Deep brain stimulation

Hypothalamic Supramammillary Control of Cognition and Motivation

The supramammillary nucleus (SuM) is a small region in the ventromedial posterior hypothalamus. The SuM has been relatively understudied with much of the prior focus being on its connection with septo-hippocampal circuitry. Thus, most studies conducted until the 21st century examined its role in hippocampal processes, such as theta rhythm and learning/memory. In recent years, the SuM has been "rediscovered" as a crucial hub for several behavioral and cognitive processes, including reward-seeking, exploration, and social memory. Additionally, it has been shown to play significant roles in hippocampal plasticity and adult neurogenesis. This review highlights findings from recent studies using cutting-edge systems neuroscience tools that have shed light on these fascinating roles for the SuM.

Advances in Deep Brain Stimulation: From Mechanisms to Applications

Deep brain stimulation (DBS) is an effective therapy for various neurologic and neuropsychiatric disorders, involving chronic implantation of electrodes into target brain regions for electrical stimulation delivery. Despite its safety and efficacy, DBS remains an underutilized therapy. Advances in the field of DBS, including in technology, mechanistic understanding, and applications have the potential to expand access and use of DBS, while also improving clinical outcomes. Developments in DBS technology, such as MRI compatibility and bidirectional DBS systems capable of sensing neural activity while providing therapeutic stimulation, have enabled advances in our understanding of DBS mechanisms and its application. In this review, we summarize recent work exploring DBS modulation of target networks. We also cover current work focusing on improved programming and the development of novel stimulation paradigms that go beyond current standards of DBS, many of which are enabled by sensing-enabled DBS systems and have the potential to expand access to DBS.

Automated calibration of somatosensory stimulation using reinforcement learning

BACKGROUND

The identification of the electrical stimulation parameters for neuromodulation is a subject-specific and time-consuming procedure that presently mostly relies on the expertise of the user (e.g., clinician, experimenter, bioengineer). Since the parameters of stimulation change over time (due to displacement of electrodes, skin status, etc.), patients undergo recurrent, long calibration sessions, along with visits to the clinics, which are inefficient and expensive. To address this issue, we developed an automatized calibration system based on reinforcement learning (RL) allowing for accurate and efficient identification of the peripheral nerve stimulation parameters for somatosensory neuroprostheses.

METHODS

We developed an RL algorithm to automatically select neurostimulation parameters for restoring sensory feedback with transcutaneous electrical nerve stimulation (TENS). First, the algorithm was trained offline on a dataset comprising 49 subjects. Then, the neurostimulation was then integrated with a graphical user interface (GUI) to create an intuitive AI-based mapping platform enabling the user to autonomously perform the sensation characterization procedure. We assessed the algorithm against the performance of both experienced and naïve and of a brute force algorithm (BFA), on 15 nerves from five subjects. Then, we validated the AI-based platform on six neuropathic nerves affected by distal sensory loss.

RESULTS

Our automatized approach demonstrated the ability to find the optimal values of neurostimulation achieving reliable and comfortable elicited sensations. When compared to alternatives, RL outperformed the naïve and BFA, significantly decreasing the time for mapping and the number of delivered stimulation trains, while improving the overall quality. Furthermore, the RL algorithm showed performance comparable to trained experimenters. Finally, we exploited it successfully for eliciting sensory feedback in neuropathic patients.

CONCLUSIONS

Our findings demonstrated that the AI-based platform based on a RL algorithm can automatically and efficiently calibrate parameters for somatosensory nerve stimulation. This holds promise to avoid experts' employment in similar scenarios, thanks to the merging between AI and neurotech. Our RL algorithm has the potential to be used in other neuromodulation fields requiring a mapping process of the stimulation parameters.

TRIAL REGISTRATION

ClinicalTrial.gov (Identifier: NCT04217005).

Deep brain stimulation for treatment resistant obsessive compulsive disorder; an observational study with ten patients under real-life conditions

Introduction

Obsessive-compulsive disorder (OCD) affects 2-3% of the global population, causing distress in many functioning levels. Standard treatments only lead to a partial recovery, and about 10% of the patients remain treatment-resistant. Deep brain stimulation offers a treatment option for severe, therapy-refractory OCD, with a reported response of about 60%. We report a comprehensive clinical, demographic, and treatment data for patients who were treated with DBS in our institution.

Methods

We offered DBS to patients with severe chronic treatment resistant OCD. Severity was defined as marked impairment in functioning and treatment resistance was defined as non-response to adequate trials of medications and psychotherapy. Between 2020 and 2022, 11 patients were implanted bilaterally in the bed nucleus of stria terminalis (BNST). Patients were evaluated with YBOCS, MADRS, GAF, CGI, and WHOQOL-BREF. We performed the ratings at baseline (before surgery), after implantation before the start of the stimulation, after reaching satisfactory stimulation parameters, and at follow-up visits 3, 6, 9, and 12 months after optimized stimulation.

Results

One patient has retracted his consent to publish the results of his treatment, thus we are reporting the results of 10 patients (5 males, 5 females, mean age: 37 years). Out of our 10 patients, 6 have shown a clear response indicated by a YBOCS-reduction between 42 and 100 percent at last follow-up. One further patient experienced a subjectively dramatic effect on OCD symptoms, but opted afterwards to stop the stimulation. The other 3 patients showed a slight, non-significant improvement of YBOCS between 8.8 and 21.9%. The overall mean YBOCS decreased from 28.3 at baseline to 13.3 (53% reduction) at the last follow-up. The improvement of the OCD symptoms was also accompanied by an improvement of depressive symptoms, global functioning, and quality of life.

Conclusion

Our results suggest that BNST-DBS can be effective for treatment-resistant OCD patients, as indicated by a reduction in symptoms and an overall improvement in functioning. Despite the need for additional research to define the patients' selection criteria, the most appropriate anatomical target, and the most effective stimulation parameters, improved patient access for this therapy should be established.

Forniceal deep brain stimulation in a mouse model of Rett syndrome increases neurogenesis and hippocampal memory beyond the treatment period

BACKGROUND

Rett syndrome (RTT), caused by mutations in the X-linked gene encoding methyl-CpG binding protein 2 (MeCP2), severely impairs learning and memory. We previously showed that forniceal deep brain stimulation (DBS) stimulates hippocampal neurogenesis with concomitant improvements in hippocampal-dependent learning and memory in a mouse model of RTT.

OBJECTIVES

To determine the duration of DBS benefits; characterize DBS effects on hippocampal neurogenesis; and determine whether DBS influences MECP2 genotype and survival of newborn dentate granular cells (DGCs) in RTT mice.

METHODS

Chronic DBS was delivered through an electrode implanted in the fimbria-fornix. We tested separate cohorts of mice in contextual and cued fear memory at different time points after DBS. We then examined neurogenesis, DGC apoptosis, and the expression of brain-derived neurotrophic factor (BDNF) and vascular endothelial growth factor (VEGF) after DBS by immunohistochemistry.

RESULTS

After two weeks of forniceal DBS, memory improvements lasted between 6 and 9 weeks. Repeating DBS every 6 weeks was sufficient to maintain the improvement. Forniceal DBS stimulated the birth of more MeCP2-positive than MeCP2-negative DGCs and had no effect on DGC survival. It also increased the expression of BDNF but not VEGF in the RTT mouse dentate gyrus.

