Modeling motor connectivity using TMS/PET and structural equation modeling

Transcranial magnetic stimulation of the brain: What is stimulated? - A consensus and critical position paper

Transcranial (electro)magnetic stimulation (TMS) is currently the method of choice to non-invasively induce neural activity in the human brain. A single transcranial stimulus induces a time-varying electric field in the brain that may evoke action potentials in cortical neurons. The spatial relationship between the locally induced electric field and the stimulated neurons determines axonal depolarization. The induced electric field is influenced by the conductive properties of the tissue compartments and is strongest in the superficial parts of the targeted cortical gyri and underlying white matter. TMS likely targets axons of both excitatory and inhibitory neurons. The propensity of individual axons to fire an action potential in response to TMS depends on their geometry, myelination and spatial relation to the imposed electric field and the physiological state of the neuron. The latter is determined by its transsynaptic dendritic and somatic inputs, intrinsic membrane potential and firing rate. Modeling work suggests that the primary target of TMS is axonal terminals in the crown top and lip regions of cortical gyri. The induced electric field may additionally excite bends of myelinated axons in the juxtacortical white matter below the gyral crown. Neuronal excitation spreads ortho- and antidromically along the stimulated axons and causes secondary excitation of connected neuronal populations within local intracortical microcircuits in the target area. Axonal and transsynaptic spread of excitation also occurs along cortico-cortical and cortico-subcortical connections, impacting on neuronal activity in the targeted network. Both local and remote neural excitation depend critically on the functional state of the stimulated target area and network. TMS also causes substantial direct co-stimulation of the peripheral nervous system. Peripheral co-excitation propagates centrally in auditory and somatosensory networks, but also produces brain responses in other networks subserving multisensory integration, orienting or arousal. The complexity of the response to TMS warrants cautious interpretation of its physiological and behavioural consequences, and a deeper understanding of the mechanistic underpinnings of TMS will be critical for advancing it as a scientific and therapeutic tool.

Maladaptive motor cortical excitability and connectivity in polyneuropathy with neuropathic pain

BACKGROUND

Sensory symptoms, especially neuropathic pain, are common in polyneuropathy. Conventional diagnostic tools can evaluate structural or functional impairment of nerves but cannot reveal mechanisms of neuropathic pain. Changes in the brain after polyneuropathy may play roles in the genesis of neuropathic pain.

METHODS

This cross-sectional study investigated changes of cortical excitability within left primary motor cortex (M1) by measuring resting motor thresholds, short-interval intracortical inhibition (SICI), intracortical facilitation (ICF), and afferent inhibition between polyneuropathy patients and controls, and investigated the correlates of these parameters with neuropathic pain and the M1 structural and functional connectivity assessed by diffusion tractography imaging and functional MRI.

RESULTS

Thirty-three painful and 15 non-painful neuropathic patients and 21 controls were enrolled. There were no differences in intraepidermal nerve fiber density, nerve conduction study, thermal thresholds, or autonomic functional tests between patients with and without neuropathic pain. Compared to controls, neuropathic patients exhibited similar resting motor thresholds or afferent inhibition, but attenuated SICI and augmented ICF, especially in painful patients. Changes of intracortical excitability in neuropathic patients were correlated with intensities of neuropathic pain, and different presentations of SICI and ICF were noted between patients with and without thermal paresthesia. Additionally, short latency afferent inhibition at interstimulus intervals of 20 ms was associated with structural connectivity of left M1 with brain areas associated with pain perception.

CONCLUSIONS

Maladaptive cortical excitability with altered structural connectivity in left M1 developed after peripheral nerve degeneration and was associated with neuropathic pain and sensory symptoms in polyneuropathy.

A multimodal approach using TMS and EEG reveals neurophysiological changes in Parkinson's disease

INTRODUCTION

Alterations in large scale neural networks leading to neurophysiological changes have been described in Parkinson's disease (PD). The combination of transcranial magnetic stimulation (TMS) and electroencephalography (EEG) has been suggested as a promising tool to identify and quantify neurophysiological mechanisms. The aim of this study was to investigate specific changes in electrical brain activity in response to stimulation of four brain areas in patients with PD.

METHODS

21 healthy controls and 32 patients with PD underwent a combined TMS-EEG assessment that included stimulation of four brain areas: left M1, right M1, left dorso-lateral prefrontal cortex (DLPFC), and right DLPFC. Six measures were calculated to characterize the TMS evoked potentials (TEP) using EEG: (1) wave form adherence (WFA), (2) late phase deflection (LPD), (3) early phase deflection (EPD), (4) short-term plasticity (STP), (5) inter-trial adherence, and (6) connectivity between right and left M1 and DLPFC. A Linear mixed-model was used to compare these measures between groups and areas stimulated.

RESULTS

Patients with PD showed lower WFA (p = 0.052), lower EPD (p = 0.009), lower inter-trial adherence (p < 0.001), and lower connectivity between homologs areas (p = 0.050), compared to healthy controls. LPD and STP measures were not different between the groups. In addition, lower inter-trial adherence correlated with longer disease duration (r = -0.355, p = 0.050).

CONCLUSIONS

Our findings provide evidence to various alterations in neurophysiological measures in patients with PD. The higher cortical excitability along with increased variability and lower widespread of the evoked potentials in PD can elucidate different aspects related to the pathophysiology of the disease.

Effective connectivity alteration according to recurrence in transient global amnesia

PURPOSE

This study aimed to evaluate alterations in structural covariance network and effective connectivity of the intrahippocampal circuit in patients with transient global amnesia (TGA). We also investigated whether there were differences of them according to recurrence.

METHODS

We enrolled 88 patients with TGA and 50 healthy controls. We classified patients with TGA into two groups: the single event group (N = 77) and recurrent events group (N = 11). We performed volumetric analysis using the FreeSurfer program and structural covariance network analysis based on the structural volumes using a graph theoretical analysis in patients with TGA and healthy controls. The effective connectivity of the intrahippocampal circuit was also evaluated using structural equation modeling.

RESULTS

There were no significant differences between patients with all TGA events/a single TGA event and healthy controls with regard to global structural covariance network. However, patients with recurrent events had significant alterations in global structural covariance network with a decrease in the small-worldness index (0.907 vs. 0.970, p = 0.032). In patients with all events/a single, there were alterations in effective connectivity from the entorhinal cortex to CA4, only. However, in patients with recurrent events, there were alterations in effective connectivity from the subiculum to the fimbria as well as from the entorhinal cortex to CA4 in bilateral hemispheres.

