The future of personalized brain stimulation

Linking connectivity of deep brain stimulation of nucleus accumbens area with clinical depression improvements: a retrospective longitudinal case series

Treatment-resistant depression is a severe form of major depressive disorder and deep brain stimulation is currently an investigational treatment. The stimulation's therapeutic effect may be explained through the functional and structural connectivities between the stimulated area and other brain regions, or to depression-associated networks. In this longitudinal, retrospective study, four female patients with treatment-resistant depression were implanted for stimulation in the nucleus accumbens area at our center. We analyzed the structural and functional connectivity of the stimulation area: the structural connectivity was investigated with probabilistic tractography; the functional connectivity was estimated by combining patient-specific stimulation volumes and a normative functional connectome. These structural and functional connectivity profiles were then related to four clinical outcome scores. At 1-year follow-up, the remission rate was 66%. We observed a consistent structural connectivity to Brodmann area 25 in the patient with the longest remission phase. The functional connectivity analysis resulted in patient-specific R-maps describing brain areas significantly correlated with symptom improvement in this patient, notably the prefrontal cortex. But the connectivity analysis was mixed across patients, calling for confirmation in a larger cohort and over longer time periods.

The promise and challenges of transcranial magnetic stimulation and deep brain stimulation as therapeutic options for obsessive-compulsive disorder

INTRODUCTION

Obsessive compulsive disorder (OCD) represents a complex and often difficult to treat disorder. Pharmacological and psychotherapeutic interventions are often associated with sub-optimal outcomes, and 40-60% of patients are resistant to first line therapies and thus left with few treatment options. OCD is underpinned by aberrant neurocircuitry within cortical, striatal, and thalamic brain networks. Considering the neurocircuitry impairments that underlie OCD symptomology, neurostimulation therapies provide an opportunity to modulate psychopathology in a personalized manner. Also, by probing pathological neural networks, enhanced understanding of disease states can be obtained.

AREAS COVERED

This perspective discusses the clinical efficacy of TMS and DBS therapies, treatment access options, and considerations and challenges in managing patients. Recent scientific progress is discussed, with a focus on neurocircuitry and biopsychosocial aspects. Translational recommendations and suggestions for future research are provided.

EXPERT OPINION

There is robust evidence to support TMS and DBS as an efficacious therapy for treatment resistant OCD patients supported by an excellent safety profile and favorable health economic data. Despite a great need for alternative therapies for chronic and severe OCD patients, resistance toward neurostimulation therapies from regulatory bodies and the psychiatric community remains. The authors contend for greater access to TMS and DBS for treatment resistant OCD patients at specialized sites with appropriate clinical resources, particularly considering adjunct and follow-up care. Also, connectome targeting has shown robust predictive ability of symptom improvements and holds potential in advancing personalized neurostimulation therapies.

Increased entrainment and decreased excitability predict efficacious treatment of closed-loop phase-locked rTMS for treatment-resistant depression

Transcranial magnetic stimulation (TMS) is an FDA-approved therapy for major depressive disorder (MDD), specifically for patients who have treatment-resistant depression (TRD). However, TMS produces response or remission in about 50% of patients but is ineffective for the other 50%. Limits on efficacy may be due to individual patient variability, but to date, there are no good biomarkers or measures of target engagement. In addition, TMS efficacy is typically not assessed until a six-week treatment ends, precluding the evaluation of intermediate improvements during the treatment duration. Here, we report on results using a closed-loop phase-locked repetitive TMS (rTMS) treatment that synchronizes the delivery of rTMS based on the timing of the pulses relative to a patient's individual electroencephalographic (EEG) prefrontal alpha oscillation informed by functional magnetic resonance imaging (fMRI). We find that, in responders, synchronized delivery of rTMS produces two systematic changes in brain dynamics. The first change is a decrease in global cortical excitability, and the second is an increase in the phase entrainment of cortical dynamics. These two effects predict clinical outcomes in the synchronized treatment group but not in an active-treatment unsynchronized control group. The systematic decrease in excitability and increase in entrainment correlated with treatment efficacy at the endpoint and intermediate weeks during the synchronized treatment. Specifically, we show that weekly tracking of these biomarkers allows for efficacy prediction and potential of dynamic adjustments through a treatment course, improving the overall response rates.

Invasive neurophysiology and whole brain connectomics for neural decoding in patients with brain implants

Brain computer interfaces (BCI) provide unprecedented spatiotemporal precision that will enable significant expansion in how numerous brain disorders are treated. Decoding dynamic patient states from brain signals with machine learning is required to leverage this precision, but a standardized framework for identifying and advancing novel clinical BCI approaches does not exist. Here, we developed a platform that integrates brain signal decoding with connectomics and demonstrate its utility across 123 hours of invasively recorded brain data from 73 neurosurgical patients treated for movement disorders, depression and epilepsy. First, we introduce connectomics-informed movement decoders that generalize across cohorts with Parkinson's disease and epilepsy from the US, Europe and China. Next, we reveal network targets for emotion decoding in left prefrontal and cingulate circuits in DBS patients with major depression. Finally, we showcase opportunities to improve seizure detection in responsive neurostimulation for epilepsy. Our platform provides rapid, high-accuracy decoding for precision medicine approaches that can dynamically adapt neuromodulation therapies in response to the individual needs of patients.

Local neuroanatomical and tract-based proxies of optimal subcallosal cingulate deep brain stimulation

BACKGROUND

Deep brain stimulation of the subcallosal cingulate area (SCC-DBS) is a promising neuromodulatory therapy for treatment-resistant depression (TRD). Biomarkers of optimal target engagement are needed to guide surgical targeting and stimulation parameter selection and to reduce variance in clinical outcome.