CONCLUSION

Improvements in learning and memory from forniceal DBS in RTT mice extends well beyond the treatment period and can be maintained by repeated DBS. Stimulation of BDNF expression correlates with improvements in hippocampal neurogenesis and memory benefits.

Desynchronizing oscillators coupled in multi-cluster networks through adaptively controlling partial networks

This article introduces an adaptive control scheme with a feedback delay, specifically designed for controlling partial networks, to achieve desynchronization in a coupled network with two or multiple clusters. The proposed scheme's effectiveness is validated through several representative examples of coupled neuronal networks with two interconnected clusters. The efficacy of this scheme is attributed to the rigorous and numerical analyses on the corresponding transcendental characteristic equation, which includes time delay and other network parameters. In addition to investigating the impact of time delay and inter-connectivity on the stability of an incoherent state, we also rigorously find that controlling only one cluster cannot realize the desynchronization in the coupled oscillators within three or more clusters. All these, we believe, can deepen the understanding of the deep brain stimulation techniques presently used in the clinical treatment of neurodegenerative diseases and suggest future avenues for enhancing these clinical techniques through adaptive feedback settings.

Protection of high-frequency low-intensity pulsed electric fields and brain-derived neurotrophic factor for SH-SY5Y cells against hydrogen peroxide-induced cell damage

Neurodegenerative diseases (NDDs) pose a significant global health threat. In particular, Alzheimer disease, the most common type causing dementia, remains an incurable disease. Alzheimer disease is thought to be associated with an imbalance of reactive oxygen species (ROS) in neurons, and scientists considered ROS modulation as a promising strategy for novel remedies. In the study, human neural cell line SH-SY5Y was used in probing the effect of combining noninvasive high-frequency low-intensity pulsed electric field (H-LIPEF) and brain-derived neurotrophic factor (BDNF) in protection against hydrogen peroxide (H2O2)-induced neuron damage. Our result finds that the combination approach has intensified the neuroprotective effect significantly, perhaps due to H-LIPEF and BDNF synergistically increasing the expression level of the phosphorylated epidermal growth factor receptor (p-EGFR), which induces the survival-related mitogen-activated protein kinases (MAPK) proteins. The study confirmed the activation of extracellular signal-regulated kinase (ERK) and the downstream pro-survival and antioxidant proteins as the mechanism underlying neuron protection. These findings highlighted the potential of H-LIPEF combined with BDNF in the treatment of NDDs. Furthermore, BDNF-mimetic drugs combining with noninvasive H-LIPEF to patients is a promising approach worthy of further research. This points to strategies for selecting drugs to cooperate with electric fields in treating neurodegenerative disorders.

Thalamo-cortical and cerebello-cortical functional connectivity in development

The thalamus is a critical relay center for neural pathways involving sensory, motor, and cognitive functions, including cortico-striato-thalamo-cortical and cortico-ponto-cerebello-thalamo-cortical loops. Despite the importance of these circuits, their development has been understudied. One way to investigate these pathways in human development in vivo is with functional connectivity MRI, yet few studies have examined thalamo-cortical and cerebello-cortical functional connectivity in development. Here, we used resting-state functional connectivity to measure functional connectivity in the thalamus and cerebellum with previously defined cortical functional networks in 2 separate data sets of children (7-12 years old) and adults (19-40 years old). In both data sets, we found stronger functional connectivity between the ventral thalamus and the somatomotor face cortical functional network in children compared with adults, extending previous cortico-striatal functional connectivity findings. In addition, there was more cortical network integration (i.e. strongest functional connectivity with multiple networks) in the thalamus in children than in adults. We found no developmental differences in cerebello-cortical functional connectivity. Together, these results suggest different maturation patterns in cortico-striato-thalamo-cortical and cortico-ponto-cerebellar-thalamo-cortical pathways.

The evolution of Big Data in neuroscience and neurology

Neurological diseases are on the rise worldwide, leading to increased healthcare costs and diminished quality of life in patients. In recent years, Big Data has started to transform the fields of Neuroscience and Neurology. Scientists and clinicians are collaborating in global alliances, combining diverse datasets on a massive scale, and solving complex computational problems that demand the utilization of increasingly powerful computational resources. This Big Data revolution is opening new avenues for developing innovative treatments for neurological diseases. Our paper surveys Big Data's impact on neurological patient care, as exemplified through work done in a comprehensive selection of areas, including Connectomics, Alzheimer's Disease, Stroke, Depression, Parkinson's Disease, Pain, and Addiction (e.g., Opioid Use Disorder). We present an overview of research and the methodologies utilizing Big Data in each area, as well as their current limitations and technical challenges. Despite the potential benefits, the full potential of Big Data in these fields currently remains unrealized. We close with recommendations for future research aimed at optimizing the use of Big Data in Neuroscience and Neurology for improved patient outcomes.

Supplementary Information

The online version contains supplementary material available at 10.1186/s40537-023-00751-2.

Hydrogel Magnetomechanical Actuator Nanoparticles for Wireless Remote Control of Mechanosignaling In Vivo

As a new enabling nanotechnology tool for wireless, target-specific, and long-distance stimulation of mechanoreceptors in vivo, here we present a hydrogel magnetomechanical actuator (h-MMA) nanoparticle. To allow both deep-tissue penetration of input signals and efficient force generation, h-MMA integrates a two-step transduction mechanism that converts magnetic anisotropic energy to thermal energy within its magnetic core (i.e., Zn0.4Fe2.6O4 nanoparticle cluster) and then to mechanical energy to induce the surrounding polymer (i.e., pNiPMAm) shell contraction, finally delivering forces to activate targeted mechanoreceptors. We show that h-MMAs enable on-demand modulation of Notch signaling in both fluorescence reporter cell lines and a xenograft mouse model, demonstrating its utility as a powerful in vivo perturbation approach for mechanobiology interrogation in a minimally invasive and untethered manner.

Electrochemical and electrophysiological considerations for clinical high channel count neural interfaces

Electrophysiological recording and stimulation are the gold standard for functional mapping during surgical and therapeutic interventions as well as capturing cellular activity in the intact human brain. A critical component probing human brain activity is the interface material at the electrode contact that electrochemically transduces brain signals to and from free charge carriers in the measurement system. Here, we summarize state-of-the-art electrode array systems in the context of translation for use in recording and stimulating human brain activity. We leverage parametric studies with multiple electrode materials to shed light on the varied levels of suitability to enable high signal-to-noise electrophysiological recordings as well as safe electrophysiological stimulation delivery. We discuss the effects of electrode scaling for recording and stimulation in pursuit of high spatial resolution, channel count electrode interfaces, delineating the electrode-tissue circuit components that dictate the electrode performance. Finally, we summarize recent efforts in the connectorization and packaging for high channel count electrode arrays and provide a brief account of efforts toward wireless neuronal monitoring systems.

