Evidence for frequency-dependent cortical plasticity in the human brain

Effects of continuous versus intermittent theta-burst TMS on fMRI connectivity

Transcranial magnetic stimulation is a noninvasive technique that can be used to evoke distributed network-level effects. Previous work demonstrated that the Hippocampal-Cortical Network responds preferably (i.e., greater memory improvement and increases in hippocampal-network connectivity) to continuous theta-burst stimulation protocol relative to intermittent theta-burst and to 20-Hz rTMS. Here, these data were further analyzed to characterize effects of continuous versus intermittent theta-burst stimulation on network-level connectivity measures - as well as local connectedness - via resting-state fMRI. In contrast to theories that propose continuous and intermittent theta-burst cause local inhibitory versus excitatory effects, respectively, both protocols caused local decreases in fMRI connectivity around the stimulated parietal site. While iTBS caused decreases in connectivity across the hippocampal-cortical network, cTBS caused increases and decreases in connectivity across the network. cTBS had no effect on the parietal-cortical network, whereas iTBS caused decreases in the right parietal cortex (contralateral hemisphere to the stimulation target). These findings suggest that continuous theta-burst may have entrained the endogenous hippocampal-cortical network, whereas the intermittent train was unable to maintain entrainment that may have yielded the long-lasting effects measured in this study (i.e., within 20-min post-stimulation). Furthermore, these effects were specific to the hippocampal-cortical network, which has a putative endogenous functionally-relevant theta rhythm, and not to the parietal network. These results add to the growing body of evidence that suggests effects of theta-burst stimulation are not fully characterized by excitatory/inhibitory theories. Further work is required to understand local and network-level effects of noninvasive stimulation.

The impact of loneliness and social adaptation on depressive symptoms: Behavioral and brain measures evidence from a brain health perspective

Introduction

Early detection of depression is a cost-effective way to prevent adverse outcomes on brain physiology, cognition, and health. Here we propose that loneliness and social adaptation are key factors that can anticipate depressive symptoms.

Methods

We analyzed data from two separate samples to evaluate the associations between loneliness, social adaptation, depressive symptoms, and their neural correlates.

Results

For both samples, hierarchical regression models on self-reported data showed that loneliness and social adaptation have negative and positive effects on depressive symptoms. Moreover, social adaptation reduces the impact of loneliness on depressive symptoms. Structural connectivity analysis showed that depressive symptoms, loneliness, and social adaptation share a common neural substrate. Furthermore, functional connectivity analysis demonstrated that only social adaptation was associated with connectivity in parietal areas.

Discussion

Altogether, our results suggest that loneliness is a strong risk factor for depressive symptoms while social adaptation acts as a buffer against the ill effects of loneliness. At the neuroanatomical level, loneliness and depression may affect the integrity of white matter structures known to be associated to emotion dysregulation and cognitive impairment. On the other hand, socio-adaptive processes may protect against the harmful effects of loneliness and depression. Structural and functional correlates of social adaptation could indicate a protective role through long and short-term effects, respectively. These findings may aid approaches to preserve brain health via social participation and adaptive social behavior.

Decoupling of interacting neuronal populations by time-shifted stimulation through spike-timing-dependent plasticity

The synaptic organization of the brain is constantly modified by activity-dependent synaptic plasticity. In several neurological disorders, abnormal neuronal activity and pathological synaptic connectivity may significantly impair normal brain function. Reorganization of neuronal circuits by therapeutic stimulation has the potential to restore normal brain dynamics. Increasing evidence suggests that the temporal stimulation pattern crucially determines the long-lasting therapeutic effects of stimulation. Here, we tested whether a specific pattern of brain stimulation can enable the suppression of pathologically strong inter-population synaptic connectivity through spike-timing-dependent plasticity (STDP). More specifically, we tested how introducing a time shift between stimuli delivered to two interacting populations of neurons can effectively decouple them. To that end, we first used a tractable model, i.e., two bidirectionally coupled leaky integrate-and-fire (LIF) neurons, to theoretically analyze the optimal range of stimulation frequency and time shift for decoupling. We then extended our results to two reciprocally connected neuronal populations (modules) where inter-population delayed connections were modified by STDP. As predicted by the theoretical results, appropriately time-shifted stimulation causes a decoupling of the two-module system through STDP, i.e., by unlearning pathologically strong synaptic interactions between the two populations. Based on the overall topology of the connections, the decoupling of the two modules, in turn, causes a desynchronization of the populations that outlasts the cessation of stimulation. Decoupling effects of the time-shifted stimulation can be realized by time-shifted burst stimulation as well as time-shifted continuous simulation. Our results provide insight into the further optimization of a variety of multichannel stimulation protocols aiming at a therapeutic reshaping of diseased brain networks.