OBJECTIVE/HYPOTHESIS

We aimed to characterize the relationship between stimulation location, white matter tract engagement, and clinical outcome in a large (n = 60) TRD cohort treated with SCC-DBS. A smaller cohort (n = 22) of SCC-DBS patients with differing primary indications (bipolar disorder/anorexia nervosa) was utilized as an out-of-sample validation cohort.

METHODS

Volumes of tissue activated (VTAs) were constructed in standard space using high-resolution structural MRI and individual stimulation parameters. VTA-based probabilistic stimulation maps (PSMs) were generated to elucidate voxelwise spatial patterns of efficacious stimulation. A whole-brain tractogram derived from Human Connectome Project diffusion-weighted MRI data was seeded with VTA pairs, and white matter streamlines whose overlap with VTAs related to outcome ('discriminative' streamlines; Puncorrected < 0.05) were identified using t-tests. Linear modelling was used to interrogate the potential clinical relevance of VTA overlap with specific structures.

RESULTS

PSMs varied by hemisphere: high-value left-sided voxels were located more anterosuperiorly and squarely in the lateral white matter, while the equivalent right-sided voxels fell more posteroinferiorly and involved a greater proportion of grey matter. Positive discriminative streamlines localized to the bilateral (but primarily left) cingulum bundle, forceps minor/rostrum of corpus callosum, and bilateral uncinate fasciculus. Conversely, negative discriminative streamlines mostly belonged to the right cingulum bundle and bilateral uncinate fasciculus. The best performing linear model, which utilized information about VTA volume overlap with each of the positive discriminative streamline bundles as well as the negative discriminative elements of the right cingulum bundle, explained significant variance in clinical improvement in the primary TRD cohort (R = 0.46, P < 0.001) and survived repeated 10-fold cross-validation (R = 0.50, P = 0.040). This model was also able to predict outcome in the out-of-sample validation cohort (R = 0.43, P = 0.047).

CONCLUSION(S)

These findings reinforce prior indications of the importance of white matter engagement to SCC-DBS treatment success while providing new insights that could inform surgical targeting and stimulation parameter selection decisions.

Closing the loop between brain and electrical stimulation: towards precision neuromodulation treatments

One of the most critical challenges in using noninvasive brain stimulation (NIBS) techniques for the treatment of psychiatric and neurologic disorders is inter- and intra-individual variability in response to NIBS. Response variations in previous findings suggest that the one-size-fits-all approach does not seem the most appropriate option for enhancing stimulation outcomes. While there is a growing body of evidence for the feasibility and effectiveness of individualized NIBS approaches, the optimal way to achieve this is yet to be determined. Transcranial electrical stimulation (tES) is one of the NIBS techniques showing promising results in modulating treatment outcomes in several psychiatric and neurologic disorders, but it faces the same challenge for individual optimization. With new computational and methodological advances, tES can be integrated with real-time functional magnetic resonance imaging (rtfMRI) to establish closed-loop tES-fMRI for individually optimized neuromodulation. Closed-loop tES-fMRI systems aim to optimize stimulation parameters based on minimizing differences between the model of the current brain state and the desired value to maximize the expected clinical outcome. The methodological space to optimize closed-loop tES fMRI for clinical applications includes (1) stimulation vs. data acquisition timing, (2) fMRI context (task-based or resting-state), (3) inherent brain oscillations, (4) dose-response function, (5) brain target trait and state and (6) optimization algorithm. Closed-loop tES-fMRI technology has several advantages over non-individualized or open-loop systems to reshape the future of neuromodulation with objective optimization in a clinically relevant context such as drug cue reactivity for substance use disorder considering both inter and intra-individual variations. Using multi-level brain and behavior measures as input and desired outcomes to individualize stimulation parameters provides a framework for designing personalized tES protocols in precision psychiatry.

Closed-loop optimal and automatic tuning of pulse amplitude and width in EMG-guided controllable transcranial magnetic stimulation

This paper proposes an efficient algorithm for automatic and optimal tuning of pulse amplitude and width for sequential parameter estimation (SPE) of the neural membrane time constant and input-output (IO) curve parameters in closed-loop electromyography-guided (EMG-guided) controllable transcranial magnetic stimulation (cTMS). The proposed SPE is performed by administering a train of optimally tuned TMS pulses and updating the estimations until a stopping rule is satisfied or the maximum number of pulses is reached. The pulse amplitude is computed by the Fisher information maximization. The pulse width is chosen by maximizing a normalized depolarization factor, which is defined to separate the optimization and tuning of the pulse amplitude and width. The normalized depolarization factor maximization identifies the critical pulse width, which is an important parameter in the identifiability analysis, without any prior neurophysiological or anatomical knowledge of the neural membrane. The effectiveness of the proposed algorithm is evaluated through simulation. The results confirm satisfactory estimation of the membrane time constant and IO curve parameters for the simulation case. By defining the stopping rule based on the satisfaction of the convergence criterion with tolerance of 0.01 for 5 consecutive times for all parameters, the IO curve parameters are estimated with 52 TMS pulses, with absolute relative estimation errors (AREs) of less than 7%. The membrane time constant is estimated with 0.67% ARE, and the pulse width value tends to the critical pulse width with 0.16% ARE with 52 TMS pulses. The results confirm that the pulse width and amplitude can be tuned optimally and automatically to estimate the membrane time constant and IO curve parameters in real-time with closed-loop EMG-guided cTMS.

Personalized Definition of Surgical Targets in Major Depression and Obsessive-Compulsive Disorder: A Potential Role for Low-Intensity Focused Ultrasound?