Computational analysis of multichannel magnetothermal neural stimulation using magnetic resonator array

Heating nanoparticles with a magnetic field could facilitate selective remote control of neural activity in deep tissue. However, current magnetothermal stimulation approaches are limited to single-channel stimulation. Here, we investigated various designs for multichannel magnetothermal stimulation based on an array of resonant coils that are driven by a single loop coil. Using a tuning capacitor that allows resonant coils to resonate at the operating frequency, each coil's ON and OFF resonance can be controlled, enabling us to select stimulation channels. We found that smaller inner diameters of resonant coils produce more localized magnetic fields while larger coils produce magnetic fields over a longer distance. The constructed multichannel resonant coil arrays can provide a high enough magnetic field intensity to raise the temperature of nanoparticles by 8 °C when we apply 35.2 W into the loop coil that is spaced 1 mm from the target neurons. This multichannel stimulation using a simple resonant circuit approach would be useful for clinical applications of magnetothermal neural stimulation.

A least square generative network based on invariant contrastive feature pair learning for multimodal MR image synthesis

PURPOSE

During MR-guided neurosurgical procedures, several factors may limit the acquisition of additional MR sequences, which are needed by neurosurgeons to adjust surgical plans or ensure complete tumor resection. Automatically synthesized MR contrasts generated from other available heterogeneous MR sequences could alleviate timing constraints.

METHODS

We propose a new multimodal MR synthesis approach leveraging a combination of MR modalities presenting glioblastomas to generate an additional modality. The proposed learning approach relies on a least square GAN (LSGAN) using an unsupervised contrastive learning strategy. We incorporate a contrastive encoder, which extracts an invariant contrastive representation from augmented pairs of the generated and real target MR contrasts. This contrastive representation describes a pair of features for each input channel, allowing to regularize the generator to be invariant to the high-frequency orientations. Moreover, when training the generator, we impose on the LSGAN loss another term reformulated as the combination of a reconstruction and a novel perception loss based on a pair of features.

RESULTS

When compared to other multimodal MR synthesis approaches evaluated on the BraTS'18 brain dataset, the model yields the highest Dice score with [Formula: see text] and achieves the lowest variability information of [Formula: see text], with a probability rand index score of [Formula: see text] and a global consistency error of [Formula: see text].

CONCLUSION

The proposed model allows to generate reliable MR contrasts with enhanced tumors on the synthesized image using a brain tumor dataset (BraTS'18). In future work, we will perform a clinical evaluation of residual tumor segmentations during MR-guided neurosurgeries, where limited MR contrasts will be acquired during the procedure.

Piezoelectric Nanomaterials Activated by Ultrasound in Disease Treatment

Electric stimulation has been used in changing the morphology, status, membrane permeability, and life cycle of cells to treat certain diseases such as trauma, degenerative disease, tumor, and infection. To minimize the side effects of invasive electric stimulation, recent studies attempt to apply ultrasound to control the piezoelectric effect of nano piezoelectric material. This method not only generates an electric field but also utilizes the benefits of ultrasound such as non-invasive and mechanical effects. In this review, important elements in the system, piezoelectricity nanomaterial and ultrasound, are first analyzed. Then, we summarize recent studies categorized into five kinds, nervous system diseases treatment, musculoskeletal tissues treatment, cancer treatment, anti-bacteria therapy, and others, to prove two main mechanics under activated piezoelectricity: one is biological change on a cellular level, the other is a piezo-chemical reaction. However, there are still technical problems to be solved and regulation processes to be completed before widespread use. The core problems include how to accurately measure piezoelectricity properties, how to concisely control electricity release through complex energy transfer processes, and a deeper understanding of related bioeffects. If these problems are conquered in the future, piezoelectric nanomaterials activated by ultrasound will provide a new pathway and realize application in disease treatment.

Aberrant neurophysiological signaling associated with speech impairments in Parkinson's disease

Difficulty producing intelligible speech is a debilitating symptom of Parkinson's disease (PD). Yet, both the robust evaluation of speech impairments and the identification of the affected brain systems are challenging. Using task-free magnetoencephalography, we examine the spectral and spatial definitions of the functional neuropathology underlying reduced speech quality in patients with PD using a new approach to characterize speech impairments and a novel brain-imaging marker. We found that the interactive scoring of speech impairments in PD (N = 59) is reliable across non-expert raters, and better related to the hallmark motor and cognitive impairments of PD than automatically-extracted acoustical features. By relating these speech impairment ratings to neurophysiological deviations from healthy adults (N = 65), we show that articulation impairments in patients with PD are associated with aberrant activity in the left inferior frontal cortex, and that functional connectivity of this region with somatomotor cortices mediates the influence of cognitive decline on speech deficits.

Ventricular Fibrillation Simulated Electrocardiogram Artifact by a Deep Brain Stimulator

Deep brain stimulation devices can disrupt cardiac rhythm interpretation by causing electrocardiogram artifact. We report the case of a deep brain stimulating device initiating ventricular fibrillation simulated electrocardiogram artifact in the prehospital setting. Mimicked ventricular fibrillation due to a deep brain stimulator has not been documented, and if unrecognized could influence unwarranted or potentially harmful clinical decisions.

Acute deep brain stimulation of the paraventricular nucleus of the hypothalamus increases brown adipose tissue thermogenesis in rats

Brown adipose tissue (BAT) activity is controlled by the sympathetic nervous system. Activation of BAT has shown significant promise in preclinical studies to elicit weight loss. Since the hypothalamic paraventricular nucleus (PVN) contributes to the regulation of BAT thermogenic activity, we sought to determine the effects of electrical stimulation of the PVN as a model of deep brain stimulation (DBS) for increasing BAT sympathetic nerve activity (SNA). The rostral raphe pallidus area (rRPa) was also chosen as a target for DBS since it contains the sympathetic premotor neurons for BAT. Electrical stimulation (100 µA, 100 µs, 100 Hz, for 5 min at a 50 % duty cycle) of the PVN increased BAT SNA and BAT thermogenesis. These effects were prevented by a local nanoinjection of bicuculline, a GABAA receptor antagonist. We suggest that electrical stimulation of the PVN elicited local release of GABA, which inhibited BAT sympathoinhibitory neurons in PVN, thereby releasing a restraint on BAT SNA. Electrical stimulation of the rRPa inhibited BAT thermogenesis and this was prevented by a local nanoinjection of bicuculline, suggesting that local release of GABA suppressed BAT SNA. Electrical stimulation of the PVN activates BAT metabolism via a mechanism that may include activation of local GABAA receptors. These findings contribute to our understanding of the mechanisms underlying the effects of DBS in the regulation of fat metabolism and provide a foundation for further DBS studies targeting hypothalamic circuits regulating BAT thermogenesis as a therapy for obesity.

Anticipatory Postural Adjustments and Compensatory Postural Responses to Multidirectional Perturbations-Effects of Medication and Subthalamic Nucleus Deep Brain Stimulation in Parkinson's Disease

BACKGROUND

Postural instability is one of the most restricting motor symptoms for patients with Parkinson's disease (PD). While medication therapy only shows minor effects, it is still unclear whether medication in conjunction with deep brain stimulation (DBS) of the subthalamic nucleus (STN) improves postural stability. Hence, the aim of this study was to investigate whether PD patients treated with medication in conjunction with STN-DBS have superior postural control compared to patients treated with medication alone.