CONCLUSION

Our study revealed significant alterations in structural covariance network and disruption of the intrahippocampal circuit in patients with TGA compared to healthy controls, which is more prominent when amnestic events recurred. It could be related to the pathogenesis of TGA.

Design of TMS coils with reduced Lorentz forces: application to concurrent TMS-fMRI

OBJECTIVE

Interleaving TMS (transcranial magnetic stimulation) with fMRI (functional Magnetic Resonance Imaging) is a promising technique to study functional connectivity in the human brain, but its development is being restricted by technical limitations, such as that due to the interaction of the TMS current pulses with the magnetic fields of an MRI scanner. In this work, a TMS coil design method capable of controlling Lorentz forces experienced by the coil in the presence of static magnetic fields is presented.

APPROACH

The suggested approach is based on an existing inverse boundary element method (IBEM) for TMS coil design, in which new electromagnetic computational models of the Lorentz forces have been included to be controlled in the design process.

MAIN RESULTS

To demonstrate the validity of this technique, it has been used for the design and simulation of TMS coils wound on rectangular flat, spherical and hemispherical surfaces with improved mechanical stability. The obtained results confirm that TMS coils with reduced Lorentz forces inside the static main field of an MRI scanner can be produced, which is achieved to the detriment of other coil performance parameters.

SIGNIFICANCE

The proposed approach provides an efficient tool to design TMS stimulators of a wide range of coil geometries with improved mechanical stability, which can be extremely useful to overcome current limitations for interleaved TMS-fMRI.

Ventral striatum links motivational and motor networks during operant-conditioned movement in rats

Voluntary actions require motives. It is already known that the medial prefrontal cortex (MPFC) assess the motivational values. However, it remains unclear how the motivational process gains access to the motor execution system in the brain. Here we present evidence that the ventral striatum (VS) plays a hub-like role in mediating motivational and motor processing in operant behavior. We used positron emission tomography (PET) to detect the neural activation areas associated with motivational action. Using obtained regions, partial correlation analysis was performed to examine how the motivational signals propagate to the motor system. The results revealed that VS activity propagated to both MPFC and primary motor cortex through the thalamus. Moreover, muscimol injection into the VS suppressed the motivational behavior, supporting the idea of representations of motivational signals in VS that trigger motivational behavior. These results suggest that the VS-thalamic pathway plays a pivotal role for both motivational processing through interactions with the MPFC and for motor processing through interactions with the motor BG circuits.

Assessing robustness against potential publication bias in Activation Likelihood Estimation (ALE) meta-analyses for fMRI

The importance of integrating research findings is incontrovertible and procedures for coordinate-based meta-analysis (CBMA) such as Activation Likelihood Estimation (ALE) have become a popular approach to combine results of fMRI studies when only peaks of activation are reported. As meta-analytical findings help building cumulative knowledge and guide future research, not only the quality of such analyses but also the way conclusions are drawn is extremely important. Like classical meta-analyses, coordinate-based meta-analyses can be subject to different forms of publication bias which may impact results and invalidate findings. The file drawer problem refers to the problem where studies fail to get published because they do not obtain anticipated results (e.g. due to lack of statistical significance). To enable assessing the stability of meta-analytical results and determine their robustness against the potential presence of the file drawer problem, we present an algorithm to determine the number of noise studies that can be added to an existing ALE fMRI meta-analysis before spatial convergence of reported activation peaks over studies in specific regions is no longer statistically significant. While methods to gain insight into the validity and limitations of results exist for other coordinate-based meta-analysis toolboxes, such as Galbraith plots for Multilevel Kernel Density Analysis (MKDA) and funnel plots and egger tests for seed-based d mapping, this procedure is the first to assess robustness against potential publication bias for the ALE algorithm. The method assists in interpreting meta-analytical results with the appropriate caution by looking how stable results remain in the presence of unreported information that may differ systematically from the information that is included. At the same time, the procedure provides further insight into the number of studies that drive the meta-analytical results. We illustrate the procedure through an example and test the effect of several parameters through extensive simulations. Code to generate noise studies is made freely available which enables users to easily use the algorithm when interpreting their results.

Changes in Effective Connectivity Network Patterns in Drug Abusers, Treated With Different Methods

Introduction:

Various treatment methods for drug abusers will result in different success rates. This is partly due to different neural assumptions and partly due to various rate of relapse in abusers because of different circumstances. Investigating the brain activation networks of treated subjects can reveal the hidden mechanisms of the therapeutic methods.

Methods:

We studied three groups of subjects: heroin abusers treated with abstinent based therapy (ABT) method, heroin abusers treated with Methadone Maintenance Therapy (MMT) method, and a control group. They were all scanned with functional magnetic resonance imaging (fMRI), using a 6-block task, where each block consisted of the rest-craving-rest-neutral sequence. Using the dynamic causal modeling (DCM) algorithm, brain effective connectivity network (caused by the drug craving stimulation) was quantified for all groups. In this regard, 4 brain areas were selected for this analysis based on previous findings: ventromedial prefrontal cortex (VMPFC), dorsolateral prefrontal cortex (DLPFC), amygdala, and ventral striatum.

Results:

Our results indicated that the control subjects did not show significant brain activations after craving stimulations, but the two other groups showed significant brain activations in all 4 regions. In addition, VMPFC showed higher activations in the ABT group compared to the MMT group. The effective connectivity network suggested that the control subjects did not have any direct input from drug-related cue indices, while the other two groups showed reactions to these cues. Also, VMPFC displayed an important role in ABT group. In encountering the craving pictures, MMT subjects manifest a very simple mechanism compared to other groups.

Conclusion:

This study revealed an activation network similar to the emotional and inhibitory control networks observed in drug abusers in previous works. The results of DCM analysis also support the regulatory role of frontal regions on bottom regions. Furthermore, this study demonstrates the different effective connectivity patterns after drug abuse treatment and in this way helps the experts in the field.

Effective connectivity in temporal lobe epilepsy with hippocampal sclerosis

OBJECTIVE

We hypothesized that temporal lobe epilepsy (TLE) patients with and without hippocampal sclerosis (HS) showed differences in their limbic networks. This study aimed to evaluate the role of the thalamus in TLE patients with HS.