Noise-modulated multistable synapses in a Wilson-Cowan-based model of plasticity

Frequency-dependent plasticity refers to changes in synaptic strength in response to different stimulation frequencies. Resonance is a factor known to be of importance in such frequency dependence, however, the role of neural noise in the process remains elusive. Considering the brain is an inherently noisy system, understanding its effects may prove beneficial in shaping therapeutic interventions based on non-invasive brain stimulation protocols. The Wilson-Cowan (WC) model is a well-established model to describe the average dynamics of neural populations and has been shown to exhibit bistability in the presence of noise. However, the important question of how the different stable regimes in the WC model can affect synaptic plasticity when cortical populations interact has not yet been addressed. Therefore, we investigated plasticity dynamics in a WC-based model of interacting neural populations coupled with activity-dependent synapses in which a periodic stimulation was applied in the presence of noise of controlled intensity. The results indicate that for a narrow range of the noise variance, synaptic strength can be optimized. In particular, there is a regime of noise intensity for which synaptic strength presents a triple-stable state. Regulating noise intensity affects the probability that the system chooses one of the stable states, thereby controlling plasticity. These results suggest that noise is a highly influential factor in determining the outcome of plasticity induced by stimulation.

Human brain functional MRS reveals interplay of metabolites implicated in neurotransmission and neuroenergetics

While functional MRI (fMRI) localizes brain activation and deactivation, functional MRS (fMRS) provides insights into the underlying metabolic conditions. There is much interest in measuring task-induced and resting levels of metabolites implicated in neuroenergetics (e.g., lactate, glucose, or β-hydroxybutyrate (BHB)) and neurotransmission (e.g., γ-aminobutyric acid (GABA) or pooled glutamate and glutamine (Glx)). Ultra-high magnetic field (e.g., 7T) has boosted the fMRS quantification precision, reliability, and stability of spectroscopic observations using short echo-time (TE) ¹H-MRS techniques. While short TE ¹H-MRS lacks sensitivity and specificity for fMRS at lower magnetic fields (e.g., 3T or 4T), most of these metabolites can also be detected by J-difference editing (JDE) ¹H-MRS with longer TE to filter overlapping resonances. The ¹H-MRS studies show that JDE can detect GABA, Glx, lactate, and BHB at 3T, 4T and 7T. Most recently, it has also been demonstrated that JDE ¹H-MRS is capable of reliable detection of metabolic changes in different brain areas at various magnetic fields. Combining fMRS measurements with fMRI is important for understanding normal brain function, but also clinically relevant for mechanisms and/or biomarkers of neurological and neuropsychiatric disorders. We provide an up-to-date overview of fMRS research in the last three decades, both in terms of applications and technological advances. Overall the emerging fMRS techniques can be expected to contribute substantially to our understanding of metabolism for brain function and dysfunction.

Evidence from theta-burst stimulation that age-related de-differentiation of the hippocampal network is functional for episodic memory

Episodic memory is supported by hippocampal interactions with a distributed network. Aging is associated with memory decline and network de-differentiation. However, the role of de-differentiation in memory decline has not been directly tested. We reasoned that hippocampal network-targeted stimulation could test these theories, as age-related changes in the network response to stimulation would indicate network reorganization, and corresponding changes in memory would suggest that this reorganization is functional. We compared effects of stimulation on fMRI connectivity and memory in younger versus older adults. Theta-burst network-targeted stimulation of left lateral parietal cortex selectively increased hippocampal network connectivity and modulated memory in younger adults. In contrast, stimulation in older adults increased connectivity throughout the brain, without network selectivity, and did not influence memory. These findings provide evidence that network responses to stimulation are de-differentiated in aging and suggest that age-related de-differentiation is relevant for memory. This manuscript is part of the Special Issue entitled "Cognitive Neuroscience of Healthy and Pathological Aging" edited by Drs. M. N. Rajah, S. Belleville, and R. Cabeza. This article is part of the Virtual Special Issue titled COGNITIVE NEUROSCIENCE OF HEALTHY AND PATHOLOGICAL AGING. The full issue can be found on ScienceDirect at https://www.sciencedirect.com/journal/neurobiology-of-aging/special-issue/105379XPWJP.

