Mapping dysfunctional circuits in the frontal cortex using deep brain stimulation.

Frontal circuits play a critical role in motor, cognitive and affective processing, and their dysfunction may result in a variety of brain disorders. However, exactly which frontal domains mediate which (dys)functions remains largely elusive. We studied 534 deep brain stimulation electrodes implanted to treat four different brain disorders. By analyzing which connections were modulated for optimal therapeutic response across these disorders, we segregated the frontal cortex into circuits that had become dysfunctional in each of them. Dysfunctional circuits were topographically arranged from occipital to frontal, ranging from interconnections with sensorimotor cortices in dystonia, the primary motor cortex in Tourette's syndrome, the supplementary motor area in Parkinson's disease, to ventromedial prefrontal and anterior cingulate cortices in obsessive-compulsive disorder. Our findings highlight the integration of deep brain stimulation with brain connectomics as a powerful tool to explore couplings between brain structure and functional impairments in the human brain.

Biophysical Principles and Computational Modeling of Deep Brain Stimulation.

BACKGROUND: Deep brain stimulation (DBS) has revolutionized the treatment of neurological disorders, yet the mechanisms of DBS are still under investigation. Computational models are important in silico tools for elucidating these underlying principles and potentially for personalizing DBS therapy to individual patients. The basic principles underlying neurostimulation computational models, however, are not well known in the clinical neuromodulation community. OBJECTIVE: In this study, we present a tutorial on the derivation of computational models of DBS and outline the biophysical contributions of electrodes, stimulation parameters, and tissue substrates to the effects of DBS. RESULTS: Given that many aspects of DBS are difficult to characterize experimentally, computational models have played an important role in understanding how material, size, shape, and contact segmentation influence device biocompatibility, energy efficiency, the spatial spread of the electric field, and the specificity of neural activation. Neural activation is dictated by stimulation parameters including frequency, current vs voltage control, amplitude, pulse width, polarity configurations, and waveform. These parameters also affect the potential for tissue damage, energy efficiency, the spatial spread of the electric field, and the specificity of neural activation. Activation of the neural substrate also is influenced by the encapsulation layer surrounding the electrode, the conductivity of the surrounding tissue, and the size and orientation of white matter fibers. These properties modulate the effects of the electric field and determine the ultimate therapeutic response. CONCLUSION: This article describes biophysical principles that are useful for understanding the mechanisms of neurostimulation.

Spatiotemporally-specific cortical-subthalamic coupling differentiates aspects of speech performance.

Speech provides a rich context for exploring human cortical-basal ganglia circuit function, but direct intracranial recordings are rare. We recorded electrocorticographic signals in the cortex synchronously with single units in the subthalamic nucleus (STN), a basal ganglia node that receives direct input from widespread cortical regions, while participants performed a syllable repetition task during deep brain stimulation (DBS) surgery. We discovered that STN neurons exhibited spike-phase coupling (SPC) events with distinct combinations of frequency, location, and timing that indexed specific aspects of speech. The strength of SPC to posterior perisylvian cortex predicted phoneme production accuracy, while that of SPC to perirolandic cortex predicted time taken for articulation Thus, STN-cortical interactions are coordinated via transient bursts of behavior-specific synchronization that involves multiple neuronal populations and timescales. These results both suggest mechanisms that support auditory-sensorimotor integration during speech and explain why firing-rate based models are insufficient for explaining basal ganglia circuit behavior.

Neurophysiological underpinnings of an intensive protocol for upper limb motor recovery in subacute and chronic stroke patients.