METHODS

Three study groups were tested: PD patients on medication (PD-MED), PD patients on medication and on STN-DBS (PD-MED-DBS), and healthy elderly subjects (HS) as a reference. Postural performance, including anticipatory postural adjustments (APA) prior to perturbation onset and compensatory postural responses (CPR) following multidirectional horizontal perturbations, was analyzed using force plate and electromyography data.

RESULTS

Regardless of the treatment condition, both patient groups showed inadequate APA and CPR with early and pronounced antagonistic muscle co-contractions compared to healthy elderly subjects. Comparing the treatment conditions, study group PD-MED-DBS only showed minor advantages over group PD-MED. In particular, group PD-MED-DBS showed faster postural reflexes and tended to have more physiological co-contraction ratios.

CONCLUSION

medication in conjunction with STN-DBS may have positive effects on the timing and amplitude of postural control.

Subthalamic neurons interact with nigral dopaminergic neurons to regulate movement in mice

AIM

This study aims to address the role of the interaction between subthalamic (STN) neurons and substantia nigra pars compacta (SNc) dopaminergic (DA) neurons in movement control.

METHODS

Fiber photometry and optogenetic/chemogenetic techniques were utilized to monitor and manipulate neuronal activity, respectively. Locomotion in mice was recorded in an open field arena and on a head-fixed apparatus. A hemiparkinsonian mouse model was established by unilateral injection of 6-OHDA in the medial forebrain bundle. Whole-cell patch-clamp techniques were applied to record electrophysiological signals in STN neurons and SNc DA neurons. c-Fos-immunostaining was used to label activated neurons. A rabies virus-based retrograde tracing system was used to visualize STN neurons projecting to SNc DA neurons.

RESULTS

The activity of STN neurons was enhanced upon locomotion in an open field arena and on a head-fixed apparatus, and the enhancement was significantly attenuated in parkinsonian mice. Optogenetic stimulation of STN neurons enhanced locomotion, increased activity of SNc DA neurons, meanwhile, reduced latency to movement initiation. Combining optogenetics with patch-clamp recordings, we confirmed that STN neurons innervated SNc DA neurons through glutamatergic monosynaptic connections. Moreover, STN neurons projecting to SNc DA neurons were evenly distributed in the STN. Either 6-OHDA-lesion or chemogenetic inhibition of SNc DA neurons attenuated the enhancement of locomotion by STN stimulation.

CONCLUSION

SNc DA neurons not only affect the response of STN neurons to movement, but also contribute to the enhancement of movement by STN stimulation. This study demonstrates the role of STN-SNc interaction in movement control.

Differential remodeling of subthalamic projections to basal ganglia output nuclei and locomotor deficits in 6-OHDA-induced hemiparkinsonian mice

The subthalamic nucleus (STN) controls basal ganglia outputs via the substantia nigra pars reticulata (SNr) and the globus pallidus internus (GPi). However, the synaptic properties of these projections and their roles in motor control remain unclear. We show that the STN-SNr and STN-GPi projections differ markedly in magnitude and activity-dependent plasticity despite the existence of collateral STN neurons projecting to both the SNr and GPi. Stimulation of either STN projection reduces locomotion; in contrast, inhibition of either the STN-SNr projection or collateral STN neurons facilitates locomotion. In 6-OHDA-hemiparkinsonian mice, the STN-SNr projection is dramatically attenuated, but the STN-GPi projection is robustly enhanced; apomorphine inhibition of the STN-GPi projection through D2 receptors is significantly augmented and improves locomotion. Optogenetic inhibition of either the STN-SNr or STN-GPi projection improves parkinsonian bradykinesia. These results suggest that the STN-GPi and STN-SNr projections are differentially involved in motor control in physiological and parkinsonian conditions.

Investigating Deep Brain Stimulation of the Habenula: A Review of Clinical Studies

OBJECTIVES

The aim of this study was to examine the current scientific literature on deep brain stimulation (DBS) targeting the habenula for the treatment of neuropsychiatric disorders including schizophrenia, major depressive disorder, and obsessive-compulsive disorder (OCD).

MATERIALS AND METHODS

Two authors performed independent data base searches using the PubMed, Cochrane, PsycINFO, and Web of Science search engines. The data bases were searched for the query ("deep brain stimulation" and "habenula"). The inclusion criteria involved screening for human clinical trials written in English and published from 2007 to 2020. From the eligible studies, data were collected on the mean age, sex, number of patients included, and disorder treated. Patient outcomes of each study were summarized.

RESULTS

The search yielded six studies, which included 11 patients in the final analysis. Treated conditions included refractory depression, bipolar disorder, OCD, schizophrenia, and major depressive disorder. Patients with bipolar disorder unmedicated for at least two months had smaller habenula volumes than healthy controls. High-frequency stimulation of the lateral habenula attenuated the rise of serotonin in the dorsal raphe nucleus for treating depression. Bilateral habenula DBS and patient OCD symptoms were reduced and maintained at one-year follow up. Low- and high-frequency stimulation DBS can simulate input paths to the lateral habenula to treat addiction, including cocaine addiction. More data are needed to draw conclusions as to the impact of DBS for schizophrenia and obesity.

CONCLUSIONS

The habenula is a novel target that could aid in reducing neuropsychiatric symptoms and should be considered in circuit-specific investigation of neuromodulation for psychiatric disorders. More information needs to be gathered and assessed before this treatment is fully approved for treatment of neuropsychiatric conditions.

[Pain Management in Palliative Care]

Patients in the palliative phase of a disease often suffer from pain, which leads to a significant reduction in quality of life. Since in most cases there is a progression rather than an improvement of the disease over time, pain therapy must also be dynamically adapted. Due to accompanying symptoms and the physical burden of the disease, treatment of pain is often difficult. In the palliative situation, pain should not only be understood as an excitation of nociceptors but is rather also an expression of mental stress. In this article, we would like to give an overview of the available drugs, application methods and alternative treatment options. Specifically, the article is divided into the following subsections: non-opioid analgesics, opioids, co-analgesics, patient-controlled analgesia procedures, neuraxial and peripheral regional anesthesia procedures, neurolysis, supportive therapies and palliative sedation. Thus, when treating palliative patients, the focus should not only be on the one symptom of pain, but, in the sense of the bio-psycho-social model, a multimodal oriented treatment of the patient with all his symptoms, also including his relatives, must be carried out.

Mitigating Mismatch Compression in Differential Local Field Potentials

Deep brain stimulation (DBS) devices capable of measuring differential local field potentials ( ∂ LFP) enable neural recordings alongside clinical therapy. Efforts to identify oscillatory correlates of various brain disorders, or disease readouts, are growing but must proceed carefully to ensure readouts are not distorted by brain environment. In this report we identified, characterized, and mitigated a major source of distortion in ∂ LFP that we introduce as mismatch compression (MC). Using in vivo, in silico, and in vitro models of MC, we showed that impedance mismatches in the two recording electrodes can yield incomplete rejection of stimulation artifact and subsequent gain compression that distorts oscillatory power. We then developed and validated an opensource mitigation pipeline that mitigates the distortions arising from MC. This work enables more reliable oscillatory readouts for adaptive DBS applications.