Aerobic Exercise Induces Alternative Splicing of Neurexins in Frontal Cortex

Aerobic exercise (AE) is known to produce beneficial effects on brain health by improving plasticity, connectivity, and cognitive functions, but the underlying molecular mechanisms are still limited. Neurexins (Nrxns) are a family of presynaptic cell adhesion molecules that are important in synapsis formation and maturation. In vertebrates, three-neurexin genes (NRXN1, NRXN2, and NRXN3) have been identified, each encoding for α and β neurexins, from two independent promoters. Moreover, each Nrxns gene (1-3) has several alternative exons and produces many splice variants that bind to a large variety of postsynaptic ligands, playing a role in trans-synaptic specification, strength, and plasticity. In this study, we investigated the impact of a continuous progressive (CP) AE program on alternative splicing (AS) of Nrxns on two brain regions: frontal cortex (FC) and hippocampus. We showed that exercise promoted Nrxns1-3 AS at splice site 4 (SS4) both in α and β isoforms, inducing a switch from exon-excluded isoforms (SS4-) to exon-included isoforms (SS4+) in FC but not in hippocampus. Additionally, we showed that the same AE program enhanced the expression level of other genes correlated with synaptic function and plasticity only in FC. Altogether, our findings demonstrated the positive effect of CP AE on FC in inducing molecular changes underlying synaptic plasticity and suggested that FC is possibly a more sensitive structure than hippocampus to show molecular changes.

Heterogeneity across neural populations: Its significance for the dynamics and functions of neural circuits

Neural populations show patterns of synchronous activity, as they share common correlated inputs. Neurons in the cortex that are connected by strong synapses cause rapid firing explosions. In addition, areas that are connected by weaker synapses have a slower dynamics and they can contribute to asymmetries in the input distributions. The aim of this work is to develop a neural model to investigate how the heterogeneities in the synaptic input distributions affect different levels of organizational activity in the brain dynamics. We analytically show how small changes in the correlation inputs can cause large changes in the interactions of the outputs that lead to a phase transition, demonstrating that a simple variation in the direction of a biased skewed distribution in the neuronal inputs can generate a transition of states in the firing rate, passing from spontaneous silence ("down state") to an absolute spiking activity ("up state"). We present an exact quantification of the dynamics of the output variables, showing that when considering a biased skewed distribution in the inputs of neuronal population, the critical point is not in an asynchronous or synchronous state but rather at an intermediate value.

GABA Modulates Frequency-Dependent Plasticity in Humans

Frequency-dependent reorganization of the primary somatosensory cortex, together with perceptual changes, arises following repetitive sensory stimulation. Here, we investigate the role of GABA in this process. We co-stimulated two finger tips and measured GABA and Glx using magnetic resonance (MR) spectroscopy at the beginning and end of the stimulation. Participants performed a perceptual learning task before and after stimulation. There were 2 sessions with stimulation frequency either at or above the resonance frequency of the primary somatosensory cortex (23 and 39 Hz, respectively). Perceptual learning occurred following above resonance stimulation only, while GABA reduced during this condition. Lower levels of early GABA were associated with greater perceptual learning. One possible mechanism underlying this finding is that cortical disinhibition “unmasks” lateral connections within the cortex to permit adaptation to the sensory environment. These results provide evidence in humans for a frequency-dependent inhibitory mechanism underlying learning and suggest a mechanism-based approach for optimizing neurostimulation frequency.

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In the context of repetitive sensory stimulation, GABA release is frequency dependent

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Stimulating above the resonance frequency of the somatosensory cortex reduces GABA

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Perceptual learning is associated with a reduction in GABA

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Early GABA reduction opens a window for plasticity and learning

Cerebellar Theta and Beta Noninvasive Stimulation Rhythms Differentially Influence Episodic Memory versus Semantic Prediction

