Perturbation of resting-state network nodes preferentially propagates to structurally rather than functionally connected regions

Mapping Motor Cortical Network Excitability and Connectivity Changes in De Novo Parkinson's Disease

BACKGROUND

Transcranial magnetic stimulation-electroencephalography (TMS-EEG) has demonstrated decreased excitability in the primary motor cortex (M1) and increased excitability in the pre-supplementary motor area (pre-SMA) in moderate-advanced Parkinson's disease (PD).

OBJECTIVES

The aim was to investigate whether these abnormalities are evident from the early stages of the disease, their behavioral correlates, and relationship to cortico-subcortical connections.

METHODS

Twenty-eight early, drug-naive (de novo) PD patients and 28 healthy controls (HCs) underwent TMS-EEG to record TMS-evoked potentials (TEPs) from the primary motor cortex (M1) and the pre-SMA, kinematic recording of finger-tapping movements, and a 3T-MRI (magnetic resonance imaging) scan to obtain diffusion tensor imaging (DTI) reconstruction of white matter (WM) tracts connecting M1 to the ventral lateral anterior thalamic nucleus and pre-SMA to the anterior putamen.

RESULTS

We found reduced M1 TEP P30 amplitude in de novo PD patients compared to HCs and similar pre-SMA TEP N40 amplitude between groups. PD patients exhibited smaller amplitude and slower velocity in finger-tapping movements and altered structural integrity in WM tracts of interest, although these changes did not correlate with TEPs.

CONCLUSIONS

M1 hypoexcitability is a characteristic of PD from early phases and may be a marker of the parkinsonian state. Pre-SMA hyperexcitability is not evident in early PD and possibly emerges at later stages of the disease. © 2024 The Author(s). Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Neuron type-specific optogenetic stimulation for differential stroke recovery in chronic capsular infarct

Cortical neuromodulation (CNM) is widely used to promote recovery after stroke. Despite the beneficial results of CNM, the roles played by different neuron types in the effects of current CNM techniques are unable to be differentiated. Our aim was to use selective optogenetic cortical stimulation to explore how different subpopulations of neuronal cells contribute to poststroke recovery. We transduced the sensory-parietal cortex (SPC) of rats with CamKII-ChR2 (pyramidal neurons), PV-ChR2 (parvalbumin-expressing inhibitory neurons), or hSyn-ChR2 (pan-neuronal population) before inducing photothrombotic capsular infarct lesions. We found that selective stimulation of inhibitory neurons resulted in significantly greater motor recovery than stimulation of excitatory neurons or the pan-neuronal population. Furthermore, 2-deoxy-2-[18F] fluoro-D-glucose microPET (FDG-microPET) imaging revealed a significant reduction in cortical diaschisis and activation of the corticostriatal neural circuit, which were correlated with behavioral recovery in the PV-ChR2 group. The spatial pattern of brain-derived neurotrophic factor (BDNF) expression was evident in the stimulated cortex and underlying cortico-subcortical circuit. Our results indicate that the plasticity of inhibitory neurons is crucial for functional recovery after capsular infarct. Modifying CNM parameters to potentiate the stimulation of inhibitory neurons could improve poststroke outcomes.

Subcallosal cingulate deep brain stimulation evokes two distinct cortical responses via differential white matter activation

Subcallosal cingulate (SCC) deep brain stimulation (DBS) is an emerging therapy for refractory depression. Good clinical outcomes are associated with the activation of white matter adjacent to the SCC. This activation produces a signature cortical evoked potential (EP), but it is unclear which of the many pathways in the vicinity of SCC is responsible for driving this response. Individualized biophysical models were built to achieve selective engagement of two target bundles: either the forceps minor (FM) or cingulum bundle (CB). Unilateral 2 Hz stimulation was performed in seven patients with treatment-resistant depression who responded to SCC DBS, and EPs were recorded using 256-sensor scalp electroencephalography. Two distinct EPs were observed: a 120 ms symmetric response spanning both hemispheres and a 60 ms asymmetrical EP. Activation of FM correlated with the symmetrical EPs, while activation of CB was correlated with the asymmetrical EPs. These results support prior model predictions that these two pathways are predominantly activated by clinical SCC DBS and provide first evidence of a link between cortical EPs and selective fiber bundle activation.

Controlling target brain regions by optimal selection of input nodes

The network control theory framework holds great potential to inform neurostimulation experiments aimed at inducing desired activity states in the brain. However, the current applicability of the framework is limited by inappropriate modeling of brain dynamics, and an overly ambitious focus on whole-brain activity control. In this work, we leverage recent progress in linear modeling of brain dynamics (effective connectivity) and we exploit the concept of target controllability to focus on the control of a single region or a small subnetwork of nodes. We discuss when control may be possible with a reasonably low energy cost and few stimulation loci, and give general predictions on where to stimulate depending on the subset of regions one wishes to control. Importantly, using the robustly asymmetric effective connectome instead of the symmetric structural connectome (as in previous research), we highlight the fundamentally different roles in- and out-hubs have in the control problem, and the relevance of inhibitory connections. The large degree of inter-individual variation in the effective connectome implies that the control problem is best formulated at the individual level, but we discuss to what extent group results may still prove useful.