A computational study on the optimization of transcranial temporal interfering stimulation with high-definition electrodes using unsupervised neural networks

Transcranial temporal interfering stimulation (tTIS) can focally stimulate deep parts of the brain related to specific functions using beats at two high frequencies that do not individually affect the human brain. However, the complexity and nonlinearity of the simulation limit it in terms of calculation time and optimization precision. We propose a method to quickly optimize the interfering current value of high-definition electrodes, which can finely stimulate the deep part of the brain, using an unsupervised neural network (USNN) for tTIS. We linked a network that generates the values of electrode currents to another network, which is constructed to compute the interference exposure, for optimization by comparing the generated stimulus with the target stimulus. Further, a computational study was conducted using 16 realistic head models. We also compared tTIS with transcranial alternating current stimulation (tACS), in terms of performance and characteristics. The proposed method generated the strongest stimulation at the target, even when targeting deep areas or performing multi-target stimulation. The high-definition tTISl was less affected than tACS by target depth, and mis-stimulation was reduced compared with the case of using two-pair inferential stimulation in deep region. The optimization of the electrode currents for the target stimulus could be performed in 3 min. Using the proposed USNN for tTIS, we demonstrated that the electrode currents of tTIS can be optimized quickly and accurately. Moreover, we confirmed the possibility of precisely stimulating the deep parts of the brain via transcranial electrical stimulation.

Phase-adaptive brain stimulation of striatal D1 medium spiny neurons in dopamine-depleted mice

Brain rhythms are strongly linked with behavior, and abnormal rhythms can signify pathophysiology. For instance, the basal ganglia exhibit a wide range of low-frequency oscillations during movement, but pathological "beta" rhythms at ~ 20 Hz have been observed in Parkinson's disease (PD) and in PD animal models. All brain rhythms have a frequency, which describes how often they oscillate, and a phase, which describes the precise time that peaks and troughs of brain rhythms occur. Although frequency has been extensively studied, the relevance of phase is unknown, in part because it is difficult to causally manipulate the instantaneous phase of ongoing brain rhythms. Here, we developed a phase-adaptive, real-time, closed-loop algorithm to deliver optogenetic stimulation at a specific phase with millisecond latency. We combined this Phase-Adaptive Brain STimulation (PABST) approach with cell-type-specific optogenetic methods to stimulate basal ganglia networks in dopamine-depleted mice that model motor aspects of human PD. We focused on striatal medium spiny neurons expressing D1-type dopamine receptors because these neurons can facilitate movement. We report three main results. First, we found that our approach delivered PABST within system latencies of 13 ms. Second, we report that closed-loop stimulation powerfully influenced the spike-field coherence of local brain rhythms within the dorsal striatum. Finally, we found that both 4 Hz PABST and 20 Hz PABST improved movement speed, but we found differences between phase only with 4 Hz PABST. These data provide causal evidence that phase is relevant for brain stimulation, which will allow for more precise, targeted, and individualized brain stimulation. Our findings are applicable to a broad range of preclinical brain stimulation approaches and could also inform circuit-specific neuromodulation treatments for human brain disease.

Bio-hybrid electronic and photonic devices

Bio-hybrid devices, combining electronic and photonic components with cells, tissues, and organs, hold potential for advancing our understanding of biology, physiology, and pathologies and for treating a wide range of conditions and diseases. In this review, I describe the devices, materials, and technologies that enable bio-hybrid devices and provide examples of their utilization at multiple biological scales ranging from the subcellular to whole organs. Finally, I describe the outcomes of a National Science Foundation (NSF)-funded workshop envisioning potential applications of these technologies to improve health outcomes and quality of life.

Modulating cell signalling in vivo with magnetic nanotransducers

Weak magnetic fields offer nearly lossless transmission of signals within biological tissue. Magnetic nanomaterials are capable of transducing magnetic fields into a range of biologically relevant signals in vitro and in vivo. These nanotransducers have recently enabled magnetic control of cellular processes, from neuronal firing and gene expression to programmed apoptosis. Effective implementation of magnetically controlled cellular signalling relies on careful tailoring of magnetic nanotransducers and magnetic fields to the responses of the intended molecular targets. This primer discusses the versatility of magnetic modulation modalities and offers practical guidelines for selection of appropriate materials and field parameters, with a particular focus on applications in neuroscience. With recent developments in magnetic instrumentation and nanoparticle chemistries, including those that are commercially available, magnetic approaches promise to empower research aimed at connecting molecular and cellular signalling to physiology and behaviour in untethered moving subjects.

Magnetic-field-synchronized wireless modulation of neural activity by magnetoelectric nanoparticles

The in vitro study demonstrates wirelessly controlled modulation of neural activity using magnetoelectric nanoparticles (MENPs), synchronized to magnetic field application with a sub-25-msec temporal response. Herein, MENPs are sub-30-nm CoFe₂O₄@BaTiO₃ core-shell nanostructures. MENPs were added to E18 rat hippocampal cell cultures (0.5 μg of MENPs per 100,000 neurons) tagged with fluorescent Ca²⁺ sensitive indicator cal520. MENPs were shown to wirelessly induce calcium transients which were synchronized with application of 1200-Oe bipolar 25-msec magnetic pulses at a rate of 20 pulses/sec. The observed calcium transients were similar, in shape and magnitude, to those generated through the control electric field stimulation with a 50-μA current, and they were inhibited by the sodium channel blocker tetrodotoxin. The observed MENP-based magnetic excitation of neural activity is in agreement with the non-linear M - H hysteresis loop of the MENPs, wherein the MENPs' coercivity value sets the threshold for the externally applied magnetic field.

Gut microbiome and Parkinson's disease: Perspective on pathogenesis and treatment

BACKGROUND

Parkinson's disease (PD) is a disease of ⍺-synuclein aggregation-mediated dopaminergic neuronal loss in the substantia nigra pars compacta, which leads to motor and non-motor symptoms. Through the last two decades of research, there has been growing consensus that inflammation-mediated oxidative stress, mitochondrial dysfunction, and cytokine-induced toxicity are mainly involved in neuronal damage and loss associated with PD. However, it remains unclear how these mechanisms relate to sporadic PD, a more common form of PD. Both enteric and central nervous systems have been implicated in the pathogenesis of sporadic PD, thus highlighting the crosstalk between the gut and brain.

AIM

of Review: In this review, we summarize how alterations in the gut microbiome can affect PD pathogenesis. We highlight various mechanisms increasing/decreasing the risk of PD development. Based on the previous supporting evidence, we suggest how early interventions could protect against PD development and how controlling specific factors, including our diet, could modify our perspective on disease mechanisms and therapeutics. We explain the strong relationship between the gut microbiota and the brain in PD subjects, by delineating the multiple mechanisms involved inneuroinflammation and oxidative stress. We conclude that the neurodetrimental effects of western diet (WD) and the neuroprotective effects of Mediterranean diets should be further exploredin humans through clinical trials. Key Scientific Concepts of Review: Alterations in the gut microbiome and associated metabolites may contribute to pathogenesis in PD. In some studies, probiotics have been shown to exert anti-oxidative effects in PD via improved mitochondrial dynamics and homeostasis, thus reducing PD-related consequences. However, there is a significant unmet need for randomized clinical trials to investigate the effectiveness of microbial products, probiotic-based supplementation, and dietary intervention in reversing gut microbial dysbiosis in PD.