The human cerebellum is thought to interact with distributed brain networks to support cognitive abilities such as episodic memory and semantic prediction. Hippocampal and fronto-temporo-parietal networks that respectively support episodic memory versus semantic prediction have been associated with distinct endogenous oscillatory activity frequency bands: theta (∼3-8 Hz) versus beta (∼13-30 Hz) respectively. We sought to test whether it is possible to toggle cerebellar participation in episodic memory versus semantic prediction by noninvasively stimulating with theta versus beta rhythmic transcranial magnetic stimulation. In human subjects of both sexes, cerebellar theta stimulation improved episodic memory encoding but did not influence neural signals of semantic prediction, whereas beta stimulation of the same cerebellar location increased neural signals of semantic prediction but did not influence episodic memory encoding. This constitutes evidence for double dissociation of cerebellar contributions to semantic prediction versus episodic memory based on stimulation rhythm, supporting the hypothesis that the cerebellum can be biased to support these distinct cognitive abilities at the command of network-specific rhythmic activity.SIGNIFICANCE STATEMENT The cerebellum interacts with several distinct large-scale brain networks for cognitive function, but the factors governing selectivity of such interactions for particular functions are not fully understood. We tested the hypothesis that cerebellar contributions to cognition are guided by neural oscillations with function-specific frequency bands. We demonstrated that matching noninvasive stimulation to network-specific frequencies selectively enhanced episodic memory versus semantic prediction. These findings suggest that cerebellar contributions to cognitive networks are selected based on corresponding activity rhythms and could be used to develop cerebellar stimulation interventions for specific neurocognitive impairments.

Implementation of an Advanced Frequency-Based Hebbian Spike Timing Dependent Plasticity

The brain is provided with an enormous computing capability and exploits neural plasticity to store and elaborate complex information. One of the multiple mechanisms that neural circuits express is the Spike-timing-dependent plasticity (STDP), a form of long-term synaptic plasticity exploiting the time relationship between pre- and post-synaptic action potentials (i.e., neuron spikes). It has been found that in certain cases, for instance at the input stage of the cerebellum, between mossy fibers and granular neurons, the plasticity is not only driven by the timing of the spikes, but also by the oscillation frequency of the inputs. This complex behaviour has been implemented in this work, where we developed a novel form of advanced synaptic plasticity model to be used in a well-established neural network simulator (NEST). The subsequent tests proved the proper functioning of the plasticity and its range of applicability, demonstrating the possibility to adopt it in noisy and variable conditions, similar to the biological settings.

Dendritic and Axonal Propagation Delays May Shape Neuronal Networks With Plastic Synapses

Biological neuronal networks are highly adaptive and plastic. For instance, spike-timing-dependent plasticity (STDP) is a core mechanism which adapts the synaptic strengths based on the relative timing of pre- and postsynaptic spikes. In various fields of physiology, time delays cause a plethora of biologically relevant dynamical phenomena. However, time delays increase the complexity of model systems together with the computational and theoretical analysis burden. Accordingly, in computational neuronal network studies propagation delays were often neglected. As a downside, a classic STDP rule in oscillatory neurons without propagation delays is unable to give rise to bidirectional synaptic couplings, i.e., loops or uncoupled states. This is at variance with basic experimental results. In this mini review, we focus on recent theoretical studies focusing on how things change in the presence of propagation delays. Realistic propagation delays may lead to the emergence of neuronal activity and synaptic connectivity patterns, which cannot be captured by classic STDP models. In fact, propagation delays determine the inventory of attractor states and shape their basins of attractions. The results reviewed here enable to overcome fundamental discrepancies between theory and experiments. Furthermore, these findings are relevant for the development of therapeutic brain stimulation techniques aiming at shifting the diseased brain to more favorable attractor states.

Frequency-specific noninvasive modulation of memory retrieval and its relationship with hippocampal network connectivity

Episodic memory is thought to rely on interactions of the hippocampus with other regions of the distributed hippocampal-cortical network (HCN) via interregional activity synchrony in the theta frequency band. We sought to causally test this hypothesis using network-targeted transcranial magnetic stimulation. Healthy human participants completed four experimental sessions, each involving a different stimulation pattern delivered to the same individualized parietal cortex location of the HCN for all sessions. There were three active stimulation conditions, including continuous theta-burst stimulation, intermittent theta-burst stimulation, and beta-frequency (20-Hz) repetitive stimulation, and one sham condition. Resting-state fMRI and episodic memory testing were used to assess the impact of stimulation on hippocampal fMRI connectivity related to retrieval success. We hypothesized that theta-burst stimulation conditions would most strongly influence hippocampal-HCN fMRI connectivity and retrieval, given the hypothesized relevance of theta-band activity for HCN memory function. Continuous theta-burst stimulation improved item retrieval success relative to sham and relative to beta-frequency stimulation, whereas intermittent theta-burst stimulation led to numerical but nonsignificant item retrieval improvement. Mean hippocampal fMRI connectivity did not vary for any stimulation conditions, whereas individual differences in retrieval improvements due to continuous theta-burst stimulation were associated with corresponding increases in fMRI connectivity between the hippocampus and other HCN locations. No such memory-related connectivity effects were identified for the other stimulation conditions, indicating that only continuous theta-burst stimulation affected memory-related hippocampal-HCN connectivity. Furthermore, these effects were specific to the targeted HCN, with no significant memory-related fMRI connectivity effects for two distinct control brain networks. These findings support a causal role for fMRI connectivity of the hippocampus with the HCN in episodic memory retrieval and indicate that contributions of this network to retrieval are particularly sensitive to continuous theta-burst noninvasive stimulation.