BACKGROUND: Upper limb (UL) motor impairment following stroke is a leading cause of functional limitations in activities of daily living. Robot-assisted therapy supports rehabilitation, but how its efficacy and the underlying neural mechanisms depend on the time after stroke is yet to be assessed. AIM: We investigated the response to an intensive protocol of robot-assisted rehabilitation in sub-acute and chronic stroke patients, by analyzing the underlying changes in clinical scores, electroencephalography (EEG) and end-effector kinematics. We aimed at identifying neural correlates of the participants' upper limb motor function recovery, following an intensive 2-week rehabilitation protocol. DESIGN: Prospective cohort study. SETTING: Inpatients and outpatients from the Neurorehabilitation Unit of Pisa University Hospital, Italy. POPULATION: Sub-acute and chronic stroke survivors. METHODS: Thirty-one stroke survivors (14 sub-acute, 17 chronic) with mild-to-moderate UL paresis were enrolled. All participants underwent ten rehabilitative sessions of task-oriented exercises with a planar end-effector robotic device. All patients were evaluated with the Fugl-Meyer Assessment Scale and the Wolf Motor Function Test, at recruitment (T0), end-of-treatment (T1), and one-month follow-up (T2). Along with clinical scales, kinematic parameters and quantitative EEG were collected for each patient. Kinematics metrics were related to velocity, acceleration and smoothness of the movement. Relative power in four frequency bands was extracted from the EEG signals. The evolution over time of kinematic and EEG features was analyzed, in correlation with motor recovery. RESULTS: Both groups displayed significant gains in motility after treatment. Sub-acute patients displayed more pronounced clinical improvements, significant changes in kinematic parameters, and a larger increase in Beta-band in the motor area of the affected hemisphere. In both groups these improvements were associated to a decrease in the Delta-band of both hemispheres. Improvements were retained at T2. CONCLUSIONS: The intensive two-week rehabilitation protocol was effective in both chronic and sub-acute patients, and improvements in the two groups shared similar dynamics. However, stronger cortical and behavioral changes were observed in sub-acute patients suggesting different reorganizational patterns. CLINICAL REHABILITATION IMPACT: This study paves the way to personalized approaches to UL motor rehabilitation after stroke, as highlighted by different neurophysiological modifications following recovery in subacute and chronic stroke patients.

Initial case series of a novel sensing deep brain stimulation device in drug-resistant epilepsy and consistent identification of alpha/beta oscillatory activity: A feasibility study.

OBJECTIVE: Here, we report a retrospective, single-center experience with a novel deep brain stimulation (DBS) device capable of chronic local field potential (LFP) recording in drug-resistant epilepsy (DRE) and explore potential electrophysiological biomarkers that may aid DBS programming and outcome tracking. METHODS: Five patients with DRE underwent thalamic DBS, targeting either the bilateral anterior (n = 3) or centromedian (n = 2) nuclei. Postoperative electrode lead localizations were visualized in Lead-DBS software. Local field potentials recorded over 12-18 months were tracked, and changes in power were associated with patient events, medication changes, and stimulation. We utilized a combination of lead localization, in-clinic broadband LFP recordings, real-time LFP response to stimulation, and chronic recordings to guide DBS programming. RESULTS: Four patients (80%) experienced a >50% reduction in seizure frequency, whereas one patient had no significant reduction. Peaks in the alpha and/or beta frequency range were observed in the thalamic LFPs of each patient. Stimulation suppressed these LFP peaks in a dose-dependent manner. Chronic timeline data identified changes in LFP amplitude associated with stimulation, seizure occurrences, and medication changes. We also noticed a circadian pattern of LFP amplitudes in all patients. Button-presses during seizure events via a mobile application served as a digital seizure diary and were associated with elevations in LFP power. SIGNIFICANCE: We describe an initial cohort of patients with DRE utilizing a novel sensing DBS device to characterize potential LFP biomarkers of epilepsy that may be associated with seizure control after DBS in DRE. We also present a new workflow utilizing the Percept device that may optimize DBS programming using real-time and chronic LFP recording.

A supervised data-driven spatial filter denoising method for speech artifacts in intracranial electrophysiological recordings.

Neurosurgical procedures that enable direct brain recordings in awake patients offer unique opportunities to explore the neurophysiology of human speech. The scarcity of these opportunities and the altruism of participating patients compel us to apply the highest rigor to signal analysis. Intracranial electroencephalography (iEEG) signals recorded during overt speech can contain a speech artifact that tracks the fundamental frequency (F0) of the participant's voice, involving the same high-gamma frequencies that are modulated during speech production and perception. To address this artifact, we developed a spatial-filtering approach to identify and remove acoustic-induced contaminations of the recorded signal. We found that traditional reference schemes jeopardized signal quality, whereas our data-driven method denoised the recordings while preserving underlying neural activity.