Organic Neuroelectronics: From Neural Interfaces to Neuroprosthetics

Requirements and recent advances in research on organic neuroelectronics are outlined herein. Neuroelectronics such as neural interfaces and neuroprosthetics provide a promising approach to diagnose and treat neurological diseases. However, the current neural interfaces are rigid and not biocompatible, so they induce an immune response and deterioration of neural signal transmission. Organic materials are promising candidates for neural interfaces, due to their mechanical softness, excellent electrochemical properties, and biocompatibility. Also, organic nervetronics, which mimics functional properties of the biological nerve system, is being developed to overcome the limitations of the complex and energy-consuming conventional neuroprosthetics that limit long-term implantation and daily-life usage. Examples of organic materials for neural interfaces and neural signal recordings are reviewed, recent advances of organic nervetronics that use organic artificial synapses are highlighted, and then further requirements for neuroprosthetics are discussed. Finally, the future challenges that must be overcome to achieve ideal organic neuroelectronics for next-generation neuroprosthetics are discussed.

Progresses of animal robots: A historical review and perspectiveness

Animal robots have remarkable advantages over traditional mechatronic ones in terms of energy supply, self-orientation, and natural concealment and can provide remarkable theoretical and practical values for scientific investigation, community service, military detection and other fields. Given these features, animal robots have become high-profile research objects and have recently attracted extensive attention. Herein, we have defined animal robots, reviewed the main types of animal robots, and discussed the potential developing directions. We have also detailed the mechanisms underlying the regulation of animal robots and introduced key methods for manipulating them. We have further proposed several application prospects for different types of animal robots. Finally, we have presented research directions for their further improvement.

Distributed interfacing by nanoscale photodiodes enables single-neuron light activation and sensory enhancement in 3D spinal explants

Among emerging technologies developed to interface neuronal signaling, engineering electrodes at the nanoscale would yield more precise biodevices opening to progress in neural circuit investigations and to new therapeutic potential. Despite remarkable progress in miniature electronics for less invasive neurostimulation, most nano-enabled, optically triggered interfaces are demonstrated in cultured cells, which precludes the studies of natural neural circuits. We exploit here free-standing silicon-based nanoscale photodiodes to optically modulate single, identified neurons in mammalian spinal cord explants. With near-infrared light stimulation, we show that activating single excitatory or inhibitory neurons differently affects sensory circuits processing in the dorsal horn. We successfully functionalize nano-photodiodes to target single molecules, such as glutamate AMPA receptor subunits, thus enabling light activation of specific synaptic pathways. We conclude that nano-enabled neural interfaces can modulate selected sensory networks with low invasiveness. The use of nanoscale photodiodes can thus provide original perspective in linking neural activity to specific behavioral outcome.

2D MXene interfaces preserve the basal electrophysiology of targeted neural circuits

Neural interfaces enable the monitoring of the state of the brain and its composite cell networks, as well as stimulate them to treat nervous disorders. In addition to their highly efficient charge transduction and stability during operation, the neural electrodes should avoid altering the physiological properties of targeted neuronal tissues. Two-dimensional (2D) MXene materials integrate the advantages of metallic conductivity, high specific-surface area and surface functionality in aqueous dispersions, showing promising potential in neural interface applications. Here, we apply uncoated Ti₃C₂T_x MXene to interface neuronal development. The impacts of the uncoated Ti₃C₂T_x MXene interface on neuronal development and neuronal microcircuit activity were tested for the first time. Compared to the standard neuronal culture with a poly-L-ornithine coated coverslip, uncoated Ti₃C₂T_x MXene surfaces did not affect the cell morphology, density, neuron ratios, maturation or the compositions of the neuronal network. Moreover, calcium imaging, spontaneous postsynaptic currents (sPSCs) and also miniature postsynaptic currents (mPSCs) were recorded to demonstrate that Ti₃C₂T_x MXene interfaces preserved the basal physiology of neuronal activity. The ability to interface neuronal circuit development without altering neuronal signaling properties enables the construction of MXene-based neural prosthetic devices for neuroscience research, diagnosis, and therapies.

Role of coupling distances in a coupled thalamocortical network for regulation of epilepsy

The recent theoretical modeling of coupled cortical thalamic network is an important advance toward the spatiotemporal dynamics of the brain. However, the diversity of coupling distances is ignored, and the better choice of deep brain stimulation (DBS) parameters to control epilepsy is still a challenge so far. A modeling object of this paper is to establish a coupled cortical thalamic model with uncertain coupling distances including nine combinations. Based on the pathways formed by pyramidal neuronal population (PY), thalamic reticular nucleus (RE) and thalamic relay nucleus (TC), we simulate the spike-wave discharges (SWD) at 2-4Hz which are the main manifestations of absence episodes. It is demonstrated that combination (1/3, 1/9) between the left and right ventricles is the optimal coupling distance of the proposed model by analyzing the percentage of SWD. A stimulating object of this paper is to find an optimum parameter range of DBS. One of the important results is that the number of SWD is inversely proportional to the amplitude, another one is that the number of SWD shows a U-shaped trend with the change of frequency. The present study has laidtheoryfoundationforthebrainplasticity, which will provide an important theoretical basis and direction for the treatment of absence epilepsy in the future. In brief, hopefully our simulation results will provide some help to patients.

Lateralization in hemi-parkinsonian rats is affected by deep brain stimulation or glutamatergic neurotransmission in the inferior colliculus

After unilateral lesion of the medial forebrain bundle (MFB) by 6-OHDA rats exhibit lateralized deficits in spontaneous behavior or apomorphine-induced rotations. We investigated whether such lateralization is attenuated by either deep brain stimulation (DBS) or glutamatergic neurotransmission in the inferior colliculus (IC) of Wistar rats. Intracollicular DBS did not affect spontaneous lateralization but attenuated apomorphine-induced rotations. Spontaneous lateralization disappeared after both glutamatergic antagonist MK-801 or the agonist NMDA microinjected in the IC. Apomorphine-induced rotations were potentiated by MK-801 but were not affected by NMDA intracollicular microinjection. After injecting a bidirectional neural tract tracer into the IC, cell bodies and/or axonal fibers were found in the periaqueductal gray, superior colliculus, substantia nigra, cuneiform nucleus and pedunculo-pontine tegmental nucleus, suggesting the involvement of these structures in the motor improvement after IC manipulation. Importantly, the side of the IC microinjection regarding the lesion (ipsi- or contralateral) is particularly important and this effect may not involve the neostriatum directly.Significance StatementThe inferior colliculus, usually viewed as an auditory structure, when properly manipulated may counteract motor deficits in Parkinsonian rats. Indeed, the present study showed that 30 Hz deep brain stimulation or glutamatergic neural network in the inferior colliculus reduced body asymmetry induced by medial forebrain bundle unilateral 6-OHDA lesion in rats, an animal model of Parkinsonism. Understanding how glutamatergic mechanisms in the inferior colliculus influence motor control, classically attributed to the basal nuclei circuitry, could be useful in the development of new therapeutics to treat Parkinson's disease and other motor disorders.

