Brain-Derived Neurotrophic Factor Polymorphism Influences Response to Single-Pulse Transcranial Magnetic Stimulation at Rest

Maximizing brain networks engagement via individualized connectome-wide target search

BACKGROUND

In recent years, the possibility to noninvasively interact with the human brain has led to unprecedented diagnostic and therapeutic opportunities. However, the vast majority of approved interventions and approaches still rely on anatomical landmarks and rarely on the individual structure of networks in the brain, drastically reducing the potential efficacy of neuromodulation.

OBJECTIVE

Here we implemented a target search algorithm leveraging on mathematical tools from Network Control Theory (NCT) and whole brain connectomics analysis. By means of computational simulations, we aimed to identify the optimal stimulation target(s)- at the individual brain level- capable of reaching maximal engagement of the stimulated networks' nodes.

RESULTS

At the model level, in silico predictions suggest that stimulation of NCT-derived cerebral sites might induce significantly higher network engagement, compared to traditionally employed neuromodulation sites, demonstrating NCT to be a useful tool in guiding brain stimulation. Indeed, NCT allows us to computationally model different stimulation scenarios tailored on the individual structural connectivity profiles and initial brain states.

CONCLUSIONS

The use of NCT to computationally predict TMS pulse propagation suggests that individualized targeting is crucial for more successful network engagement. Future studies will be needed to verify such prediction in real stimulation scenarios.

Genetic and Neurophysiological Biomarkers of Neuroplasticity Inform Post-Stroke Language Recovery

BACKGROUND

There is high variability in post-stroke aphasia severity and predicting recovery remains imprecise. Standard prognostics do not include neurophysiological indicators or genetic biomarkers of neuroplasticity, which may be critical sources of variability.

OBJECTIVE

To evaluate whether a common polymorphism (Val⁶⁶Met) in the gene for brain-derived neurotrophic factor (BDNF) contributes to variability in post-stroke aphasia, and to assess whether BDNF polymorphism interacts with neurophysiological indicators of neuroplasticity (cortical excitability and stimulation-induced neuroplasticity) to improve estimates of aphasia severity.

METHODS

Saliva samples and motor-evoked potentials (MEPs) were collected from participants with chronic aphasia subsequent to left-hemisphere stroke. MEPs were collected prior to continuous theta burst stimulation (cTBS; index for cortical excitability) and 10 minutes following cTBS (index for stimulation-induced neuroplasticity) to the right primary motor cortex. Analyses assessed the extent to which BDNF polymorphism interacted with cortical excitability and stimulation-induced neuroplasticity to predict aphasia severity beyond established predictors.

RESULTS

Val⁶⁶Val carriers showed less aphasia severity than Val⁶⁶Met carriers, after controlling for lesion volume and time post-stroke. Furthermore, Val⁶⁶Val carriers showed expected effects of age on aphasia severity, and positive associations between severity and both cortical excitability and stimulation-induced neuroplasticity. In contrast, Val⁶⁶Met carriers showed weaker effects of age and negative associations between cortical excitability, stimulation-induced neuroplasticity and aphasia severity.

CONCLUSIONS

Neurophysiological indicators and genetic biomarkers of neuroplasticity improved aphasia severity predictions. Furthermore, BDNF polymorphism interacted with cortical excitability and stimulation-induced neuroplasticity to improve predictions. These findings provide novel insights into mechanisms of variability in stroke recovery and may improve aphasia prognostics.

Noninvasive brain stimulation to augment language therapy for poststroke aphasia

Behavioral language treatment approaches represent the standard of care for persons with aphasia (PWA), but the benefits of these treatments are variable. Moreover, due to the logistic and financial limitations on the amount of behavioral therapy available to patients, it is often infeasible for PWA to receive behavioral interventions with the level of frequency, intensity, or duration that would provide significant and lasting benefit, underscoring the need for novel, effective treatment approaches. Noninvasive brain stimulation (NIBS) techniques, such as transcranial magnetic stimulation (TMS) and transcranial direct current stimulation (tDCS), have emerged as promising neurally-based tools to enhance language abilities for PWA following stroke. This chapter first provides an overview of the methods and physiologic basis motivating the use of NIBS to enhance aphasia recovery followed by a selective review of the growing evidence of its potential as a novel therapeutic tool. Subsequent sections discuss some of the principles that may prove most useful in guiding and optimizing the effects of NIBS on aphasia recovery, focusing on how the functional state of the brain at the time of stimulation interacts with the behavioral aftereffects of neuromodulation. We conclude with a discussion of current challenges and future directions for NIBS in aphasia treatment.

BDNF Val66Met gene polymorphism modulates brain activity following rTMS-induced memory impairment

The BDNF Val66Met gene polymorphism is a relevant factor explaining inter-individual differences to TMS responses in studies of the motor system. However, whether this variant also contributes to TMS-induced memory effects, as well as their underlying brain mechanisms, remains unexplored. In this investigation, we applied rTMS during encoding of a visual memory task either over the left frontal cortex (LFC; experimental condition) or the cranial vertex (control condition). Subsequently, individuals underwent a recognition memory phase during a functional MRI acquisition. We included 43 young volunteers and classified them as 19 Met allele carriers and 24 as Val/Val individuals. The results revealed that rTMS delivered over LFC compared to vertex stimulation resulted in reduced memory performance only amongst Val/Val allele carriers. This genetic group also exhibited greater fMRI brain activity during memory recognition, mainly over frontal regions, which was positively associated with cognitive performance. We concluded that BDNF Val66Met gene polymorphism, known to exert a significant effect on neuroplasticity, modulates the impact of rTMS both at the cognitive as well as at the associated brain networks expression levels. This data provides new insights on the brain mechanisms explaining cognitive inter-individual differences to TMS, and may inform future, more individually-tailored rTMS interventions.

The Problem and Potential of TMS' Infinite Parameter Space: A Targeted Review and Road Map Forward

BACKGROUND

Repetitive transcranial magnetic stimulation (rTMS) is a non-invasive, effective, and FDA-approved brain stimulation method. However, rTMS parameter selection remains largely unexplored, with great potential for optimization. In this review, we highlight key studies underlying next generation rTMS therapies, particularly focusing on: (1) rTMS Parameters, (2) rTMS Target Engagement, (3) rTMS Interactions with Endogenous Brain Activity, and (4) Heritable Predisposition to Brain Stimulation Treatments.

METHODS

We performed a targeted review of pre-clinical and clinical rTMS studies.

RESULTS

Current evidence suggests that rTMS pattern, intensity, frequency, train duration, intertrain interval, intersession interval, pulse and session number, pulse width, and pulse shape can alter motor excitability, long term potentiation (LTP)-like facilitation, and clinical antidepressant response. Additionally, an emerging theme is how endogenous brain state impacts rTMS response. Researchers have used resting state functional magnetic resonance imaging (rsfMRI) analyses to identify personalized rTMS targets. Electroencephalography (EEG) may measure endogenous alpha rhythms that preferentially respond to personalized stimulation frequencies, or in closed-loop EEG, may be synchronized with endogenous oscillations and even phase to optimize response. Lastly, neuroimaging and genotyping have identified individual predispositions that may underlie rTMS efficacy.

CONCLUSIONS

We envision next generation rTMS will be delivered using optimized stimulation parameters to rsfMRI-determined targets at intensities determined by energy delivered to the cortex, and frequency personalized and synchronized to endogenous alpha-rhythms. Further research is needed to define the dose-response curve of each parameter on plasticity and clinical response at the group level, to determine how these parameters interact, and to ultimately personalize these parameters.