Mapping Dysfunctional Circuits in the Frontal Cortex Using Deep Brain Stimulation.

Frontal circuits play a critical role in motor, cognitive, and affective processing - and their dysfunction may result in a variety of brain disorders. However, exactly which frontal domains mediate which (dys)function remains largely elusive. Here, we study 534 deep brain stimulation electrodes implanted to treat four different brain disorders. By analyzing which connections were modulated for optimal therapeutic response across these disorders, we segregate the frontal cortex into circuits that became dysfunctional in each of them. Dysfunctional circuits were topographically arranged from occipital to rostral, ranging from interconnections with sensorimotor cortices in dystonia, with the primary motor cortex in Tourette's syndrome, the supplementary motor area in Parkinson's disease, to ventromedial prefrontal and anterior cingulate cortices in obsessive-compulsive disorder. Our findings highlight the integration of deep brain stimulation with brain connectomics as a powerful tool to explore couplings between brain structure and functional impairment in the human brain.

Lead-DBS v3.0: Mapping deep brain stimulation effects to local anatomy and global networks.

Following its introduction in 2014 and with support of a broad international community, the open-source toolbox Lead-DBS has evolved into a comprehensive neuroimaging platform dedicated to localizing, reconstructing, and visualizing electrodes implanted in the human brain, in the context of deep brain stimulation (DBS) and epilepsy monitoring. Expanding clinical indications for DBS, increasing availability of related research tools, and a growing community of clinician-scientist researchers, however, have led to an ongoing need to maintain, update, and standardize the codebase of Lead-DBS. Major development efforts of the platform in recent years have now yielded an end-to-end solution for DBS-based neuroimaging analysis allowing comprehensive image preprocessing, lead localization, stimulation volume modeling, and statistical analysis within a single tool. The aim of the present manuscript is to introduce fundamental additions to the Lead-DBS pipeline including a deformation warpfield editor and novel algorithms for electrode localization. Furthermore, we introduce a total of three comprehensive tools to map DBS effects to local, tract- and brain network-levels. These updates are demonstrated using a single patient example (for subject-level analysis), as well as a retrospective cohort of 51 Parkinson's disease patients who underwent DBS of the subthalamic nucleus (for group-level analysis). Their applicability is further demonstrated by comparing the various methodological choices and the amount of explained variance in clinical outcomes across analysis streams. Finally, based on an increasing need to standardize folder and file naming specifications across research groups in neuroscience, we introduce the brain imaging data structure (BIDS) derivative standard for Lead-DBS. Thus, this multi-institutional collaborative effort represents an important stage in the evolution of a comprehensive, open-source pipeline for DBS imaging and connectomics.

Disruption of layer-specific visual processing in a model of focal neocortical epilepsy.

The epileptic brain is the result of a sequence of events transforming normal neuronal populations into hyperexcitable networks supporting recurrent seizure generation. These modifications are known to induce fundamental alterations of circuit function and, ultimately, of behavior. However, how hyperexcitability affects information processing in cortical sensory circuits is not yet fully understood. Here, we investigated interlaminar alterations in sensory processing of the visual cortex in a mouse model of focal epilepsy. We found three main circuit dynamics alterations in epileptic mice: (i) a spreading of visual contrast-driven gamma modulation across layers, (ii) an increase in firing rate that is layer-unspecific for excitatory units and localized in infragranular layers for inhibitory neurons, and (iii) a strong and contrast-dependent locking of firing units to network activity. Altogether, our data show that epileptic circuits display a functional disruption of layer-specific organization of visual sensory processing, which could account for visual dysfunction observed in epileptic subjects. Understanding these mechanisms paves the way to circuital therapeutic interventions for epilepsy.

Body and peripersonal space representations in chronic stroke patients with upper limb motor deficits.