MATERIALS AND METHODS

Twenty-nine TLE patients with HS and 30 controls were enrolled in this study. In addition, we included eight TLE patients without HS as a disease control group. Using whole-brain T1-weighted MRIs, we analyzed the volumes of the limbic structures, including the hippocampus, thalamus, and total cortex, with FreeSurfer 5.1. We also investigated the effective connectivity among these structures using SPSS Amos 21 based on these volumetric measures. Moreover, we quantified correlations between epilepsy duration and the volumes of these structures.

RESULTS

There was a statistically significant effective connectivity from the hippocampus to the thalamus in TLE patients with HS. Moreover, the volumes of the left and right thalamus were negatively correlated with epilepsy duration (r=-.42, P=.0315 and r=-.52, P=.0062, respectively). However, neither TLE patients without HS nor normal controls had a significant effective connectivity from the hippocampus to the thalamus.

CONCLUSIONS

The limbic networks of TLE patients with and without HS could be different, and the thalamus might play a critical role in TLE patients with HS.

Progressive Bidirectional Age-Related Changes in Default Mode Network Effective Connectivity across Six Decades

The default mode network (DMN) is a set of regions that is tonically engaged during the resting state and exhibits task-related deactivation that is readily reproducible across a wide range of paradigms and modalities. The DMN has been implicated in numerous disorders of cognition and, in particular, in disorders exhibiting age-related cognitive decline. Despite these observations, investigations of the DMN in normal aging are scant. Here, we used blood oxygen level dependent (BOLD) functional magnetic resonance imaging (fMRI) acquired during rest to investigate age-related changes in functional connectivity of the DMN in 120 healthy normal volunteers comprising six, 20-subject, decade cohorts (from 20–29 to 70–79). Structural equation modeling (SEM) was used to assess age-related changes in inter-regional connectivity within the DMN. SEM was applied both using a previously published, meta-analytically derived, node-and-edge model, and using exploratory modeling searching for connections that optimized model fit improvement. Although the two models were highly similar (only 3 of 13 paths differed), the sample demonstrated significantly better fit with the exploratory model. For this reason, the exploratory model was used to assess age-related changes across the decade cohorts. Progressive, highly significant changes in path weights were found in 8 (of 13) paths: four rising, and four falling (most changes were significant by the third or fourth decade). In all cases, rising paths and falling paths projected in pairs onto the same nodes, suggesting compensatory increases associated with age-related decreases. This study demonstrates that age-related changes in DMN physiology (inter-regional connectivity) are bidirectional, progressive, of early onset and part of normal aging.

Modeling the effective connectivity of the visual network in healthy and photosensitive, epileptic baboons

The baboon provides a model of photosensitive, generalized epilepsy. This study compares cerebral blood flow responses during intermittent light stimulation (ILS) between photosensitive (PS) and healthy control (CTL) baboons using H 2 (15) O-PET. We examined effective connectivity associated with visual stimulation in both groups using structural equation modeling (SEM). Eight PS and six CTL baboons, matched for age, gender and weight, were classified on the basis of scalp EEG findings performed during the neuroimaging studies. Five H 2 (15) O-PET studies were acquired alternating between resting and activation (ILS at 25 Hz) scans. PET images were acquired in 3D mode and co-registered with MRI. SEM demonstrated differences in neural connectivity between PS and CTL groups during ILS that were not previously identified using traditional activation analyses. First-level pathways consisted of similar posterior-to-anterior projections in both groups. While second-level pathways were mainly lateralized to the left hemisphere in the CTL group, they consisted of bilateral anterior-to-posterior projections in the PS baboons. Third- and fourth-level pathways were only evident in PS baboons. This is the first functional neuroimaging study used to model the photoparoxysmal response (PPR) using a primate model of photosensitive, generalized epilepsy. Evidence of increased interhemispheric connectivity and bidirectional feedback loops in the PS baboons represents electrophysiological synchronization associated with the generation of epileptic discharges. PS baboons demonstrated decreased model stability compared to controls, which may be attributed to greater variability in the driving response or PPRs, or to the influence of regions not included in the model.

Repetitive Transcranial Magnetic Stimulation Educes Frequency-Specific Causal Relationships in the Motor Network

BACKGROUND

Repetitive transcranial magnetic stimulation (rTMS) has the potential to treat brain disorders by modulating the activity of disease-specific brain networks, yet the rTMS frequencies used are delivered in a binary fashion - excitatory (>1 Hz) and inhibitory (≤1 Hz).

OBJECTIVE

To assess the effective connectivity of the motor network at different rTMS stimulation rates during positron-emission tomography (PET) and confirm that not all excitatory rTMS frequencies act on the motor network in the same manner.

METHODS

We delivered image-guided, supra-threshold rTMS at 3 Hz, 5 Hz, 10 Hz, 15 Hz and rest (in separate randomized sessions) to the primary motor cortex (M1) of the lightly anesthetized baboon during PET imaging. Each rTMS/PET session was analyzed using normalized cerebral blood flow (CBF) measurements. Path analysis - using structural equation modeling (SEM) - was employed to determine the effective connectivity of the motor network at all rTMS frequencies. Once determined, the final model of the motor network was used to assess any differences in effective connectivity at each rTMS frequency.

RESULTS

The exploratory SEM produced a very well fitting final network model (χ(2) = 18.04, df = 21, RMSEA = 0.000, p = 0.647, TLI = 1.12) using seven nodes of the motor network. 5 Hz rTMS produced the strongest path coefficients in four of the seven connections, suggesting that this frequency is the optimal rTMS frequency for stimulation the motor network (as a whole); however, the premotor cerebellum connection was optimally stimulated at 10 Hz rTMS and the supplementary motor area caudate connection was optimally driven at 15 Hz rTMS.

CONCLUSION(S)

We have demonstrated that 1) 5 Hz rTMS revealed the strongest path coefficients (i.e. causal influence) on the nodes of the motor network, 2) stimulation at "excitatory" rTMS frequencies did not produce increased CBF in all nodes of the motor network, 3) specific rTMS frequencies may be used to target specific none-to-node interactions in the stimulated brain network, and 4) more research needs to be performed to determine the optimum frequency for each brain circuit and/or node.