Developing control-theoretic objectives for large-scale brain dynamics and cognitive enhancement

The development of technologies for brain stimulation provides a means for scientists and clinicians to directly actuate the brain and nervous system. Brain stimulation has shown intriguing potential in terms of modifying particular symptom clusters in patients and behavioral characteristics of subjects. The stage is thus set for optimization of these techniques and the pursuit of more nuanced stimulation objectives, including the modification of complex cognitive functions such as memory and attention. Control theory and engineering will play a key role in the development of these methods, guiding computational and algorithmic strategies for stimulation. In particular, realizing this goal will require new development of frameworks that allow for controlling not only brain activity, but also latent dynamics that underlie neural computation and information processing. In the current opinion, we review recent progress in brain stimulation and outline challenges and potential research pathways associated with exogenous control of cognitive function.

High-precision neural stimulation through optoacoustic emitters

Neuromodulation poses an invaluable role in deciphering neural circuits and exploring clinical treatment of neurological diseases. Optoacoustic neuromodulation is an emerging modality benefiting from the merits of ultrasound with high penetration depth as well as the merits of photons with high spatial precision. We summarize recent development in a variety of optoacoustic platforms for neural modulation, including fiber, film, and nanotransducer-based devices, highlighting the key advantages of each platform. The possible mechanisms and main barriers for optoacoustics as a viable neuromodulation tool are discussed. Future directions in fundamental and translational research are proposed.

Automated Tuning of Closed-loop Neuromodulation Control Systems using Bayesian Optimization

Tuning the parameters of controllers to attain the best performance is a challenging task in designing effective closed-loop neuromodulation systems. In this paper, we present a distributed architecture for automated tuning and adaptation of closed-loop neuromodulation control systems. We use this approach for the automated parameter tuning of a Proportional-Integral (PI) neuromodulation controller using Bayesian optimization. We use a biophysically-grounded mean-field model of neural populations under electrical stimulation as a simulation environment for testing and prototyping the proposed framework and characterizing its performance. Our results demonstrate the feasibility of using Bayesian optimization for performance-based automated tuning of a PI controller in closed-loop set-point neuromodulation control tasks.

Thalamic deep brain stimulation paradigm to reduce consciousness: Cortico-striatal dynamics implicated in mechanisms of consciousness

Anesthetic manipulations provide much-needed causal evidence for neural correlates of consciousness, but non-specific drug effects complicate their interpretation. Evidence suggests that thalamic deep brain stimulation (DBS) can either increase or decrease consciousness, depending on the stimulation target and parameters. The putative role of the central lateral thalamus (CL) in consciousness makes it an ideal DBS target to manipulate circuit-level mechanisms in cortico-striato-thalamic (CST) systems, thereby influencing consciousness and related processes. We used multi-microelectrode DBS targeted to CL in macaques while recording from frontal, parietal, and striatal regions. DBS induced episodes of abnormally long, vacant staring with low-frequency oscillations here termed vacant, perturbed consciousness (VPC). DBS modulated VPC likelihood in a frequency-specific manner. VPC events corresponded to decreases in measures of neural complexity (entropy) and integration (Φ*), proposed indices of consciousness, and substantial changes to communication in CST circuits. During VPC, power spectral density and coherence at low frequencies increased across CST circuits, especially in thalamo-parietal and cortico-striatal pathways. Decreased consciousness and neural integration corresponded to shifts in cortico-striatal network configurations that dissociated parietal and subcortical structures. Overall, the features of VPC and implicated networks were similar to those of absence epilepsy. As this same multi-microelectrode DBS method-but at different stimulation frequencies-can also increase consciousness in anesthetized macaques, it can be used to flexibly address questions of consciousness with limited confounds, as well as inform clinical investigations of other consciousness disorders.

Stimulation-induced changes at the electrode-tissue interface and their influence on deep brain stimulation

OBJECTIVE

During deep brain stimulation (DBS) the electrode-tissue interface forms a critical path between device and brain tissue. Although changes in the electrical double layer and glial scar can impact stimulation efficacy, the effects of chronic DBS on the electrode-tissue interface have not yet been established.

APPROACH

In this study, we characterised the electrode-tissue interface surrounding chronically implanted DBS electrodes in rats and compared the impedance and histological properties at the electrode interface in animals that received daily stimulation and in those where no stimulation was applied, up to eight weeks post-surgery. A computational model was developed based on the experimental data, which allowed the dispersive electrical properties of the surrounding encapsulation tissue to be estimated. The model was then used to study the effect of stimulation-induced changes in the electrode-tissue interface on the electric field and neural activation during voltage- and current-controlled stimulation.

MAIN RESULTS

Incorporating the observed changes in simulations in silico, we estimated the frequency-dependent dielectric properties of the electrical double layer and surrounding encapsulation tissue. Through simulations we show how stimulation-induced changes in the properties of the electrode-tissue interface influence the electric field and alter neural activation during voltage-controlled stimulation. A substantial increase in the number of stimulated collaterals, and their distance from the electrode, was observed during voltage-controlled stimulation with stimulated ETI properties. In vitro examination of stimulated electrodes confirmed that high frequency stimulation leads to desorption of proteins at the electrode interface, with a concomitant reduction in impedance.

SIGNIFICANCE

The demonstration of stimulation-induced changes in the electrode-tissue interface has important implications for future DBS systems including closed-loop systems where the applied stimulation may change over time. Understanding these changes is particularly important for systems incorporating simultaneous stimulation and sensing, which interact dynamically with brain networks.

Model-based quantitative optimization of deep brain stimulation and prediction of parkinson's states

Although the exact etiology of Parkinson's disease (PD) is still unknown, there are a variety of treatments available to alleviate its symptoms according to the development stage of PD. Deep brain stimulation (DBS), the most common surgical treatment for advanced PD, accurately locates and implants stimulating electrodes at specific targets in the brain to deliver high-frequency electrical stimulation that alters the excitability of the corresponding nuclei. However, for different patients and stages of PD development, there exists a choice of the optimal DBS protocol. In this paper, we propose a quantitative method (multi-dimensional feature indexes) to determine the stimulation pattern, stimulation parameters, and target of DBS from the perspective of the network model. On the other hand, based on this method, the development of PD can be predicted so that timely treatment can be given to patients. Simulation results show that, first, different network states can be distinguished by extracting features of the firing activity of neuronal populations within the basal ganglia network system. Secondly, the optimal DBS treatment can be selected by comparing the feature indexes vectors of the pre- and post-state of the network after the action of different modes of DBS. Lastly, the evolution of the network state from normal to pathological is simulated. The critical point of network state transitions is determined. These results provide a quantitative and qualitative method for determining the optimal regimen for DBS for PD, which is helpful for clinical practice.

The Instrumental Activities of Daily Living in Parkinson’s Disease Patients Treated by Subthalamic Deep Brain Stimulation

Background

Everyday functioning and instrumental activities of daily living (IADL) play a vital role in preserving the quality of life in patients with Parkinson’s disease (PD) after deep brain stimulation of the subthalamic nucleus (STN-DBS).

Objective

The main goal of the current study was to examine IADL change in pre-and post-surgery of the STN-DBS. We also analyzed the influence of the levodopa equivalent daily dose (LEDD) and global cognitive performance (Dementia Rating Scale; DRS-2) as covariates in relation to IADL.

