Early attentional processing and cortical remapping strategies of tactile stimuli in adults with an early and late-onset visual impairment: A cross-sectional study.

Neuroplastic changes appear in people with visual impairment (VI) and they show greater tactile abilities. Improvements in performance could be associated with the development of enhanced early attentional processes based on neuroplasticity. Currently, the various early attentional and cortical remapping strategies that are utilized by people with early (EB) and late-onset blindness (LB) remain unclear. Thus, more research is required to develop effective rehabilitation programs and substitution devices. Our objective was to explore the differences in spatial tactile brain processing in adults with EB, LB and a sighted control group (CG). In this cross-sectional study 27 participants with VI were categorized into EB (n = 14) and LB (n = 13) groups. They were then compared with a CG (n = 15). A vibrotactile device and event-related potentials (ERPs) were utilized while participants performed a spatial tactile line recognition task. The P100 latency and cortical areas of maximal activity were analyzed during the task. The three groups had no statistical differences in P100 latency (p>0.05). All subjects showed significant activation in the right superior frontal areas. Only individuals with VI activated the left superior frontal regions. In EB subjects, a higher activation was found in the mid-frontal and occipital areas. A higher activation of the mid-frontal, anterior cingulate cortex and orbitofrontal zones was observed in LB participants. Compared to the CG, LB individuals showed greater activity in the left orbitofrontal zone, while EB exhibited greater activity in the right superior parietal cortex. The EB had greater activity in the left orbitofrontal region compared to the LB. People with VI may not have faster early attentional processing. EB subjects activate the occipital lobe and right superior parietal cortex during tactile stimulation because of an early lack of visual stimuli and a multimodal information processing. In individuals with LB and EB the orbitofrontal area is activated, suggesting greater emotional processing.

Heritability of functional gradients in the human subcortico-cortical connectivity.

The human subcortex plays a pivotal role in cognition and is widely implicated in the pathophysiology of many psychiatric disorders. However, the heritability of functional gradients based on subcortico-cortical functional connectivity remains elusive. Here, leveraging twin functional MRI (fMRI) data from both the Human Connectome Project (n = 1023) and the Adolescent Brain Cognitive Development study (n = 936) datasets, we construct large-scale subcortical functional gradients and delineate an increased principal functional gradient pattern from unimodal sensory/motor networks to transmodal association networks. We observed that this principal functional gradient is heritable, and the strength of heritability exhibits a heterogeneous pattern along a hierarchical unimodal-transmodal axis in subcortex for both young adults and children. Furthermore, employing a machine learning framework, we show that this heterogeneous pattern of the principal functional gradient in subcortex can accurately discern the relationship between monozygotic twin pairs and dizygotic twin pairs with an accuracy of 76.2% (P < 0.001). The heritability of functional gradients is associated with the anatomical myelin proxied by MRI-derived T1-weighted/T2-weighted (T1w/T2w) ratio mapping in subcortex. This study provides new insights into the biological basis of subcortical functional hierarchy by revealing the structural and genetic properties of the subcortical functional gradients.

Co-existence of synaptic plasticity and metastable dynamics in a spiking model of cortical circuits.

Evidence for metastable dynamics and its role in brain function is emerging at a fast pace and is changing our understanding of neural coding by putting an emphasis on hidden states of transient activity. Clustered networks of spiking neurons have enhanced synaptic connections among groups of neurons forming structures called cell assemblies; such networks are capable of producing metastable dynamics that is in agreement with many experimental results. However, it is unclear how a clustered network structure producing metastable dynamics may emerge from a fully local plasticity rule, i.e., a plasticity rule where each synapse has only access to the activity of the neurons it connects (as opposed to the activity of other neurons or other synapses). Here, we propose a local plasticity rule producing ongoing metastable dynamics in a deterministic, recurrent network of spiking neurons. The metastable dynamics co-exists with ongoing plasticity and is the consequence of a self-tuning mechanism that keeps the synaptic weights close to the instability line where memories are spontaneously reactivated. In turn, the synaptic structure is stable to ongoing dynamics and random perturbations, yet it remains sufficiently plastic to remap sensory representations to encode new sets of stimuli. Both the plasticity rule and the metastable dynamics scale well with network size, with synaptic stability increasing with the number of neurons. Overall, our results show that it is possible to generate metastable dynamics over meaningful hidden states using a simple but biologically plausible plasticity rule which co-exists with ongoing neural dynamics.

Differential cortical layer engagement during seizure initiation and spread in humans.

Despite decades of research, we still do not understand how spontaneous human seizures start and spread - especially at the level of neuronal microcircuits. In this study, we used laminar arrays of micro-electrodes to simultaneously record the local field potentials and multi-unit neural activities across the six layers of the neocortex during focal seizures in humans. We found that, within the ictal onset zone, the discharges generated during a seizure consisted of current sinks and sources only within the infra-granular and granular layers. Outside of the seizure onset zone, ictal discharges reflected current flow in the supra-granular layers. Interestingly, these patterns of current flow evolved during the course of the seizure - especially outside the seizure onset zone where superficial sinks and sources extended into the deeper layers. Based on these observations, a framework describing cortical-cortical dynamics of seizures is proposed with implications for seizure localization, surgical targeting, and neuromodulation techniques to block the generation and propagation of seizures.