Functional activation and effective connectivity differences in adolescent marijuana users performing a simulated gambling task

Background. Adolescent marijuana use is associated with structural and functional differences in forebrain regions while performing memory and attention tasks. In the present study, we investigated neural processing in adolescent marijuana users experiencing rewards and losses. Fourteen adolescents with frequent marijuana use (>5 uses per week) and 14 nonuser controls performed a computer task where they were required to guess the outcome of a simulated coin flip while undergoing magnetic resonance imaging. Results. Across all participants, “Wins” and “Losses” were associated with activations including cingulate, middle frontal, superior frontal, and inferior frontal gyri and declive activations. Relative to controls, users had greater activity in the middle and inferior frontal gyri, caudate, and claustrum during “Wins” and greater activity in the anterior and posterior cingulate, middle frontal gyrus, insula, claustrum, and declive during “Losses.” Effective connectivity analyses revealed similar overall network interactions among these regions for users and controls during both “Wins” and “Losses.” However, users and controls had significantly different causal interactions for 10 out of 28 individual paths during the “Losses” condition. Conclusions. Collectively, these results indicate adolescent marijuana users have enhanced neural responses to simulated monetary rewards and losses and relatively subtle differences in effective connectivity.

The neural changes in connectivity of the voice network during voice pitch perturbation

Voice control is critical to communication. To date, studies have used behavioral, electrophysiological and functional data to investigate the neural correlates of voice control using perturbation tasks, but have yet to examine the interactions of these neural regions. The goal of this study was to use structural equation modeling of functional neuroimaging data to examine network properties of voice with and without perturbation. Results showed that the presence of a pitch shift, which was processed as an error in vocalization, altered connections between right STG and left STG. Other regions that revealed differences in connectivity during error detection and correction included bilateral inferior frontal gyrus, and the primary and pre motor cortices. Results indicated that STG plays a critical role in voice control, specifically, during error detection and correction. Additionally, pitch perturbation elicits changes in the voice network that suggest the right hemisphere is critical to pitch modulation.

Distant functional connectivity for bimanual finger coordination declines with aging: an fMRI and SEM exploration

Although bimanual finger coordination is known to decline with aging, it still remains unclear how exactly the neural substrates underlying the coordination differ between young and elderly adults. The present study focused on: (1) characterization of the functional connectivity within the motor association cortex which is required for successful bimanual finger coordination, and (2) to elucidate upon its age-related decline. To address these objectives, we utilized functional magnetic resonance imaging (fMRI) in combination with structural equation modeling (SEM). This allowed us to compare functional connectivity models between young and elderly age groups during a visually guided bimanual finger movement task using both stable in-phase and complex anti-phase modes. Our SEM exploration of functional connectivity revealed significant age-related differences in connections surrounding the PMd in the dominant hemisphere. In the young group who generally displayed accurate behavior, the SEM model for the anti-phase mode exhibited significant connections from the dominant PMd to the non-dominant SPL, and from the dominant PMd to the dominant S1. However, the model for the elderly group's anti-phase mode in which task performance dropped, did not exhibit significant connections within the aforementioned regions. These results suggest that: (1) the dominant PMd acts as an intermediary to invoke intense intra- and inter-hemispheric connectivity with distant regions among the higher motor areas including the dominant S1 and the non-dominant SPL in order to achieve successful bimanual finger coordination, and (2) the distant connectivity among the higher motor areas declines with aging, whereas the local connectivity within the bilateral M1 is enhanced for the complex anti-phase mode. The latter may underlie the elderly's decreased performance in the complex anti-phase mode of the bimanual finger movement task.

Brain stimulation and functional imaging with fMRI and PET

The use of functional brain imaging techniques, including positron emission tomography (PET), single-photon emission computed tomography (SPECT), and functional magnetic resonance imaging (fMRI), has allowed for monitoring neuronal and neurochemical activities in the living human brain and identifying abnormal changes in various neurological and psychiatric diseases. Combining these methods with techniques such as deep brain stimulation (DBS) and transcranial magnetic stimulation (TMS) has greatly advanced our understanding of the effects of such treatment on brain activity at targeted regions as well as specific disease-related networks. Indeed, recent network-level analysis focusing on inter-regional covarying activities in data interpretation has unveiled several key mechanisms underlying the therapeutic effects of brain stimulation. However, non-negligible discrepancies have been reported in the literature, attributable in part to the heterogeneity of both imaging and brain stimulation techniques. This chapter summarizes recent studies that combine brain imaging and brain stimulation, and includes discussion of future direction in these lines of research.

Meta-analysis in human neuroimaging: computational modeling of large-scale databases

Spatial normalization--applying standardized coordinates as anatomical addresses within a reference space--was introduced to human neuroimaging research nearly 30 years ago. Over these three decades, an impressive series of methodological advances have adopted, extended, and popularized this standard. Collectively, this work has generated a methodologically coherent literature of unprecedented rigor, size, and scope. Large-scale online databases have compiled these observations and their associated meta-data, stimulating the development of meta-analytic methods to exploit this expanding corpus. Coordinate-based meta-analytic methods have emerged and evolved in rigor and utility. Early methods computed cross-study consensus, in a manner roughly comparable to traditional (nonimaging) meta-analysis. Recent advances now compute coactivation-based connectivity, connectivity-based functional parcellation, and complex network models powered from data sets representing tens of thousands of subjects. Meta-analyses of human neuroimaging data in large-scale databases now stand at the forefront of computational neurobiology.

Modulation of human corticospinal excitability by paired associative stimulation

Paired Associative Stimulation (PAS) has come to prominence as a potential therapeutic intervention for the treatment of brain injury/disease, and as an experimental method with which to investigate Hebbian principles of neural plasticity in humans. Prototypically, a single electrical stimulus is directed to a peripheral nerve in advance of transcranial magnetic stimulation (TMS) delivered to the contralateral primary motor cortex (M1). Repeated pairing of the stimuli (i.e., association) over an extended period may increase or decrease the excitability of corticospinal projections from M1, in manner that depends on the interstimulus interval (ISI). It has been suggested that these effects represent a form of associative long-term potentiation (LTP) and depression (LTD) that bears resemblance to spike-timing dependent plasticity (STDP) as it has been elaborated in animal models. With a large body of empirical evidence having emerged since the cardinal features of PAS were first described, and in light of the variations from the original protocols that have been implemented, it is opportune to consider whether the phenomenology of PAS remains consistent with the characteristic features that were initially disclosed. This assessment necessarily has bearing upon interpretation of the effects of PAS in relation to the specific cellular pathways that are putatively engaged, including those that adhere to the rules of STDP. The balance of evidence suggests that the mechanisms that contribute to the LTP- and LTD-type responses to PAS differ depending on the precise nature of the induction protocol that is used. In addition to emphasizing the requirement for additional explanatory models, in the present analysis we highlight the key features of the PAS phenomenology that require interpretation.