Major Depressive Disorder (MDD) and Obsessive-Compulsive Disorder (OCD) are common and potentially incapacitating conditions. Even when recognized and adequately treated, in over a third of patients with these conditions the response to first-line pharmacological and psychotherapeutic measures is not satisfactory. After more assertive measures including pharmacological augmentation (and in the case of depression, transcranial magnetic stimulation, electroconvulsive therapy, or treatment with ketamine or esketamine), a significant number of individuals remain severely symptomatic. In these persons, different ablation and deep-brain stimulation (DBS) psychosurgical techniques have been employed. However, apart from the cost and potential morbidity associated with surgery, on average only about half of patients show adequate response, which limits the widespread application of these potentially life-saving interventions. Possible reasons are considered for the wide variation in outcomes across different series of patients with MDD or OCD exposed to ablative or DBS psychosurgery, including interindividual anatomical and etiological variability. Low-intensity focused ultrasound (LIFU) is an emerging technique that holds promise in its ability to achieve anatomically circumscribed, noninvasive, and reversible neuromodulation of deep brain structures. A possible role for LIFU in the personalized presurgical definition of neuromodulation targets in the individual patient is discussed, including a proposed roadmap for clinical trials addressed at testing whether this technique can help to improve psychosurgical outcomes.

Synapses, predictions, and prediction errors: A neocortical computational study of MDD using the temporal memory algorithm of HTM

INTRODUCTION

Synapses and spines play a significant role in major depressive disorder (MDD) pathophysiology, recently highlighted by the rapid antidepressant effect of ketamine and psilocybin. According to the Bayesian brain and interoception perspectives, MDD is formalized as being stuck in affective states constantly predicting negative energy balance. To understand how spines and synapses relate to the predictive function of the neocortex and thus to symptoms, we used the temporal memory (TM), an unsupervised machine-learning algorithm. TM models a single neocortical layer, learns in real-time, and extracts and predicts temporal sequences. TM exhibits neocortical biological features such as sparse firing and continuous online learning using local Hebbian-learning rules.

METHODS

We trained a TM model on random sequences of upper-case alphabetical letters, representing sequences of affective states. To model depression, we progressively destroyed synapses in the TM model and examined how that affected the predictive capacity of the network. We found that the number of predictions decreased non-linearly.

RESULTS

Destroying 50% of the synapses slightly reduced the number of predictions, followed by a marked drop with further destruction. However, reducing the synapses by 25% distinctly dropped the confidence in the predictions. Therefore, even though the network was making accurate predictions, the network was no longer confident about these predictions.

DISCUSSION

These findings explain how interoceptive cortices could be stuck in limited affective states with high prediction error. Connecting ketamine and psilocybin's proposed mechanism of action to depression pathophysiology, the growth of new synapses would allow representing more futuristic predictions with higher confidence. To our knowledge, this is the first study to use the TM model to connect changes happening at synaptic levels to the Bayesian formulation of psychiatric symptomatology. Linking neurobiological abnormalities to symptoms will allow us to understand the mechanisms of treatments and possibly, develop new ones.

Predictive neuromodulation of cingulo-frontal neural dynamics in major depressive disorder using a brain-computer interface system: A simulation study

Introduction

Deep brain stimulation (DBS) is a promising therapy for treatment-resistant major depressive disorder (MDD). MDD involves the dysfunction of a brain network that can exhibit complex nonlinear neural dynamics in multiple frequency bands. However, current open-loop and responsive DBS methods cannot track the complex multiband neural dynamics in MDD, leading to imprecise regulation of symptoms, variable treatment effects among patients, and high battery power consumption.

Methods

Here, we develop a closed-loop brain-computer interface (BCI) system of predictive neuromodulation for treating MDD. We first use a biophysically plausible ventral anterior cingulate cortex (vACC)-dorsolateral prefrontal cortex (dlPFC) neural mass model of MDD to simulate nonlinear and multiband neural dynamics in response to DBS. We then use offline system identification to build a dynamic model that predicts the DBS effect on neural activity. We next use the offline identified model to design an online BCI system of predictive neuromodulation. The online BCI system consists of a dynamic brain state estimator and a model predictive controller. The brain state estimator estimates the MDD brain state from the history of neural activity and previously delivered DBS patterns. The predictive controller takes the estimated MDD brain state as the feedback signal and optimally adjusts DBS to regulate the MDD neural dynamics to therapeutic targets. We use the vACC-dlPFC neural mass model as a simulation testbed to test the BCI system and compare it with state-of-the-art open-loop and responsive DBS treatments of MDD.

Results

We demonstrate that our dynamic model accurately predicts nonlinear and multiband neural activity. Consequently, the predictive neuromodulation system accurately regulates the neural dynamics in MDD, resulting in significantly smaller control errors and lower DBS battery power consumption than open-loop and responsive DBS.

Discussion

Our results have implications for developing future precisely-tailored clinical closed-loop DBS treatments for MDD.