A neural mass model to predict electrical stimulation evoked responses in human and non-human primate brain

OBJECTIVE

Deep brain stimulation (DBS) is a valuable tool for ameliorating drug resistant pathologies such as movement disorders and epilepsy. DBS is also being considered for complex neuro-psychiatric disorders, which are characterized by high variability in symptoms and slow responses that hinder DBS setting optimization. The objective of this work was to develop an in silico platform to examine the effects of electrical stimulation in regions neighboring a stimulated brain region.

APPROACH

We used the Jansen-Rit neural mass model of single and coupled nodes to simulate the response to a train of electrical current pulses at different frequencies (10-160 Hz) of the local field potential recorded in the amygdala and cortical structures in human subjects and a non-human primate.

RESULTS

We found that using a single node model, the evoked responses could be accurately modeled following a narrow range of stimulation frequencies. Including a second coupled node increased the range of stimulation frequencies whose evoked responses could be efficiently modeled. Furthermore, in a chronic recording from a non-human primate, features of the in vivo evoked response remained consistent for several weeks, suggesting that model re-parameterization for chronic stimulation protocols would be infrequent.

SIGNIFICANCE

Using a model of neural population activity, we reproduced the evoked response to cortical and subcortical stimulation in human and non-human primate. This modeling framework provides an environment to explore, safely and rapidly, a wide range of stimulation settings not possible in human brain stimulation studies. The model can be trained on a limited dataset of stimulation responses to develop an optimal stimulation strategy for an individual patient.

Propagation delays determine neuronal activity and synaptic connectivity patterns emerging in plastic neuronal networks

In plastic neuronal networks, the synaptic strengths are adapted to the neuronal activity. Specifically, spike-timing-dependent plasticity (STDP) is a fundamental mechanism that modifies the synaptic strengths based on the relative timing of pre- and postsynaptic spikes, taking into account the spikes' temporal order. In many studies, propagation delays were neglected to avoid additional dynamic complexity or computational costs. So far, networks equipped with a classic STDP rule typically rule out bidirectional couplings (i.e., either loops or uncoupled states) and are, hence, not able to reproduce fundamental experimental findings. In this review paper, we consider additional features, e.g., extensions of the classic STDP rule or additional aspects like noise, in order to overcome the contradictions between theory and experiment. In addition, we review in detail recent studies showing that a classic STDP rule combined with realistic propagation patterns is able to capture relevant experimental findings. In two coupled oscillatory neurons with propagation delays, bidirectional synapses can be preserved and potentiated. This result also holds for large networks of type-II phase oscillators. In addition, not only the mean of the initial distribution of synaptic weights, but also its standard deviation crucially determines the emergent structural connectivity, i.e., the mean final synaptic weight, the number of two-neuron loops, and the symmetry of the final connectivity pattern. The latter is affected by the firing rates, where more symmetric synaptic configurations emerge at higher firing rates. Finally, we discuss these findings in the context of the computational neuroscience-based development of desynchronizing brain stimulation techniques.

Principled Approaches to Direct Brain Stimulation for Cognitive Enhancement

In this brief review, we identify key areas of research that inform a systematic and targeted approach for invasive brain stimulation with the goal of modulating higher cognitive functions such as memory. We outline several specific challenges that must be successfully navigated in order to achieve this goal. Specifically, using direct brain stimulation to support memory requires demonstrating that (1) there are reliable neural patterns corresponding to different events and memory states, (2) stimulation can be used to induce these target activity patterns, and (3) inducing such patterns modulates memory in the expected directions. Invasive stimulation studies typically have not taken into account intrinsic brain states and dynamics, nor have they a priori targeted specific neural patterns that have previously been identified as playing an important role in memory. Moreover, the effects of stimulation on neural activity are poorly understood and are sensitive to multiple factors including the specific stimulation parameters, the processing state of the brain at the time of stimulation, and neuroanatomy of the stimulated region. As a result, several studies have reported conflicting results regarding the use of direct stimulation for memory modulation. Here, we review the latest findings relevant to these issues and discuss how we can gain better control over the effects of direct brain stimulation for modulating human memory and cognition.