Network-level dynamics underlying a combined rTMS and psychotherapy treatment for major depressive disorder: An exploratory network analysis

Background

Despite the growing use of repetitive transcranial magnetic stimulation (rTMS) as a treatment for depression, there is a limited understanding of the mechanisms of action and how potential treatment-related brain changes help to characterize treatment response. To address this gap in understanding we investigated the effects of an approach combining rTMS with simultaneous psychotherapy on global functional connectivity.

Method

We compared task-related functional connectomes based on an idiographic goal priming task tied to emotional regulation acquired before and after simultaneous rTMS/psychotherapy treatment for patients with major depressive disorders and compared these changes to normative connectivity patterns from a set of healthy volunteers (HV) performing the same task.

Results

At baseline, compared to HVs, patients demonstrated hyperconnectivity of the DMN, cerebellum and limbic system, and hypoconnectivity of the fronto-parietal dorsal-attention network and visual cortex. Simultaneous rTMS/psychotherapy helped to normalize these differences, which were reduced after treatment. This finding suggests that the rTMS/therapy treatment regularizes connectivity patterns in both hyperactive and hypoactive brain networks.

Conclusions

These results help to link treatment to a comprehensive model of the neurocircuitry underlying depression and pave the way for future studies using network-guided principles to significantly improve rTMS efficacy for depression.

Intracortical and intercortical networks in patients after stroke: a concurrent TMS-EEG study

BACKGROUND

Concurrent transcranial magnetic stimulation and electroencephalography (TMS-EEG) recording provides information on both intracortical reorganization and networking, and that information could yield new insights into post-stroke neuroplasticity. However, a comprehensive investigation using both concurrent TMS-EEG and motor-evoked potential-based outcomes has not been carried out in patients with chronic stroke. Therefore, this study sought to investigate the intracortical and network neurophysiological features of patients with chronic stroke, using concurrent TMS-EEG and motor-evoked potential-based outcomes.

METHODS

A battery of motor-evoked potential-based measures and concurrent TMS-EEG recording were performed in 23 patients with chronic stroke and 21 age-matched healthy controls.

RESULTS

The ipsilesional primary motor cortex (M1) of the patients with stroke showed significantly higher resting motor threshold (P = 0.002), reduced active motor-evoked potential amplitudes (P = 0.001) and a prolonged cortical silent period (P = 0.007), compared with their contralesional M1. The ipsilesional stimulation also produced a reduction in N100 amplitude of TMS-evoked potentials around the stimulated M1 (P = 0.007), which was significantly correlated with the ipsilesional resting motor threshold (P = 0.011) and motor-evoked potential amplitudes (P = 0.020). In addition, TMS-related oscillatory power was significantly reduced over the ipsilesional midline-prefrontal and parietal regions. Both intra/interhemispheric connectivity and network measures in the theta band were significantly reduced in the ipsilesional hemisphere compared with those in the contralesional hemisphere.

CONCLUSIONS

The ipsilesional M1 demonstrated impaired GABA-B receptor-mediated intracortical inhibition characterized by reduced duration, but reduced magnitude. The N100 of TMS-evoked potentials appears to be a useful biomarker of post-stroke recovery.

Network-specific metabolic and haemodynamic effects elicited by non-invasive brain stimulation

Repetitive transcranial magnetic stimulation (TMS), when applied to the dorsolateral prefrontal cortex (dlPFC), treats depression. Therapeutic effects are hypothesized to arise from propagation of local dlPFC stimulation effects across distributed networks; however, the mechanisms of this remain unresolved. dlPFC contains representations of different networks. As such, dlPFC TMS may exert different effects depending on the network being stimulated. Here, to test this, we applied high-frequency TMS to two nearby dlPFC targets functionally embedded in distinct anti-correlated networks-the default and salience networks- in the same individuals in separate sessions. Local and distributed TMS effects were measured with combined 18fluorodeoxyglucose positron emission tomography and functional magnetic resonance imaging. Identical TMS patterns caused opposing effects on local glucose metabolism: metabolism increased at the salience target following salience TMS but decreased at the default target following default TMS. At the distributed level, both conditions increased functional connectivity between the default and salience networks, with this effect being dramatically larger following default TMS. Metabolic and haemodynamic effects were also linked: across subjects, the magnitude of local metabolic changes correlated with the degree of functional connectivity changes. These results suggest that TMS effects upon dlPFC are network specific. They also invoke putative antidepressant mechanisms of TMS: network de-coupling.

