scored with the Area Under the receiver operating characteristic curve (AUC). Our results showed that the subset SEM_LH BA_47-21 (left fronto-temporal activation induced by the SEM task) provided the best discrimination between the two groups (AUC of 91 ± 5%). The results are discussed in the framework of the current debates of language reorganization in focal epilepsy.

Interaction between physical fitness and brain network plasticity induced by rTMS

Interaction between physical fitness and brain network plasticity induced by rTMS

Enhanced working memory performance via transcranial direct current stimulation: The possibility of near and far transfer

Although working memory (WM) training programs consistently result in improvement on the trained task, benefit is typically short-lived and extends only to tasks very similar to the trained task (i.e., near transfer). It is possible that pairing repeated performance of a WM task with brain stimulation encourages plasticity in brain networks involved in WM task performance, thereby improving the training benefit. In the current study, transcranial direct current stimulation (tDCS) was paired with performance of a WM task (n-back). In Experiment 1, participants performed a spatial location-monitoring n-back during stimulation, while Experiment 2 used a verbal identity-monitoring n-back. In each experiment, participants received either active (2.0mA) or sham (0.1mA) stimulation with the anode placed over either the right or the left dorsolateral prefrontal cortex (DLPFC) and the cathode placed extracephalically. In Experiment 1, only participants receiving active stimulation with the anode placed over the right DLPFC showed marginal improvement on the trained spatial n-back, which did not extend to a near transfer (verbal n-back) or far transfer task (a matrix-reasoning task designed to measure fluid intelligence). In Experiment 2, both left and right anode placements led to improvement, and right DLPFC stimulation resulted in numerical (though not sham-adjusted) improvement on the near transfer (spatial n-back) and far transfer (fluid intelligence) task. Results suggest that WM training paired with brain stimulation may result in cognitive enhancement that transfers to performance on other tasks, depending on the combination of training task and tDCS parameters used.

A voxelwise approach to determine consensus regions-of-interest for the study of brain network plasticity.

Despite exciting advances in the functional imaging of the brain, it remains a challenge to define regions of interest (ROIs) that do not require investigator supervision and permit examination of change in networks over time (or plasticity). Plasticity is most readily examined by maintaining ROIs constant via seed-based and anatomical-atlas based techniques, but these approaches are not data-driven, requiring definition based on prior experience (e.g., choice of seed-region, anatomical landmarks). These approaches are limiting especially when functional connectivity may evolve over time in areas that are finer than known anatomical landmarks or in areas outside predetermined seeded regions. An ideal method would permit investigators to study network plasticity due to learning, maturation effects, or clinical recovery via multiple time point data that can be compared to one another in the same ROI while also preserving the voxel-level data in those ROIs at each time point. Data-driven approaches (e.g., whole-brain voxelwise approaches) ameliorate concerns regarding investigator bias, but the fundamental problem of comparing the results between distinct data sets remains. In this paper we propose an approach, aggregate-initialized label propagation (AILP), which allows for data at separate time points to be compared for examining developmental processes resulting in network change (plasticity). To do so, we use a whole-brain modularity approach to parcellate the brain into anatomically constrained functional modules at separate time points and then apply the AILP algorithm to form a consensus set of ROIs for examining change over time. To demonstrate its utility, we make use of a known dataset of individuals with traumatic brain injury sampled at two time points during the first year of recovery and show how the AILP procedure can be applied to select regions of interest to be used in a graph theoretical analysis of plasticity.

The Plasticity of Brain Networks as a Basis for a Science of Nervous System Rehabilitation

The paper overviews physiologic efficiencies to provide justification for moving away from a medical model of rehabilitation using a binary conceptualization of impairment and moving towards a linear model of optimization and human efficiencies. While the clinical and neuropathological evaluation of neurological compromise has traditionally concentrated upon the focal distribution of brain disease, ignored have been the changes in the complex connections linking brain areas crucial for cognition and optimized human performance. The paper reviews the nature of nervous system plasticity, from a systems standpoint using language development and bilingualism as well as music and the brain as examples of optimized network functioning.

Simulating structural plasticity of large scale networks in NEST

The brain is much less hard-wired as traditionally thought. Permanently, new synapses are formed, existing synapses are deleted or connectivity rewires by re-routing axonal branches (structural plasticity). However, all current large-scale neuronal network models are hard-wired with plasticity merely arising from changes in the strength of existing synapses, therefore missing an important aspect of the plasticity of brain networks. This project is to develop the first large-scale neuronal network model with structural plasticity in the neuronal network simulator NEST [1] and to make it scalable for HPC. Formation and deletion of synapses in the model for structural plasticity (MSP) [2] depends on the number of synaptic contact possibilities that each neuron has, i.e. the number of axonal boutons and dendritic spines. Therefore, we developed a framework that allows the addition of synaptic elements (i.e. axonal boutons or dendritic spines) for every neuron model already implemented in NEST. The user can then define its own synaptic elements and their corresponding growth dynamic depending on the electrical activity (see Figure ​Figure1).1). Synapses are formed by merging corresponding synaptic elements or are deleted when synaptic elements are lost. The update in connectivity depends on the availability of the synaptic elements in the entire networks. To make this model scalable for HPC, we developed a probabilistic approach that reduce both communication between compute nodes and their memory usage. Figure 1 Example of the definition of two synaptic elements for an integrate and fire neuron using PyNEST. Here, the growth curve of the synaptic element is a Gaussian defined by three parameters: υ (the growth rate), ηz and e (intersections ... This implementation of the MSP in NEST allows neuroscientists to address important scientific questions on how large-scale networks rewire their connectivity in response to distortions in electrical activity balances

Assessing brain plasticity across the lifespan with transcranial magnetic stimulation: why, how, and what is the ultimate goal?

Sustaining brain and cognitive function across the lifespan must be one of the main biomedical goals of the twenty-first century. We need to aim to prevent neuropsychiatric diseases and, thus, to identify and remediate brain and cognitive dysfunction before clinical symptoms manifest and disability develops. The brain undergoes a complex array of changes from developmental years into old age, putatively the underpinnings of changes in cognition and behavior throughout life. A functionally “normal” brain is a changing brain, a brain whose capacity and mechanisms of change are shifting appropriately from one time-point to another in a given individual's life. Therefore, assessing the mechanisms of brain plasticity across the lifespan is critical to gain insight into an individual's brain health. Indexing brain plasticity in humans is possible with transcranial magnetic stimulation (TMS), which, in combination with neuroimaging, provides a powerful tool for exploring local cortical and brain network plasticity. Here, we review investigations to date, summarize findings, and discuss some of the challenges that need to be solved to enhance the use of TMS measures of brain plasticity across all ages. Ultimately, TMS measures of plasticity can become the foundation for a brain health index (BHI) to enable objective correlates of an individual's brain health over time, assessment across diseases and disorders, and reliable evaluation of indicators of efficacy of future preventive and therapeutic interventions.