The continuous stream of multisensory information between the brain and the body during body-environment interactions is crucial to maintain the updated representation of the perceived dimensions of body parts (metric body representation) and the space around the body (the peripersonal space). Such flow of multisensory signals is often limited by upper limb sensorimotor deficits after stroke. This would suggest the presence of systematic distortions of metric body representation and peripersonal space in chronic patients with persistent sensorimotor deficits. We assessed metric body representation and peripersonal space representation in 60 chronic stroke patients with unilateral upper limb motor deficits, in comparison with age-matched healthy controls. We also administered a questionnaire capturing explicit feelings towards the affected limb. These novel measures were analysed with respect to patients' clinical profiles and brain lesions to investigate the neural and functional origin of putative deficits. Stroke patients showed distortions in metric body representation of the affected limb, characterized by an underestimation of the arm length and an alteration of the arm global shape. A descriptive lesion analysis (subtraction analysis) suggests that these distortions may be more frequently associated with lesions involving the superior corona radiata and the superior frontal gyrus. Peripersonal space representation was also altered, with reduced multisensory facilitation for stimuli presented around the affected limb. These deficits were more common in patients reporting pain during motion. Explorative lesion analyses (subtraction analysis, disconnection maps) suggest that the peripersonal space distortions would be more frequently associated with lesions involving the parietal operculum and white matter frontoparietal connections. Moreover, patients reported altered feelings towards the affected limb, which were associated with right brain damage, proprioceptive deficits and a lower cognitive profile. These results reveal implicit and explicit distortions involving metric body representation, peripersonal space representation and the perception of the affected limb in chronic stroke patients. These findings might have important clinical implications for the longitudinal monitoring and the treatments of often-neglected deficits in body perception and representation.

Brain-inspired meta-reinforcement learning cognitive control in conflictual inhibition decision-making task for artificial agents.

Conflictual cues and unexpected changes in human real-case scenarios may be detrimental to the execution of tasks by artificial agents, thus affecting their performance. Meta-learning applied to reinforcement learning may enhance the design of control algorithms, where an outer learning system progressively adjusts the operation of an inner learning system, leading to practical benefits for the learning schema. Here, we developed a brain-inspired meta-learning framework for inhibition cognitive control that i) exploits the meta-learning principles in the neuromodulation theory proposed by Doya, ii) relies on a well-established neural architecture that contains distributed learning systems in the human brain, and iii) proposes optimization rules of meta-learning hyperparameters that mimic the dynamics of the major neurotransmitters in the brain. We tested an artificial agent in inhibiting the action command in two well-known tasks described in the literature: NoGo and Stop-Signal Paradigms. After a short learning phase, the artificial agent learned to react to the hold signal, and hence to successfully inhibit the motor command in both tasks, via the continuous adjustment of the learning hyperparameters. We found a significant increase in global accuracy, right inhibition, and a reduction in the latency time required to cancel the action process, i.e., the Stop-signal reaction time. We also performed a sensitivity analysis to evaluate the behavioral effects of the meta-parameters, focusing on the serotoninergic modulation of the dopamine release. We demonstrated that brain-inspired principles can be integrated into artificial agents to achieve more flexible behavior when conflictual inhibitory signals are present in the environment.

Impulsivity is associated with firing regularity in parkinsonian ventral subthalamic nucleus.

Impulsive-compulsive behaviors (ICB) are over-represented in Parkinson's disease (PD) patients. Neurons in the ventral subthalamic nucleus (STN) might play a predominant role in the modulation of impulsivity. We characterized the firing regularity of 742 subthalamic neurons from 24 PD patients (12 ICB+ and 12 ICB-) in an OFF medication state. We computed the firing regularity in the dorsal and ventral STN regions, and we compared their performance in discriminating ICB patients. Regularity of ventral neurons in ICB+ patients is higher and supports a significant discrimination between the two cohorts. These results substantiate a ventral location of neurons involved in impulsivity.

Impaired reach-to-grasp kinematics in parkinsonian patients relates to dopamine-dependent, subthalamic beta bursts.