Time-varying coupling of EEG oscillations predicts excitability fluctuations in the primary motor cortex as reflected by motor evoked potentials amplitude: an EEG-TMS study

PURPOSE

Motor evoked potentials (MEPs) elicited by a train of consecutive, individual transcranial magnetic stimuli demonstrate fluctuations in amplitude with respect to time when recorded from a relaxed muscle. The influence of time-varying, instantaneous modifications of the electroencephalography (EEG) properties immediately preceding the transcranial magnetic stimulation (TMS) has rarely been explored. The aim of this study was to investigate the influence of the pre-TMS motor cortex and related areas EEG profile on time variants of the MEPs amplitude.

METHOD

MRI-navigated TMS and multichannel TMS-compatible EEG devices were used. For each experimental subject, post-hoc analysis of the MEPs amplitude that was based on the 50th percentile of the MEPs amplitude distribution provided two subgroups corresponding to "high" (large amplitude) and "low" (small amplitude). The pre-stimulus EEG characteristics (coherence and spectral profile) from the motor cortex and related areas were analyzed separately for the "high" and "low" MEPs and were then compared.

RESULTS

On the stimulated hemisphere, EEG coupling was observed more often in the high compared to the low MEP trials. Moreover, a paradigmatic pattern in which TMS was able to lead to significantly larger MEPs was found when the EEG of the stimulated motor cortex was coupled in the beta 2 band with the ipsilateral prefrontal cortex and in the delta band with the bilateral centro-parietal-occipital cortices.

CONCLUSION

This data provide evidence for a statistically significant influence of time-varying and spatially patterned synchronization of EEG rhythms in determining cortical excitability, namely motor cortex excitability in response to TMS.

Repetitive transcranial magnetic stimulation elicits rate-dependent brain network responses in non-human primates

BACKGROUND

Transcranial magnetic stimulation (TMS) has the potential to treat brain disorders by tonically modulating firing patterns in disease-specific neural circuits. The selection of treatment parameters for clinical repetitive transcranial magnetic stimulation (rTMS) trials has not been rule based, likely contributing to the variability of observed outcomes.

OBJECTIVE

To utilize our newly developed baboon (Papio hamadryas anubis) model of rTMS during position-emission tomography (PET) to quantify the brain's rate-response functions in the motor system during rTMS.

METHODS

We delivered image-guided, suprathreshold rTMS at 3 Hz, 5 Hz, 10 Hz, 15 Hz and rest (in separate randomized sessions) to the primary motor cortex (M1) of the lightly anesthetized baboon during PET imaging; we also administered a (reversible) paralytic to eliminate any somatosensory feedback due to rTMS-induced muscle contractions. Each rTMS/PET session was analyzed using normalized cerebral blood flow (CBF) measurements; statistical parametric images and the resulting areas of significance underwent post-hoc analysis to determine any rate-specific rTMS effects throughout the motor network.

RESULTS

The motor system's rate-response curves were unimodal and system wide--with all nodes in the network showing highly similar rate response functions--and an optimal network stimulation frequency of 5 Hz.

CONCLUSION(S)

These findings suggest that non-invasive brain stimulation may be more efficiently delivered at (system-specific) optimal frequencies throughout the targeted network and that functional imaging in non-human primates is a promising strategy for identifying the optimal treatment parameters for TMS clinical trials in specific brain regions and/or networks.

Automated regional behavioral analysis for human brain images

Behavioral categories of functional imaging experiments along with standardized brain coordinates of associated activations were used to develop a method to automate regional behavioral analysis of human brain images. Behavioral and coordinate data were taken from the BrainMap database (http://www.brainmap.org/), which documents over 20 years of published functional brain imaging studies. A brain region of interest (ROI) for behavioral analysis can be defined in functional images, anatomical images or brain atlases, if images are spatially normalized to MNI or Talairach standards. Results of behavioral analysis are presented for each of BrainMap's 51 behavioral sub-domains spanning five behavioral domains (Action, Cognition, Emotion, Interoception, and Perception). For each behavioral sub-domain the fraction of coordinates falling within the ROI was computed and compared with the fraction expected if coordinates for the behavior were not clustered, i.e., uniformly distributed. When the difference between these fractions is large behavioral association is indicated. A z-score ≥ 3.0 was used to designate statistically significant behavioral association. The left-right symmetry of ~100K activation foci was evaluated by hemisphere, lobe, and by behavioral sub-domain. Results highlighted the classic left-side dominance for language while asymmetry for most sub-domains (~75%) was not statistically significant. Use scenarios were presented for anatomical ROIs from the Harvard-Oxford cortical (HOC) brain atlas, functional ROIs from statistical parametric maps in a TMS-PET study, a task-based fMRI study, and ROIs from the ten "major representative" functional networks in a previously published resting state fMRI study. Statistically significant behavioral findings for these use scenarios were consistent with published behaviors for associated anatomical and functional regions.

Electrophysiological and functional connectivity of the human supplementary motor area

Neuro-imaging methods for detecting functional and structural inter-regional connectivity are in a rapid phase of development. While reports of regional connectivity patterns based on individual methods are becoming common, studies comparing the results of two or more connectivity-mapping methods remain rare. In this study, we applied transcranial magnetic stimulation during PET imaging (TMS/PET), a stimulation-based method, and meta-analytic connectivity modeling (MACM), a task-based method to map the connectivity patterns of the supplementary motor area (SMA). Further, we drew upon the behavioral domain meta-data of the BrainMap® database to characterize the behavioral domain specificity of two maps. Both MACM and TMS/PET detected multi-synaptic connectivity patterns, with the MACM-detected connections being more extensive. Both MACM and TMS/PET detected connections belonging to multiple behavioral domains, including action, cognition and perception. Finally, we show that the two connectivity-mapping methods are complementary in that, the MACM informed on the functional nature of SMA connections, while TMS/PET identified brain areas electrophysiologically connected with the SMA. Thus, we demonstrate that integrating multimodal database and imaging techniques can derive comprehensive connectivity maps of brain areas.