10 Minutes Frontal 40 Hz tACS-Effects on Working Memory Tested by Luck-Vogel Task

Working memory is a cognitive process that involves short-term active maintenance, flexible updating, and processing of goal- or task-relevant information. All frequency bands are involved in working memory. The activities of the theta and gamma frequency bands in the frontoparietal network are highly involved in working memory processes; theta oscillations play a role in the temporal organization of working memory items, and gamma oscillations influence the maintenance of information in working memory. Transcranial alternating current stimulation (tACS) results in frequency-specific modulation of endogenous oscillations and has shown promising results in cognitive neuroscience. The electrophysiological and behavioral changes induced by the modulation of endogenous gamma frequency in the prefrontal cortex using tACS have not been extensively studied in the context of working memory. Therefore, we aimed to investigate the effects of frontal gamma-tACS on working memory outcomes. We hypothesized that a 10-min gamma tACS administered over the frontal cortex would significantly improve working memory outcomes. Young healthy participants performed Luck-Vogel cognitive behavioral tasks with simultaneous pre- and post-intervention EEG recording (Sham versus 40 Hz tACS). Data from forty-one participants: sham (15 participants) and tACS (26 participants), were used for the statistical and behavioral analysis. The relative changes in behavioral outcomes and EEG due to the intervention were analyzed. The results show that tACS caused an increase in the power spectral density in the high beta and low gamma EEG bands and a decrease in left-right coherence. On the other hand, tACS had no significant effect on success rates and response times. Conclusion: 10 min of frontal 40 Hz tACS was not sufficient to produce detectable behavioral effects on working memory, whereas electrophysiological changes were evident. The limitations of the current stimulation protocol and future directions are discussed in detail in the following sections.

Systems consolidation and fear memory generalisation as a potential target for trauma-related disorders

Fear memory generalisation is a central hallmark in the broad range of anxiety and trauma-related disorders. Recent findings suggest that fear generalisation is closely related to hippocampal dependency during retrieval. In this review, we describe the current understanding about memory generalisation and its potential influence in fear attenuation through pharmacological and behavioural interventions. In light of systems consolidation framework, we propose that keeping memory precision could be a key step to enhance therapeutic outcomes.

A formalism for sequential estimation of neural membrane time constant and input--output curve towards selective and closed-loop transcranial magnetic stimulation

OBJECTIVE

To obtain a formalism for real-time concurrent sequential estimation of neural membrane time constant and input--output (IO) curve with transcranial magnetic stimulation (TMS).

APPROACH

First, the neural membrane response and depolarization factor, which leads to motor evoked potentials (MEPs) with TMS are analytically computed and discussed. Then, an integrated model is developed which combines the neural membrane time constant and input--output curve. Identifiability of the proposed integrated model is discussed. A condition is derived, which assures estimation of the proposed integrated model. Finally, sequential parameter estimation (SPE) of the neural membrane time constant and IO curve is described through closed-loop optimal sampling and open-loop uniform sampling TMS. Without loss of generality, this paper focuses on a specific case of commercialized TMS pulse shapes. The proposed formalism and SPE method are directly applicable to other pulse shapes.

MAIN RESULTS

The results confirm satisfactory estimation of the membrane time constant and IO curve parameters. By defining a stopping rule based on five times consecutive convergence of the estimation parameters with a tolerances of 0.01, the membrane time constant and IO curve parameters are estimated with 82 TMS pulses with absolute relative estimation errors (AREs) of less than 4% with the optimal sampling SPE method. At this point, the uniform sampling SPE method leads to AREs up to 16%. The uniform sampling method does not satisfy the stopping rule due to the large estimation variations.

SIGNIFICANCE

This paper provides a tool for real-time closed-loop SPE of the neural time constant and IO curve, which can contribute novel insights in TMS studies. SPE of the membrane time constant enables selective stimulation, which can be used for advanced brain research, precision medicine and personalized medicine.

Determining the Individual Theta Frequency for Associative Memory Targeted Personalized Transcranial Brain Stimulation

Non-invasive brain stimulation (NIBS) methods have gained increased interest in research and therapy of associative memory (AM) and its impairments. However, the one-size-fits-all approach yields inconsistent findings, thus putting forward the need for electroencephalography (EEG)-guided personalized frequency-modulated NIBS protocols to increase the focality and the effectiveness of the interventions. Still, extraction of individual frequency, especially in the theta band, turned out to be a challenging task. Here we present an approach to extracting the individual theta-band frequency (ITF) from EEG signals recorded during the AM task. The method showed a 93% success rate, good reliability, and the full range of variability of the extracted ITFs. This paper provides a rationale behind the adopted approach and critically evaluates it in comparison to the alternative methods that have been reported in the literature. Finally, we discuss how it could be used as an input parameter for personalized frequency-modulated NIBS approaches—transcranial alternating current stimulation (tACS) and transcranial oscillatory current stimulation (otDCS) directed at AM neuromodulation.

Spatiotemporal Dynamics of Stress-Induced Network Reconfigurations Reflect Negative Affectivity

BACKGROUND

Maladaptive stress responses are important risk factors in the etiology of mood and anxiety disorders, but exact pathomechanisms remain to be understood. Mapping individual differences of acute stress-induced neurophysiological changes, especially on the level of neural activation and functional connectivity (FC), could provide important insights in how variation in the individual stress response is linked to disease risk.

METHODS

Using an established psychosocial stress task flanked by two resting states, we measured subjective, physiological, and brain responses to acute stress and recovery in 217 participants with and without mood and anxiety disorders. To estimate blockwise changes in stress-induced activation and FC, we used hierarchical mixed-effects models based on denoised time series within predefined stress-related regions. We predicted inter- and intraindividual differences in stress phases (anticipation vs. stress vs. recovery) and transdiagnostic dimensions of stress reactivity using elastic net and support vector machines.

RESULTS

We identified four subnetworks showing distinct changes in FC over time. FC but not activation trajectories predicted the stress phase (accuracy = 70%, p_perm < .001) and increases in heart rate (R² = 0.075, p_perm < .001). Critically, individual spatiotemporal trajectories of changes across networks also predicted negative affectivity (ΔR² = 0.075, p_perm = .030) but not the presence or absence of a mood and anxiety disorder.

CONCLUSIONS

Spatiotemporal dynamics of brain network reconfiguration induced by stress reflect individual differences in the psychopathology dimension of negative affectivity. These results support the idea that vulnerability for mood and anxiety disorders can be conceptualized best at the level of network dynamics, which may pave the way for improved prediction of individual risk.