Excessive beta-band oscillations in the subthalamic nucleus are key neural features of Parkinson's disease. Yet the distinctive contributions of beta low and high bands, their dependency on striatal dopamine, and their correlates with movement kinematics are unclear. Here, we show that the movement phases of the reach-to-grasp motor task are coded by the subthalamic bursting activity in a maximally-informative beta high range. A strong, three-fold correlation linked beta high range bursts, imbalanced inter-hemispheric striatal dopaminergic tone, and impaired inter-joint movement coordination. These results provide new insight into the neural correlates of motor control in parkinsonian patients, paving the way for more informative use of beta-band features for adaptive deep brain stimulation devices.

Impulsivity Markers in Parkinsonian Subthalamic Single-Unit Activity.

BACKGROUND: Impulsive-compulsive behaviors are common in Parkinson's disease (PD) patients. However, the basal ganglia dysfunctions associated with high impulsivity have not been fully characterized. The objective of this study was to identify the features associated with impulsive-compulsive behaviors in single neurons of the subthalamic nucleus (STN). METHODS: We compared temporal and spectral features of 412 subthalamic neurons from 12 PD patients with impulsive-compulsive behaviors and 330 neurons from 12 PD patients without. Single-unit activities were extracted from exploratory microrecordings performed during deep brain stimulation (DBS) implant surgery in an OFF medication state. RESULTS: Patients with impulsive-compulsive behaviors displayed decreased firing frequency during bursts and a larger fraction of tonic neurons combined with weaker beta coherence. Information carried by these features led to the identification of patients with impulsive-compulsive behaviors with an accuracy greater than 80%. CONCLUSIONS: Impulsive-compulsive behaviors in PD patients are associated with decreased bursts in STN neurons in the OFF medication state. © 2021 International Parkinson and Movement Disorder Society.

Deep brain stimulation: a review of the open neural engineering challenges.

OBJECTIVE: Deep brain stimulation (DBS) is an established and valid therapy for a variety of pathological conditions ranging from motor to cognitive disorders. Still, much of the DBS-related mechanism of action is far from being understood, and there are several side effects of DBS whose origin is unclear. In the last years DBS limitations have been tackled by a variety of approaches, including adaptive deep brain stimulation (aDBS), a technique that relies on using chronically implanted electrodes on 'sensing mode' to detect the neural markers of specific motor symptoms and to deliver on-demand or modulate the stimulation parameters accordingly. Here we will review the state of the art of the several approaches to improve DBS and summarize the main challenges toward the development of an effective aDBS therapy. APPROACH: We discuss models of basal ganglia disorders pathogenesis, hardware and software improvements for conventional DBS, and candidate neural and non-neural features and related control strategies for aDBS. MAIN RESULTS: We identify then the main operative challenges toward optimal DBS such as (i) accurate target localization, (ii) increased spatial resolution of stimulation, (iii) development of in silico tests for DBS, (iv) identification of specific motor symptoms biomarkers, in particular (v) assessing how LFP oscillations relate to behavioral disfunctions, and (vi) clarify how stimulation affects the cortico-basal-ganglia-thalamic network to (vii) design optimal stimulation patterns. SIGNIFICANCE: This roadmap will lead neural engineers novel to the field toward the most relevant open issues of DBS, while the in-depth readers might find a careful comparison of advantages and drawbacks of the most recent attempts to improve DBS-related neuromodulatory strategies.

Gait Initiation in Parkinson's Disease: Impact of Dopamine Depletion and Initial Stance Condition.