Exploration and modulation of brain network interactions with noninvasive brain stimulation in combination with neuroimaging

Much recent work in systems neuroscience has focused on how dynamic interactions between different cortical regions underlie complex brain functions such as motor coordination, language and emotional regulation. Various studies using neuroimaging and neurophysiologic techniques have suggested that in many neuropsychiatric disorders, these dynamic brain networks are dysregulated. Here we review the utility of combined noninvasive brain stimulation and neuroimaging approaches towards greater understanding of dynamic brain networks in health and disease. Brain stimulation techniques, such as transcranial magnetic stimulation and transcranial direct current stimulation, use electromagnetic principles to alter brain activity noninvasively, and induce focal but also network effects beyond the stimulation site. When combined with brain imaging techniques such as functional magnetic resonance imaging, positron emission tomography and electroencephalography, these brain stimulation techniques enable a causal assessment of the interaction between different network components, and their respective functional roles. The same techniques can also be applied to explore hypotheses regarding the changes in functional connectivity that occur during task performance and in various disease states such as stroke, depression and schizophrenia. Finally, in diseases characterized by pathologic alterations in either the excitability within a single region or in the activity of distributed networks, such techniques provide a potential mechanism to alter cortical network function and architectures in a beneficial manner.

Distributed processing; distributed functions?

After more than twenty years busily mapping the human brain, what have we learned from neuroimaging? This review (coda) considers this question from the point of view of structure-function relationships and the two cornerstones of functional neuroimaging; functional segregation and integration. Despite remarkable advances and insights into the brain's functional architecture, the earliest and simplest challenge in human brain mapping remains unresolved: We do not have a principled way to map brain function onto its structure in a way that speaks directly to cognitive neuroscience. Having said this, there are distinct clues about how this might be done: First, there is a growing appreciation of the role of functional integration in the distributed nature of neuronal processing. Second, there is an emerging interest in data-driven cognitive ontologies, i.e., that are internally consistent with functional anatomy. We will focus this review on the growing momentum in the fields of functional connectivity and distributed brain responses and consider this in the light of meta-analyses that use very large data sets to disclose large-scale structure-function mappings in the human brain.

Functional PET Evaluation of the Photosensitive Baboon

The baboon provides a unique, natural model of epilepsy in nonhuman primates. Additionally, photosensitivity of the epileptic baboon provides an important window into the mechanism of human idiopathic generalized epilepsies. In order to better understand the networks underlying this model, our group utilized functional positron emission tomography (PET) to compare cerebral blood flow (CBF) changes occurring during intermittent light stimulation (ILS) and rest between baboons photosensitive, epileptic (PS) and asymptomatic, control (CTL) animals. Our studies utilized subtraction and covariance analyses to evaluate CBF changes occurring during ILS across activation and resting states, but also evaluated CBF correlations with ketamine doses and interictal epileptic discharge (IED) rate during the resting state. Furthermore, our group also assessed the CBF responses related to variation of ILS in PS and CTL animals. CBF changes in the subtraction and covariance analyses reveal the physiological response and visual connectivity in CTL animals and pathophysiological networks underlying responses associated with the activation of ictal and interictal epileptic discharges in PS animals. The correlation with ketamine dose is essential to understanding differences in CBF responses between both groups, and correlations with IED rate provides an insight into an epileptic network independent of visual activation. Finally, the ILS frequency dependent changes can help develop a framework to study not only spatial connectivity but also the temporal sequence of regional activations and deactivations related to ILS. The maps generated by the CBF analyses will be used to target specific nodes in the epileptic network for electrophysiological evaluation using intracranial electrodes.

Evoked potentials in large-scale cortical networks elicited by TMS of the visual cortex

Single pulses of transcranial magnetic stimulation (TMS) result in distal and long-lasting oscillations, a finding directly challenging the virtual lesion hypothesis. Previous research supporting this finding has primarily come from stimulation of the motor cortex. We have used single-pulse TMS with simultaneous EEG to target seven brain regions, six of which belong to the visual system [left and right primary visual area V1, motion-sensitive human middle temporal cortex, and a ventral temporal region], as determined with functional MRI-guided neuronavigation, and a vertex "control" site to measure the network effects of the TMS pulse. We found the TMS-evoked potential (TMS-EP) over visual cortex consists mostly of site-dependent theta- and alphaband oscillations. These site-dependent oscillations extended beyond the stimulation site to functionally connected cortical regions and correspond to time windows where the EEG responses maximally diverge (40, 200, and 385 ms). Correlations revealed two site-independent oscillations ∼350 ms after the TMS pulse: a theta-band oscillation carried by the frontal cortex, and an alpha-band oscillation over parietal and frontal cortical regions. A manipulation of stimulation intensity at one stimulation site (right hemisphere V1-V3) revealed sensitivity to the stimulation intensity at different regions of cortex, evidence of intensity tuning in regions distal to the site of stimulation. Together these results suggest that a TMS pulse applied to the visual cortex has a complex effect on brain function, engaging multiple brain networks functionally connected to the visual system with both invariant and site-specific spatiotemporal dynamics. With this characterization of TMS, we propose an alternative to the virtual lesion hypothesis. Rather than a technique that simulates lesions, we propose TMS generates natural brain signals and engages functional networks.

Functional neuroimaging of the baboon during concurrent image-guided transcranial magnetic stimulation

Transcranial magnetic stimulation (TMS) has well-established applications in basic neuroscience and promising applications in neurological and psychiatric disorders. However the underlying mechanisms of TMS-induced alterations in brain function are not well understood. As a result, treatment design parameters are determined ad hoc and not informed by any coherent theory or model. Once the mechanisms underlying TMS's modulatory effects on brain systems are better understood and modeled, TMS's potential as a therapeutic and/or investigative tool will be more readily explored and exploited. An animal model is better suited to study different TMS variables, therefore we developed a baboon model to facilitate testing of some of the current theoretical models of TMS interactions with brain regions. We have demonstrated the feasibility of this approach by successfully imaging cerebral blood flow (CBF) changes with H(2)(15)O positron emission tomography imaging during high-frequency, suprathreshold repetitive TMS in the primary motor cortex of five healthy, adult baboons.