Methods

Thirty-two non-demented PD patients were administered before and after STN-DBS neurosurgery the Penn Parkinson’s Daily Activities Questionnaire (PDAQ; self-report), the DRS-2 and Beck Depression Inventory (BDI-II) to assess IADL change, global cognition, and depression.

Results

We found a positive effect of STN-DBS on IADL in the post-surgery phase. Moreover, lower global cognition and lower LEDD are predictive of lower IADL in both pre-surgery and post-surgery examinations.

Summary/Conclusion

STN-DBS in PD is a safe method for improvement of everyday functioning and IADL. In the post-surgery phase, we show a relation of IADL to the severity of cognitive impairment in PD and to LEDD.

Designing and Pilot Testing a Novel Transcranial Temporal Interference Stimulation Device for Neuromodulation

Transcranial temporal interference stimulation (tTIS) has been proposed as a new neuromodulation technology for non-invasive deep-brain stimulation (DBS). However, few studies have detailed the design method of a tTIS device and provided system validation. Thus, a detailed design and validation scheme of a novel tTIS device for animal brain stimulation are presented in this study. In the proposed tTIS device, a direct digital synthesizer (DDS) was used to generate a sine wave potential of different frequencies, which was converted to an adjustable sine wave current. A current transformer was used to produce electrical isolation of different channels, which eliminated the current crosstalk between channels and greatly increased the load capacity by amplifying the output voltage. Several in vitro experiments were first conducted to validate the tTIS device. Our results indicated that the error percentages of the stimulation currents were within ±2%. Current crosstalk between channels was almost completely eliminated. Then, in vivo electric field measurement shows that the 2-pole arrangement may provide better cortical targeting than the 4-pole mode. A pilot animal experiment was conducted in which evoked motion and electromyographic activation of the contralateral forelimb were observed, which indicated that the 2-pole tTIS had successfully activated the primary motor cortex in a rat. Motor activation induced by the 2-pole tTIS demonstrated the feasibility and safety potential when applying our tTIS device for neuromodulation.

Rapid treatments for depression: Endocannabinoid system as a therapeutic target

Current first-line treatments for major depressive disorder (MDD), i.e., antidepressant drugs and psychotherapy, show delayed onset of therapeutic effect as late as 2-3 weeks or more. In the clinic, the speed of beginning of the actions of antidepressant drugs or other interventions is vital for many reasons. Late-onset means that depression, its related disability, and the potential danger of suicide remain a threat for some patients. There are some rapid-acting antidepressant interventions, such as sleep deprivation, ketamine, acute exercise, which induce a significant response, ranging from a few hours to maximally one week, and most of them share a common characteristic that is the activation of the endocannabinoid (eCB) system. Activation of this system, i.e., augmentation of eCB signaling, appears to have anti-depressant-like actions. This article puts the idea forward that the activation of eCB signaling represents a critical mechanism of rapid-acting therapeutic interventions in MDD, and this system might contribute to the development of novel rapid-acting treatments for MDD.

Medical deep learning-A systematic meta-review

Deep learning has remarkably impacted several different scientific disciplines over the last few years. For example, in image processing and analysis, deep learning algorithms were able to outperform other cutting-edge methods. Additionally, deep learning has delivered state-of-the-art results in tasks like autonomous driving, outclassing previous attempts. There are even instances where deep learning outperformed humans, for example with object recognition and gaming. Deep learning is also showing vast potential in the medical domain. With the collection of large quantities of patient records and data, and a trend towards personalized treatments, there is a great need for automated and reliable processing and analysis of health information. Patient data is not only collected in clinical centers, like hospitals and private practices, but also by mobile healthcare apps or online websites. The abundance of collected patient data and the recent growth in the deep learning field has resulted in a large increase in research efforts. In Q2/2020, the search engine PubMed returned already over 11,000 results for the search term 'deep learning', and around 90% of these publications are from the last three years. However, even though PubMed represents the largest search engine in the medical field, it does not cover all medical-related publications. Hence, a complete overview of the field of 'medical deep learning' is almost impossible to obtain and acquiring a full overview of medical sub-fields is becoming increasingly more difficult. Nevertheless, several review and survey articles about medical deep learning have been published within the last few years. They focus, in general, on specific medical scenarios, like the analysis of medical images containing specific pathologies. With these surveys as a foundation, the aim of this article is to provide the first high-level, systematic meta-review of medical deep learning surveys.

Biophysical and Architectural Mechanisms of Subthalamic Theta under Response Conflict

The cortico-basal ganglia circuit is needed to suppress prepotent actions and to facilitate controlled behavior. Under conditions of response conflict, the frontal cortex and subthalamic nucleus (STN) exhibit increased spiking and theta band power, which are linked to adaptive regulation of behavioral output. The electrophysiological mechanisms underlying these neural signatures of impulse control remain poorly understood. To address this lacuna, we constructed a novel large-scale, biophysically principled model of the subthalamopallidal (STN-globus pallidus externus) network and examined the mechanisms that modulate theta power and spiking in response to cortical input. Simulations confirmed that theta power does not emerge from intrinsic network dynamics but is robustly elicited in response to cortical input as burst events representing action selection dynamics. Rhythmic burst events of multiple cortical populations, representing a state of conflict where cortical motor plans vacillate in the theta range, led to prolonged STN theta and increased spiking, consistent with empirical literature. Notably, theta band signaling required NMDA, but not AMPA, currents, which were in turn related to a triphasic STN response characterized by spiking, silence, and bursting periods. Finally, theta band resonance was also strongly modulated by architectural connectivity, with maximal theta arising when multiple cortical populations project to individual STN "conflict detector" units because of an NMDA-dependent supralinear response. Our results provide insights into the biophysical principles and architectural constraints that give rise to STN dynamics during response conflict, and how their disruption can lead to impulsivity and compulsivity.SIGNIFICANCE STATEMENT The subthalamic nucleus exhibits theta band power modulation related to cognitive control over motor actions during conditions of response conflict. However, the mechanisms of such dynamics are not understood. Here we developed a novel biophysically detailed and data-constrained large-scale model of the subthalamopallidal network, and examined the impacts of cellular and network architectural properties that give rise to theta dynamics. Our investigations implicate an important role for NMDA receptors and cortico-subthalamic nucleus topographical connectivities in theta power modulation.

Rationally Navigating Subjective Preferences in Memory Modification

Discussion of the ethics of memory modification technologies (MMTs) has often focused on questions about the limits of their permissibility. In the current paper, I focus primarily on a different issue: when (if ever) is it rational to prefer MMTs to alternative interventions? My conclusion is that these conditions are rare. The reason stems from considerations of autonomy. When compared with other interventions, MMTs do a particularly poor job at promoting the autonomy of their users. If this conclusion is true, moreover, it provides a fresh perspective on debates about the permissibility of MMTs. On the one hand, for those who would limit the use of MMTs to a narrow range of circumstances, the conclusion that MMTs are rarely preferable gives them further reason to eye MMTs with suspicion. On the other hand, for those who view MMTs as permissible in a wide range of circumstances, the conclusion may deflate their endorsement.