TMS-evoked responses are driven by recurrent large-scale network dynamics

A compelling way to disentangle the complexity of the brain is to measure the effects of spatially and temporally synchronized systematic perturbations. In humans, this can be non-invasively achieved by combining transcranial magnetic stimulation (TMS) and electroencephalography (EEG). Spatiotemporally complex and long-lasting TMS-EEG evoked potential (TEP) waveforms are believed to result from recurrent, re-entrant activity that propagates broadly across multiple cortical and subcortical regions, dispersing from and later re-converging on, the primary stimulation site. However, if we loosely understand the TEP of a TMS-stimulated region as the impulse response function of a noisy underdamped harmonic oscillator, then multiple later activity components (waveform peaks) should be expected even for an isolated network node in the complete absence of recurrent inputs. Thus emerges a critically important question for basic and clinical research on human brain dynamics: what parts of the TEP are due to purely local dynamics, what parts are due to reverberant, re-entrant network activity, and how can we distinguish between the two? To disentangle this, we used source-localized TMS-EEG analyses and whole-brain connectome-based computational modelling. Results indicated that recurrent network feedback begins to drive TEP responses from 100 ms post-stimulation, with earlier TEP components being attributable to local reverberatory activity within the stimulated region. Subject-specific estimation of neurophysiological parameters additionally indicated an important role for inhibitory GABAergic neural populations in scaling cortical excitability levels, as reflected in TEP waveform characteristics. The novel discoveries and new software technologies introduced here should be of broad utility in basic and clinical neuroscience research.

M1-P15 as a cortical marker for transcallosal inhibition: A preregistered TMS-EEG study

In a recently published study combining transcranial magnetic stimulation and electroencephalography (TMS-EEG), an early component of TMS-evoked potentials (TEPs), i.e., M1-P15, was proposed as a measure of transcallosal inhibition between motor cortices. Given that early TEPs are known to be highly variable, further evidence is needed before M1-P15 can be considered a reliable index of effective connectivity. Here, we conceived a new preregistered TMS-EEG study with two aims. The first aim was validating the M1-P15 as a cortical index of transcallosal inhibition by replicating previous findings on its relationship with the ipsilateral silent period (iSP) and with performance in bimanual coordination. The second aim was inducing a task-dependent modulation of transcallosal inhibition. A new sample of 32 healthy right-handed participants underwent behavioral motor tasks and TMS-EEG recording, in which left and right M1 were stimulated both during bimanual tasks and during an iSP paradigm. Hypotheses and methods were preregistered before data collection. Results show a replication of our previous findings on the positive relationship between M1-P15 amplitude and the iSP normalized area. Differently, the relationship between M1-P15 latency and bimanual coordination was not confirmed. Finally, M1-P15 amplitude was modulated by the characteristics of the bimanual task the participants were performing, and not by the contralateral hand activity during the iSP paradigm. In sum, the present results corroborate our previous findings in validating the M1-P15 as a cortical marker of transcallosal inhibition and provide novel evidence of its task-dependent modulation. Importantly, we demonstrate the feasibility of preregistration in the TMS-EEG field to increase methodological rigor and transparency.

Personalizing Repetitive Transcranial Magnetic Stimulation Parameters for Depression Treatment Using Multimodal Neuroimaging

Repetitive transcranial magnetic stimulation (rTMS) is a tool that can be used to administer treatment for neuropsychiatric disorders such as major depressive disorder, although the clinical efficacy is still rather modest. Overly general stimulation protocols that consider neither patient-specific depression symptomology nor individualized brain characteristics, such as anatomy or structural and functional connections, may be the cause of the high inter- and intraindividual variability in rTMS clinical responses. Multimodal neuroimaging can provide the necessary insights into individual brain characteristics and can therefore be used to personalize rTMS parameters. Optimal coil positioning should include a three-step process: 1) identify the optimal (indirect) target area based on the exact symptom pattern of the patient; 2) derive the cortical (direct) target location based on functional and/or structural connectomes derived from functional and diffusion magnetic resonance imaging data; and 3) determine the ideal coil position by computational modeling, such that the electric field distribution overlaps with the cortical target. These TMS-induced electric field simulations, derived from anatomical and diffusion magnetic resonance imaging data, can be further applied to compute optimal stimulation intensities. In addition to magnetic resonance imaging, electroencephalography can provide complementary information regarding the ongoing brain oscillations. This information can be used to determine the optimal timing and frequency of the stimuli. The heightened benefits of these personalized stimulation approaches are logically reasoned, but speculative. Randomized clinical trials will be required to compare clinical responses from standard rTMS protocols to personalized protocols. Ultimately, an optimized clinical response may result from precision protocols derived from combinations of personalized stimulation parameters.