Personalizing repetitive transcranial magnetic stimulation for precision depression treatment based on functional brain network controllability and optimal control analysis

Brain neuromodulation effectively treats neurological diseases and psychiatric disorders such as Depression. However, due to patient heterogeneity, neuromodulation treatment outcomes are often highly variable, requiring patient-specific stimulation protocols throughout the recovery stages to optimize treatment outcomes. Therefore, it is critical to personalize neuromodulation protocol to optimize the patient-specific stimulation targets and parameters by accommodating inherent interpatient variability and intersession alteration during treatments. The study aims to develop a personalized repetitive transcranial magnetic stimulation (rTMS) protocol and evaluate its feasibility in optimizing the treatment efficiency using an existing dataset from an antidepressant experimental imaging study in depression. The personalization of the rTMS treatment protocol was achieved by personalizing both stimulation targets and parameters via a novel approach integrating the functional brain network controllability analysis and optimal control analysis. First, the functional brain network controllability analysis was performed to identify the optimal rTMS stimulation target from the effective connectivity network constructed from patient-specific resting-state functional magnetic resonance imaging data. The optimal control algorithm was then applied to optimize the rTMS stimulation parameters based on the optimized target. The performance of the proposed personalized rTMS technique was evaluated using datasets collected from a longitudinal antidepressant experimental imaging study in depression (n = 20). Simulation models demonstrated that the proposed personalized rTMS protocol outperformed the standard rTMS treatment by efficiently steering a depressive resting brain state to a healthy resting brain state, indicated by the significantly less control energy needed and higher model fitting accuracy achieved. The node with the maximum average controllability of each patient was designated as the optimal target region for the personalized rTMS protocol. Our results also demonstrated the theoretical feasibility of achieving comparable neuromodulation efficacy by stimulating a single node compared to stimulating multiple driver nodes. The findings support the feasibility of developing personalized neuromodulation protocols to more efficiently treat depression and other neurological diseases.

Single-interface bioelectronic medicines - concept, clinical applications and preclinical data

Presently, large groups of patients with various diseases are either intolerant, or irresponsive to drug therapies and also intractable by surgery. For several diseases, one option which is available for such patients is the implantable neurostimulation therapy. However, lacking closed-loop control and selective stimulation capabilities, the present neurostimulation therapies are not optimal and are therefore used as only "third" therapeutic options when a disease cannot be treated by drugs or surgery. Addressing those limitations, a next generation class of closed-loop controlled and selective neurostimulators generically named bioelectronic medicines seems within reach. A sub-class of such devices is meant to monitor and treat impaired functions by intercepting, analyzing and modulating neural signals involved in the regulation of such functions using just one neural interface for those purposes. The primary objective of this review is to provide a first broad perspective on this type of single-interface devices for bioelectronic therapies. For this purpose, the concept, clinical applications and preclinical studies for further developments with such devices are here analyzed in a narrative manner.

Deep Brain Stimulation of the Nucleus Accumbens in Severe Enduring Anorexia Nervosa: A Pilot Study

Introduction

Anorexia nervosa (AN) is one of the most debilitating psychiatric disorders, becoming severe and enduring in a third of cases; with few effective treatments. Deep brain stimulation is a reversible, adjustable neurosurgical procedure that has been gaining ground in psychiatry as a treatment for depression and obsessive-compulsive disorder, yet few studies have investigated AN. Abnormal eating behavior and the compulsive pursuit of thinness in AN is, in part, a consequence of dysfunction in reward circuitry and the nucleus accumbens (NAcc) is central to reward processing.

Methods

Phase 1 prospective open-label pilot study of seven individuals with severe enduring AN. Electrodes were implanted bilaterally into the NAcc with stimulation at the anterior limb of the internal capsule using rechargeable implantable pulse generators. The protocol of 15 months included 12 months of deep brain stimulation incorporating two consecutive, randomized blind on-off fortnights 9 months after stimulation onset. The primary objectives were to investigate safety and feasibility, together with changes in eating disorder psychopathology.

Results

Feasibility and safety was demonstrated with no serious adverse events due to deep brain stimulation. Three patients responded to treatment [defined as > 35% reduction in Eating Disorders Examination (EDE) score at 12 months] and four patients were non-responders. Responders had a statistically significant mean reduction in EDE scores (50.3% reduction; 95% CI 2.6-98.2%), Clinical Impairment Assessment (45.6% reduction; 95% CI 7.4-83.7%). Responders also had a statistically significant mean reduction in Hamilton Depression Scale, Hamilton Anxiety Scale and Snaith-Hamilton pleasure scale. There were no statistically significant changes in Body Mass Index, Yale-Brown-Cornell Eating Disorder Scale, Yale-Brown Obsessive-Compulsive Scale and World Health Organization Quality of Life Psychological subscale.

Conclusion

This study provides some preliminary indication that deep brain stimulation to the NAcc. Might potentially improve some key features of enduring AN. In this small study, the three responders had comorbid obsessive-compulsive disorder which predated AN diagnosis. Future studies should aim to further elucidate predictors of outcome.

Clinical Trial Registration

[www.ClinicalTrials.gov], identifier [Project ID 128658].