Minimizing within-experiment and within-group effects in Activation Likelihood Estimation meta-analyses

Activation Likelihood Estimation (ALE) is an objective, quantitative technique for coordinate-based meta-analysis (CBMA) of neuroimaging results that has been validated for a variety of uses. Stepwise modifications have improved ALE's theoretical and statistical rigor since its introduction. Here, we evaluate two avenues to further optimize ALE. First, we demonstrate that the maximum contribution of an experiment makes to an ALE map is related to the number of foci it reports and their proximity. We present a modified ALE algorithm that eliminates these within-experiment effects. However, we show that these effects only account for 2-3% of cumulative ALE values, and removing them has little impact on thresholded ALE maps. Next, we present an alternate organizational approach to datasets that prevents subject groups with multiple experiments in a dataset from influencing ALE values more than others. This modification decreases cumulative ALE values by 7-9%, changes the relative magnitude of some clusters, and reduces cluster extents. Overall, differences between results of the standard approach and these new methods were small. This finding validates previous ALE reports against concerns that they were driven by within-experiment or within-group effects. We suggest that the modified ALE algorithm is theoretically advantageous compared with the current algorithm, and that the alternate organization of datasets is the most conservative approach for typical ALE analyses and other CBMA methods. Combining the two modifications minimizes both within-experiment and within-group effects, optimizing the degree to which ALE values represent concordance of findings across independent reports.

[Brain imaging: towards a more detailed knowledge of the central nervous system]

Functional imaging allowed the knowledge of the brain to progress significantly. Recent findings about modeling of the activity and the interactivity measured by functional imaging are presented, they allow to apprehend brain functioning and brain plasticity both in normal and pathophysiological conditions. The study of specific circuits in networks should make it possible to define more realistically the dynamic functioning of the central nervous system which underlies numerous brain functions.

Transcranial magnetic stimulation and connectivity mapping: tools for studying the neural bases of brain disorders

There has been an increasing emphasis on characterizing pathophysiology underlying psychiatric and neurological disorders in terms of altered neural connectivity and network dynamics. Transcranial magnetic stimulation (TMS) provides a unique opportunity for investigating connectivity in the human brain. TMS allows researchers and clinicians to directly stimulate cortical regions accessible to electromagnetic coils positioned on the scalp. The induced activation can then propagate through long-range connections to other brain areas. Thus, by identifying distal regions activated during TMS, researchers can infer connectivity patterns in the healthy human brain and can examine how those patterns may be disrupted in patients with different brain disorders. Conversely, connectivity maps derived using neuroimaging methods can identify components of a dysfunctional network. Nodes in this dysfunctional network accessible as targets for TMS by virtue of their proximity to the scalp may then permit TMS-induced alterations of components of the network not directly accessible to TMS via propagated effects. Thus TMS can provide a portal for accessing and altering neural dynamics in networks that are widely distributed anatomically. Finally, when long-term modulation of network dynamics is induced by trains of repetitive TMS, changes in functional connectivity patterns can be studied in parallel with changes in patient symptoms. These correlational data can elucidate neural mechanisms underlying illness and recovery. In this review, we focus on the application of these approaches to the study of psychiatric and neurological illnesses.

On meta-analyses of imaging data and the mixture of records

Neumann et al. (2010) aim to find directed graphical representations of the independence and dependence relations among activities in brain regions by applying a search procedure to merged fMRI activity records from a large number of contrasts obtained under a variety of conditions. To that end, Neumann et al., obtain three graphical models, justifying their search procedure with simulations that find that merging the data sampled from probability distributions characterized by two distinct Bayes net graphs results in a graphical object that combines the edges in the individual graphs. We argue that the graphical objects they obtain cannot be interpreted as representations of conditional independence and dependence relations among localized neural activities; specifically, directed edges and directed pathways in their graphical results may be artifacts of the manner in which separate studies are combined in the meta-analytic procedure. With a larger simulation study, we argue that their simulation results with combined data sets are an artifact of their choice of examples. We provide sufficient conditions and necessary conditions for the merger of two or more probability distributions, each characterized by the Markov equivalence class of a directed acyclic graph, to be describable by a Markov equivalence class whose edges are a union of those for the individual distributions. Contrary to Neumann et al., we argue that the scientific value of searches for network representations from imaging data lies in attempting to characterize large scaled neural mechanisms, and we suggest several alternative strategies for combining data from multiple experiments.

Changes in regional activity are accompanied with changes in inter-regional connectivity during 4 weeks motor learning

Structural equation modeling (SEM) and fMRI were used to test whether changes in the regional activity are accompanied by changes in the inter-regional connectivity as motor practice progresses. Ten healthy subjects were trained to perform finger movement task daily for 4 weeks. Three sessions of fMRI images were acquired within 4 weeks. The changes in inter-regional connectivity were evaluated by measuring the effective connectivity between the primary motor area (M1), supplementary motor area (SMA), dorsal premotor cortex (PMd), basal ganglia (BG), cerebellum (CB), and posterior ventrolateral prefrontal cortex (pVLPFC). The regional activities in M1 and SMA increased from pre-training to week 2 and decreased from week 2 to week 4. The inter-regional connectivity generally increased in strength (with SEM path coefficients becoming more positive or negative) as practice progressed. The increases in the strength of the inter-regional connectivity may reflect long-term reorganization of the skilled motor network. We suggest that the performance gain was achieved by dynamically tuning the inter-regional connectivity in the motor network.