Postural instability, in particular at gait initiation (GI), and resulting falls are a major determinant of poor quality of life in subjects with Parkinson's disease (PD). Still, the contribution of the basal ganglia and dopamine on the feedforward postural control associated with this motor task is poorly known. In addition, the influence of anthropometric measures (AM) and initial stance condition on GI has never been consistently assessed. The biomechanical resultants of anticipatory postural adjustments contributing to GI [imbalance (IMB), unloading (UNL), and stepping phase) were studied in 26 unmedicated subjects with idiopathic PD and in 27 healthy subjects. A subset of 13 patients was analyzed under standardized medication conditions and the striatal dopaminergic innervation was studied in 22 patients using FP-CIT and SPECT. People with PD showed a significant reduction in center of pressure (CoP) displacement and velocity during the IMB phase, reduced first step length and velocity, and decreased velocity and acceleration of the center of mass (CoM) at toe off of the stance foot. All these measurements correlated with the dopaminergic innervation of the putamen and substantially improved with levodopa. These results were not influenced by anthropometric parameters or by the initial stance condition. In contrast, most of the measurements of the UNL phase were influenced by the foot placement and did not correlate with putaminal dopaminergic innervation. Our results suggest a significant role of dopamine and the putamen particularly in the elaboration of the IMB phase of anticipatory postural adjustments and in the execution of the first step. The basal ganglia circuitry may contribute to defining the optimal referent body configuration for a proper initiation of gait and possibly gait adaptation to the environment.

Spatio-temporal structure of single neuron subthalamic activity identifies DBS target for anesthetized Tourette syndrome patients.

OBJECTIVE: Deep brain stimulation (DBS) of basal ganglia effectively tackles motor symptoms of movement disorders such as Tourette syndrome (TS). The precise location of target stimulation site determines the range of clinical outcome in DBS patients, and the occurrence of side-effects of DBS. DBS implant procedures currently localize stimulation target relying on a combination of pre-surgical imaging, standardized brain atlases and on-the-spot clinical tests. Here we show that temporal structure of single unit activity in subthalamic nucleus (STN) of patients affected by pure TS can contribute to identify the optimal target location of DBS. APPROACH: Neural activity was recorded at different depths within STN with microelectrodes during DBS implant surgery. Depth specific neural features were extracted and correlated with the optimal depth for tic control. MAIN RESULTS: We describe for the first time temporal spike patterns of single neurons from sensorimotor STN of anesthetized TS patients. A large fraction of units (31.2%) displayed intense bursting in the delta band (<4 Hz). The highest firing irregularity and hence the higher density of bursting units (42%) were found at the optimal spot for tic control. Discharge patterns irregularity and dominant oscillations frequency (but not firing rate) carried significant information on optimal target. SIGNIFICANCE: We found single unit activity features in the STN of TS patients reliably associated to optimal DBS target site for tic control. In future works measures of firing irregularity could be integrated with current target localization methods leading to a more effective and safer DBS for TS patients.

Hand perceptions induced by single pulse transcranial magnetic stimulation over the primary motor cortex.

BACKGROUND: When single pulse transcranial magnetic stimulation (TMS) is applied over the primary motor cortex (M1) with sufficient intensity, it evokes muscular contractions (motor-evoked potentials, MEPs) and muscle twitches (TMS-evoked movements). Participants may also report various hand sensations related to TMS, but the perception elicited by TMS and its relationship to MEPs and evoked movements has not been systematically studied. OBJECTIVE: The main aim of this work is to evaluate participants' kinesthetic and somatosensory hand perceptions elicited by single-pulse TMS over M1-hand area at different intensities of stimulation and their relation with MEPs and TMS-evoked movements. METHODS: We compared the number of MEPs (measured by electromyography), TMS-evoked movements (measured by an accelerometer) and participants' hand perception (measured by verbal report) elicited by TMS at different intensity of stimulation. This way, we estimated the amplitude of MEPs and the acceleration of TMS-evoked movements sufficient to trigger TMS evoked hand perceptions. RESULTS: We found that TMS-evoked hand perceptions are induced at 105% of the individual resting motor threshold, a value significantly different from the threshold inducing MEPs (about 100%) and TMS-evoked movements (about 110%). Our data indicate that only MEPs with an amplitude higher than 0.62 mV and TMS-evoked movements with acceleration higher than 0.42 m/s2 were associated with hand perceptions at threshold. CONCLUSIONS: Our data reveal the main features of TMS-evoked hand perception and show that in addition to MEPs and TMS-evoked movements, this is a separate discernible response associated to single-pulse TMS over M1.