Personalized Frequency Modulated Transcranial Electrical Stimulation for Associative Memory Enhancement

Associative memory (AM) is the ability to remember the relationship between previously unrelated items. AM is significantly affected by normal aging and neurodegenerative conditions, thus there is a growing interest in applying non-invasive brain stimulation (NIBS) techniques for AM enhancement. A growing body of studies identifies posterior parietal cortex (PPC) as the most promising cortical target for both transcranial magnetic stimulation (TMS) and transcranial electrical stimulation (tES) to modulate a cortico-hippocampal network that underlines AM. In that sense, theta frequency oscillatory tES protocols, targeted towards the hallmark oscillatory activity within the cortico-hippocampal network, are increasingly coming to prominence. To increase precision and effectiveness, the need for EEG guided individualization of the tES protocols is proposed. Here, we present the study protocol in which two types of personalized oscillatory tES-transcranial alternating current stimulation (tACS) and oscillatory transcranial direct current stimulation (otDCS), both frequency-modulated to the individual theta-band frequency (ITF), are compared to the non-oscillatory transcranial direct current stimulation (tDCS) and to the sham stimulation. The study has cross-over design with four tES conditions (tACS, otDCS, tDCS, sham), and the comprehensive set of neurophysiological (resting state EEG and AM-evoked EEG) and behavioral outcomes, including AM tasks (short-term associative memory, face-word, face-object, object-location), as well as measures of other cognitive functions (cognitive control, verbal fluency, and working memory).

Measuring Brain Response to Transcutaneous Vagus Nerve Stimulation (tVNS) using Simultaneous Magnetoencephalography (MEG)

Objective: Transcutaneous vagus nerve stimulation (tVNS) is form of non-invasive brain stimulation that delivers a sequence of electrical pulses to the auricular branch of the vagus nerve, and is used increasingly in the treatment of a number of health conditions such as epilepsy and depression. Recent research has focused on the efficacy of tVNS to treat different medical conditions, but there is little conclusive evidence concerning the optimal stimulation parameters.There are relatively few studies that have combined tVNS with a neuroimaging modality, and none that have attempted simultaneous magnetoencephalography (MEG) and tVNS due to the presence of large stimulation artifacts produced by the electrical stimulation which are many orders of magnitude larger than underlying brain activity. Approach: The aim of this study is to investigate the utility of MEG to gain insight into the regions of the brain most strongly influenced by tVNS and how variation of the stimulation parameters can affect this response in healthy participants. Main Results: We have successfully demonstrated that MEG can be used to measure brain response to tVNS. We have also shown that varying the stimulation frequency can lead to a difference in brain response, with the brain also responding in different anatomical regions depending on the frequency. Significance: The main contribution of this paper is to demonstrate the feasibility of simultaneous pulsed tVNS and MEG recording, allowing direct investigation of the changes in brain activity that result from different stimulation parameters. This may lead to the development of customised therapeutic approaches for the targeted treatment of different conditions.

Toward personalized medicine in connectomic deep brain stimulation

At the group-level, deep brain stimulation leads to significant therapeutic benefit in a multitude of neurological and neuropsychiatric disorders. At the single-patient level, however, symptoms may sometimes persist despite "optimal" electrode placement at established treatment coordinates. This may be partly explained by limitations of disease-centric strategies that are unable to account for heterogeneous phenotypes and comorbidities observed in clinical practice. Instead, tailoring electrode placement and programming to individual patients' symptom profiles may increase the fraction of top-responding patients. Here, we propose a three-step, circuit-based framework with the aim of developing patient-specific treatment targets that address the unique symptom constellation prevalent in each patient. First, we describe how a symptom network target library could be established by mapping beneficial or undesirable DBS effects to distinct circuits based on (retrospective) group-level data. Second, we suggest ways of matching the resulting symptom networks to circuits defined in the individual patient (template matching). Third, we introduce network blending as a strategy to calculate optimal stimulation targets and parameters by selecting and weighting a set of symptom-specific networks based on the symptom profile and subjective priorities of the individual patient. We integrate the approach with published literature and conclude by discussing limitations and future challenges.

Neurons as will and representation

Memory recollections and voluntary actions are often perceived as spontaneously generated irrespective of external stimuli. Although products of our neurons, they are only rarely accessible in humans at the neuronal level. Here I review insights gleaned from unique neurosurgical opportunities to record and stimulate single-neuron activity in people who can declare their thoughts, memories and wishes. I discuss evidence that the subjective experience of human recollection and that of voluntary action arise from the activity of two internal neuronal generators, the former from medial temporal lobe reactivation and the latter from frontoparietal preactivation. I characterize properties of these generators and their interaction, enabling flexible recruitment of memory-based choices for action as well as recruitment of action-based plans for the representation of conceptual knowledge in memories. Both internal generators operate on surprisingly explicit but different neuronal codes, which appear to arise with distinct single-neuron activity, often observed before participants' reports of conscious awareness. I discuss prediction of behaviour based on these codes, and the potential for their modulation. The prospects of editing human memories and volitions by enhancement, inception or deletion of specific, selected content raise therapeutic possibilities and ethical concerns.