Investigating the functional heterogeneity of the default mode network using coordinate-based meta-analytic modeling

The default mode network (DMN) comprises a set of regions that exhibit ongoing, intrinsic activity in the resting state and task-related decreases in activity across a range of paradigms. However, DMN regions have also been reported as task-related increases, either independently or coactivated with other regions in the network. Cognitive subtractions and the use of low-level baseline conditions have generally masked the functional nature of these regions. Using a combination of activation likelihood estimation, which assesses statistically significant convergence of neuroimaging results, and tools distributed with the BrainMap database, we identified core regions in the DMN and examined their functional heterogeneity. Meta-analytic coactivation maps of task-related increases were independently generated for each region, which included both within-DMN and non-DMN connections. Their functional properties were assessed using behavioral domain metadata in BrainMap. These results were integrated to determine a DMN connectivity model that represents the patterns of interactions observed in task-related increases in activity across diverse tasks. Subnetwork components of this model were identified, and behavioral domain analysis of these cliques yielded discrete functional properties, demonstrating that components of the DMN are differentially specialized. Affective and perceptual cliques of the DMN were identified, as well as the cliques associated with a reduced preference for motor processing. In summary, we used advanced coordinate-based meta-analysis techniques to explicate behavior and connectivity in the default mode network; future work will involve applying this analysis strategy to other modes of brain function, such as executive function or sensorimotor systems.

Coordinate-based activation likelihood estimation meta-analysis of neuroimaging data: a random-effects approach based on empirical estimates of spatial uncertainty

A widely used technique for coordinate-based meta-analyses of neuroimaging data is activation likelihood estimation (ALE). ALE assesses the overlap between foci based on modeling them as probability distributions centered at the respective coordinates. In this Human Brain Project/Neuroinformatics research, the authors present a revised ALE algorithm addressing drawbacks associated with former implementations. The first change pertains to the size of the probability distributions, which had to be specified by the used. To provide a more principled solution, the authors analyzed fMRI data of 21 subjects, each normalized into MNI space using nine different approaches. This analysis provided quantitative estimates of between-subject and between-template variability for 16 functionally defined regions, which were then used to explicitly model the spatial uncertainty associated with each reported coordinate. Secondly, instead of testing for an above-chance clustering between foci, the revised algorithm assesses above-chance clustering between experiments. The spatial relationship between foci in a given experiment is now assumed to be fixed and ALE results are assessed against a null-distribution of random spatial association between experiments. Critically, this modification entails a change from fixed- to random-effects inference in ALE analysis allowing generalization of the results to the entire population of studies analyzed. By comparative analysis of real and simulated data, the authors showed that the revised ALE-algorithm overcomes conceptual problems of former meta-analyses and increases the specificity of the ensuing results without loosing the sensitivity of the original approach. It may thus provide a methodologically improved tool for coordinate-based meta-analyses on functional imaging data.

Motor imagery after stroke: relating outcome to motor network connectivity

OBJECTIVE

Neuroplasticity is essential for recovery after stroke and is the target for new stroke therapies. During recovery from subcortical motor stroke, brain activations associated with movement may appear normal despite residual functional impairment. This raises an important question: how far does recovery of motor performance depend on the processes that precede movement execution involving the premotor and prefrontal cortex, rather than recovery of the corticospinal system alone?

METHODS

We examined stroke patients with functional magnetic resonance imaging while they either imagined or executed a finger-thumb opposition sequence. In addition to classical analyses of regional activations, we studied neuroplasticity in terms of differential network connectivity using structural equation modeling. The study included 8 right-handed patients who had suffered a left-hemisphere subcortical ischemic stroke with paresis, and 13 age-matched healthy controls.

RESULTS

With good functional recovery, the regional activations had returned to normal in patients. However, connectivity within the extended motor network remained abnormal. These abnormalities were seen predominantly during motor imagery and correlated with motor performance.

INTERPRETATION

Our results indicate that neuroplasticity can manifest itself as differences in connectivity among cortical areas remote from the infarct, rather than in the degree of regional activation. Connection strengths between nodes of the cortical motor network correlate with motor outcome. The altered organization of connectivity of the prefrontal areas may reflect the role of the prefrontal cortex in higher order planning of movement. Our results are relevant to the assessment and understanding of emerging physical and neurophysiological therapies for stroke rehabilitation.

Learning partially directed functional networks from meta-analysis imaging data

We propose a new exploratory method for the discovery of partially directed functional networks from fMRI meta-analysis data. The method performs structure learning of Bayesian networks in search of directed probabilistic dependencies between brain regions. Learning is based on the co-activation of brain regions observed across several independent imaging experiments. In a series of simulations, we first demonstrate the reliability of the method. We then present the application of our approach in an extensive meta-analysis including several thousand activation coordinates from more than 500 imaging studies. Results show that our method is able to automatically infer Bayesian networks that capture both directed and undirected probabilistic dependencies between a number of brain regions, including regions that are frequently observed in motor-related and cognitive control tasks.

ALE Meta-Analysis Workflows Via the Brainmap Database: Progress Towards A Probabilistic Functional Brain Atlas

With the ever-increasing number of studies in human functional brain mapping, an abundance of data has been generated that is ready to be synthesized and modeled on a large scale. The BrainMap database archives peak coordinates from published neuroimaging studies, along with the corresponding metadata that summarize the experimental design. BrainMap was designed to facilitate quantitative meta-analysis of neuroimaging results reported in the literature and supports the use of the activation likelihood estimation (ALE) method. In this paper, we present a discussion of the potential analyses that are possible using the BrainMap database and coordinate-based ALE meta-analyses, along with some examples of how these tools can be applied to create a probabilistic atlas and ontological system of describing function–structure correspondences.

Network activation during bimanual movements in humans

The coordination of movement between the upper limbs is a function highly distributed across the animal kingdom. How the central nervous system generates such bilateral, synchronous movements, and how this differs from the generation of unilateral movements, remain uncertain. Electrophysiologic and functional imaging studies support that the activity of many brain regions during bimanual and unimanual movement is quite similar. Thus, the same brain regions (and indeed the same neurons) respond similarly during unimanual and bimanual movements as measured by electrophysiological responses. How then are different motor behaviors generated? To address this question, we studied unimanual and bimanual movements using fMRI and constructed networks of activation using Structural Equation Modeling (SEM). Our results suggest that (1) the dominant hemisphere appears to initiate activity responsible for bimanual movement; (2) activation during bimanual movement does not reflect the sum of right and left unimanual activation; (3) production of unimanual movement involves a network that is distinct from, and not a mirror of, the network for contralateral unimanual movement; and (4) using SEM, it is possible to obtain robust group networks representative of a population and to identify individual networks which can be used to detect subtle differences both between subjects as well as within a single subject over time. In summary, these results highlight a differential role for the dominant and non-dominant hemispheres during bimanual movements, further elaborating the concept of handedness and dominance. This knowledge increases our understanding of cortical motor physiology in health and after neurological damage.