3 T MRI of rapid brain activity changes driven by subcallosal cingulate deep brain stimulation

Deep brain stimulation targeting the subcallosal cingulate area (SCC-DBS), a hub with multiple axonal projections, has shown therapeutic potential for treatment-resistant mood disorders. While SCC-DBS drives long-term metabolic changes in corticolimbic circuits, the brain areas that are directly modulated by electrical stimulation of this region are not known. We used 3.0 Tesla functional MRI to map the topography of acute brain changes produced by stimulation in an initial cohort of twelve patients with fully implanted SCC-DBS devices. Four additional SCC-DBS patients were also scanned and employed as a validation cohort. Participants underwent resting state scans (n=78 acquisitions overall) during i) inactive DBS; ii) clinically optimal active DBS; iii) suboptimal active DBS. All scans were acquired within a single MRI session, each separated by a 5-minute washout period. Analysis of the amplitude of low frequency fluctuations (ALFF) in each sequence indicated that clinically optimal SCC-DBS reduced spontaneous brain activity in several areas, including bilateral dorsal anterior cingulate cortex (dACC), posterior cingulate cortex (PCC), precuneus, and left inferior parietal lobule (pBonferroni<0.0001). Stimulation-induced dACC signal reduction correlated with immediate within-session mood fluctuations, was greater at optimal versus suboptimal settings, and related to local cingulum bundle engagement. Moreover, linear modelling showed that immediate changes in dACC, PCC, and precuneus activity could predict individual long-term antidepressant improvement. A model derived from the primary cohort that incorporated ALFF changes in these three areas (along with pre-operative symptom severity) explained 55% of the variance in clinical improvement in that cohort. The same model also explained 93% of the variance in the out-of-sample validation cohort. Additionally all three brain areas exhibited significant changes in functional connectivity between active and inactive DBS states (pBonferroni<0.01). These results provide insight into the network-level mechanisms of SCC-DBS and point towards potential acute biomarkers of clinical response that could help to optimize and personalize this therapy.

Long-term ecological assessment of intracranial electrophysiology synchronized to behavioral markers in obsessive-compulsive disorder

Detection of neural signatures related to pathological behavioral states could enable adaptive deep brain stimulation (DBS), a potential strategy for improving efficacy of DBS for neurological and psychiatric disorders. This approach requires identifying neural biomarkers of relevant behavioral states, a task best performed in ecologically valid environments. Here, in human participants with obsessive-compulsive disorder (OCD) implanted with recording-capable DBS devices, we synchronized chronic ventral striatum local field potentials with relevant, disease-specific behaviors. We captured over 1,000 h of local field potentials in the clinic and at home during unstructured activity, as well as during DBS and exposure therapy. The wide range of symptom severity over which the data were captured allowed us to identify candidate neural biomarkers of OCD symptom intensity. This work demonstrates the feasibility and utility of capturing chronic intracranial electrophysiology during daily symptom fluctuations to enable neural biomarker identification, a prerequisite for future development of adaptive DBS for OCD and other psychiatric disorders.

Neurodegenerative disorders management: state-of-art and prospects of nano-biotechnology

Neurodegenerative disorders (NDs) are highly prevalent among the aging population. It affects primarily the central nervous system (CNS) but the effects are also observed in the peripheral nervous system. Neural degeneration is a progressive loss of structure and function of neurons, which may ultimately involve cell death. Such patients suffer from debilitating memory loss and altered motor coordination which bring up non-affordable and unavoidable socio-economic burdens. Due to the unavailability of specific therapeutics and diagnostics, the necessity to control or manage NDs raised the demand to investigate and develop efficient alternative approaches. Keeping trends and advancements in view, this report describes both state-of-the-art and challenges in nano-biotechnology-based approaches to manage NDs, toward personalized healthcare management. Sincere efforts are being made to customize nano-theragnostics to control: therapeutic cargo packaging, delivery to the brain, nanomedicine of higher efficacy, deep brain stimulation, implanted stimulation, and managing brain cell functioning. These advancements are useful to design future therapy based on the severity of the patient's neurodegenerative disease. However, we observe a lack of knowledge shared among scientists of a variety of expertise to explore this multi-disciplinary research field for NDs management. Consequently, this review will provide a guideline platform that will be useful in developing novel smart nano-therapies by considering the aspects and advantages of nano-biotechnology to manage NDs in a personalized manner. Nano-biotechnology-based approaches have been proposed as effective and affordable alternatives at the clinical level due to recent advancements in nanotechnology-assisted theragnostics, targeted delivery, higher efficacy, and minimal side effects.

Neural interface systems with on-device computing: machine learning and neuromorphic architectures

Development of neural interface and brain-machine interface (BMI) systems enables the treatment of neurological disorders including cognitive, sensory, and motor dysfunctions. While neural interfaces have steadily decreased in form factor, recent developments target pervasive implantables. Along with advances in electrodes, neural recording, and neurostimulation circuits, integration of disease biomarkers and machine learning algorithms enables real-time and on-site processing of neural activity with no need for power-demanding telemetry. This recent trend on combining artificial intelligence and machine learning with modern neural interfaces will lead to a new generation of low-power, smart, and miniaturized therapeutic devices for a wide range of neurological and psychiatric disorders. This paper reviews the recent development of the 'on-chip' machine learning and neuromorphic architectures, which is one of the key puzzles in devising next-generation clinically viable neural interface systems.

Striving toward translation: strategies for reliable fMRI measurement

fMRI has considerable potential as a translational tool for understanding risk, prioritizing interventions, and improving the treatment of brain disorders. However, recent studies have found that many of the most widely used fMRI measures have low reliability, undermining this potential. Here, we argue that many fMRI measures are unreliable because they were designed to identify group effects, not to precisely quantify individual differences. We then highlight four emerging strategies [extended aggregation, reliability modeling, multi-echo fMRI (ME-fMRI), and stimulus design] that build on established psychometric properties to generate more precise and reliable fMRI measures. By adopting such strategies to improve reliability, we are optimistic that fMRI can fulfill its potential as a clinical tool.

Closed-loop automated reaching apparatus (CLARA) for interrogating complex motor behaviors

Objective.Closed-loop neuromodulation technology is a rapidly expanding category of therapeutics for a broad range of indications. Development of these innovative neurological devices requires high-throughput systems for closed-loop stimulation of model organisms, while monitoring physiological signals and complex, naturalistic behaviors. To address this need, we developed CLARA, a closed-loop automated reaching apparatus.Approach.Using breakthroughs in computer vision, CLARA integrates fully-automated, markerless kinematic tracking of multiple features to classify animal behavior and precisely deliver neural stimulation based on behavioral outcomes. CLARA is compatible with advanced neurophysiological tools, enabling the testing of neurostimulation devices and identification of novel neurological biomarkers.Results.The CLARA system tracks unconstrained skilled reach behavior in 3D at 150 Hz without physical markers. The system fully automates trial initiation and pellet delivery and is capable of accurately delivering stimulation in response to trial outcome with short latency. Kinematic data from the CLARA system provided novel insights into the dynamics of reach consistency over the course of learning, suggesting that learning selectively improves reach failures but does not alter the kinematics of successful reaches. Additionally, using the closed-loop capabilities of CLARA, we demonstrate that vagus nerve stimulation (VNS) improves skilled reach performance and increases reach trajectory consistency in healthy animals.Significance.The CLARA system is the first mouse behavior apparatus that uses markerless pose tracking to provide real-time closed-loop stimulation in response to the outcome of an unconstrained motor task. Additionally, we demonstrate that the CLARA system was essential for our investigating the role of closed-loop VNS stimulation on motor performance in healthy animals. This approach has high translational relevance for developing neurostimulation technology based on complex human behavior.

Neuromodulatory treatments for psychiatric disease: A comprehensive survey of the clinical trial landscape

BACKGROUND

Numerous neuromodulatory therapies are currently under investigation or in clinical use for the treatment of psychiatric conditions.

OBJECTIVE/HYPOTHESIS

We sought to catalogue past and present human research studies on psychiatric neuromodulation and identify relevant trends in this field.

METHODS

ClinicalTrials.gov (https://www.clinicaltrials.gov/) and the International Clinical Trials Registry Platform (https://www.who.int/ictrp/en/) were queried in March 2020 for trials assessing the outcome of neuromodulation for psychiatric disorders. Relevant trials were categorized by variables such as neuromodulation modality, country, brain target, publication status, design, and funding source.

RESULTS

From 72,086 initial search results, 1252 unique trials were identified. The number of trials registered annually has consistently increased. Half of all trials were active and a quarter have translated to publications. The largest proportion of trials involved depression (45%), schizophrenia (18%), and substance use disorders (14%). Trials spanned 37 countries; China, the second largest contributor (13%) after the United States (28%), has increased its output substantially in recent years. Over 75% of trials involved non-convulsive non-invasive modalities (e.g., transcranial magnetic stimulation), while convulsive (e.g., electroconvulsive therapy) and invasive modalities (e.g., deep brain stimulation) were less represented. 72% of trials featured approved or cleared interventions. Characteristic inter-modality differences were observed with respect to enrollment size, trial design/phase, and funding. Dorsolateral prefrontal cortex accounted for over half of focal neuromodulation trial targets. The proportion of trials examining biological correlates of neuromodulation has increased.

CONCLUSION(S)

These results provide a comprehensive overview of the state of psychiatric neuromodulation research, revealing the growing scope and internationalism of this field.

Electrical deep neuromodulation in psychiatry

Addressing treatment refractoriness in psychiatric diseases is an essential public health objective. The last two decades have seen an increasing interest for deep brain stimulation (DBS) of several brain targets. In this chapter, we have reviewed the main DBS clinical trials in psychiatric diseases, mainly obsessive compulsive disorders (OCD) and depression, but also emerging research in other psychiatric disorders. While its efficacy and safety are confirmed, DBS is still not considered as standard therapy in psychiatry. However, advances in neuroimaging research combined to behavioral and electrophysiological data uniquely provided by DBS studies improve knowledge on physiopathology in these brain diseases. This will help define the optimal brain targets according to specific phenotype dimensions. Revealing the mechanisms of action and effects of DBS will support that its impact goes beyond a loco-regional brain stimulation and confirms that electrical neuromodulation influences brain networks. Added to the progress in neuromodulation technology, these insights will hopefully facilitate a more widespread application of this promising treatment. Future development of a personalized multimodal assessment of underlying dysfunctional brain networks will open new circuit-specific treatment perspectives that may facilitate better patient outcomes.

Invasive Electrophysiology for Circuit Discovery and Study of Comorbid Psychiatric Disorders in Patients With Epilepsy: Challenges, Opportunities, and Novel Technologies

Intracranial electroencephalographic (iEEG) recordings from patients with epilepsy provide distinct opportunities and novel data for the study of co-occurring psychiatric disorders. Comorbid psychiatric disorders are very common in drug-resistant epilepsy and their added complexity warrants careful consideration. In this review, we first discuss psychiatric comorbidities and symptoms in patients with epilepsy. We describe how epilepsy can potentially impact patient presentation and how these factors can be addressed in the experimental designs of studies focused on the electrophysiologic correlates of mood. Second, we review emerging technologies to integrate long-term iEEG recording with dense behavioral tracking in naturalistic environments. Third, we explore questions on how best to address the intersection between epilepsy and psychiatric comorbidities. Advances in ambulatory iEEG and long-term behavioral monitoring technologies will be instrumental in studying the intersection of seizures, epilepsy, psychiatric comorbidities, and their underlying circuitry.

Clinical Implications of Androgen-Positive Triple-Negative Breast Cancer

This review summarizes the recent findings of a vast array of studies conducted on androgen receptor-positive triple-negative breast cancer (AR-positive TNBC) to provide a better understanding of this specific breast cancer subgroup. AR expression is correlated with higher age, lower histological grade, lower proliferation index Ki-67, spiculated masses, and calcifications on mammography. Studies investigating the correlation between AR expression and lymph node metastasis are highly discordant. In addition, results regarding prognosis are highly contradictory. AR antagonists are a promising novel therapeutic approach in AR-positive TNBC. However, AR signaling pathways should be more investigated in order to understand the influence of AR expression on TNBC more thoroughly.