The primate cortical LFP exhibits multiple spectral and temporal gradients and widespread task dependence during visual short-term memory.

Although cognitive functions are hypothesized to be mediated by synchronous neuronal interactions in multiple frequency bands among widely distributed cortical areas, we still lack a basic understanding of the distribution and task dependence of oscillatory activity across the cortical map. Here, we ask how the spectral and temporal properties of the local field potential (LFP) vary across the primate cerebral cortex, and how they are modulated during visual short-term memory. We measured the LFP from 55 cortical areas in two macaque monkeys while they performed a visual delayed match to sample task. Analysis of peak frequencies in the LFP power spectra reveals multiple discrete frequency bands between 3 and 80 Hz that differ between the two monkeys. The LFP power in each band, as well as the sample entropy, a measure of signal complexity, display distinct spatial gradients across the cortex, some of which correlate with reported spine counts in cortical pyramidal neurons. Cortical areas can be robustly decoded using a small number of spectral and temporal parameters, and significant task-dependent increases and decreases in spectral power occur in all cortical areas. These findings reveal pronounced, widespread, and spatially organized gradients in the spectral and temporal activity of cortical areas. Task-dependent changes in cortical activity are globally distributed, even for a simple cognitive task.NEW & NOTEWORTHY We recorded extracellular electrophysiological signals from roughly the breadth and depth of a cortical hemisphere in nonhuman primates (NHPs) performing a visual memory task. Analyses of the band-limited local field potential (LFP) power displayed widespread, frequency-dependent cortical gradients in spectral power. Using a machine learning classifier, these features allowed robust cortical area decoding. Further task dependence in LFP power were found to be widespread, indicating large-scale gradients of LFP activity, and task-related activity.

A Reconsideration of the Core and Matrix Classification of Thalamocortical Projections.

In 1998, Jones suggested a classification of thalamocortical projections into core and matrix divisions (Jones, 1998). In this classification, core projections are specific, topographical, innervate middle cortical layers, and serve to transmit specific information to the cortex for further analysis; matrix projections, in contrast, are diffuse, much less topographic, innervate upper layers, especially Layer 1, and serve a more global, modulatory function, such as affecting levels of arousal. This classification has proven especially influential in studies of thalamocortical relationships. Whereas it may be the case that a clear subset of thalamocortical connections fit the core motif, since they are specific, topographic, and innervate middle layers, we argue that there is no clear evidence for any single class that encompasses the remainder of thalamocortical connections as is claimed for matrix. Instead, there is great morphological variation in connections made by thalamocortical projections fitting neither a core nor matrix classification. We thus conclude that the core/matrix classification should be abandoned, because its application is not helpful in providing insights into thalamocortical interactions and can even be misleading. As one example of the latter, recent suggestions indicate that core projections are equivalent to first-order thalamic relays (i.e., those that relay subcortical information to the cortex) and matrix to higher-order relays (i.e., those that relay information from one cortical area to another), but available evidence does not support this relationship. All of this points to a need to replace the core/matrix grouping with a more complete classification of thalamocortical projections.

Cortical nitric oxide required for presynaptic long-term potentiation in the insular cortex.

Nitric oxide (NO) is a key diffusible messenger in the mammalian brain. It has been proposed that NO may diffuse retrogradely into presynaptic terminals, contributing to the induction of hippocampal long-term potentiation (LTP). Here, we present novel evidence that NO is required for kainate receptor (KAR)-dependent presynaptic form of LTP (pre-LTP) in the adult insular cortex (IC). In the IC, we found that inhibition of NO synthase erased the maintenance of pre-LTP, while the induction of pre-LTP required the activation of KAR. Furthermore, NO is essential for pre-LTP induced between two pyramidal cells in the IC using the double patch-clamp recording. These results suggest that NO is required for homosynaptic pre-LTP in the IC. Our results present strong evidence for the critical roles of NO in pre-LTP in the IC. This article is part of a discussion meeting issue 'Long-term potentiation: 50 years on'.

Reconfigurations of cortical manifold structure during reward-based motor learning.

Adaptive motor behavior depends on the coordinated activity of multiple neural systems distributed across the brain. While the role of sensorimotor cortex in motor learning has been well established, how higher-order brain systems interact with sensorimotor cortex to guide learning is less well understood. Using functional MRI, we examined human brain activity during a reward-based motor task where subjects learned to shape their hand trajectories through reinforcement feedback. We projected patterns of cortical and striatal functional connectivity onto a low-dimensional manifold space and examined how regions expanded and contracted along the manifold during learning. During early learning, we found that several sensorimotor areas in the dorsal attention network exhibited increased covariance with areas of the salience/ventral attention network and reduced covariance with areas of the default mode network (DMN). During late learning, these effects reversed, with sensorimotor areas now exhibiting increased covariance with DMN areas. However, areas in posteromedial cortex showed the opposite pattern across learning phases, with its connectivity suggesting a role in coordinating activity across different networks over time. Our results establish the neural changes that support reward-based motor learning and identify distinct transitions in the functional coupling of sensorimotor to transmodal cortex when adapting behavior.

EEG Activity during Pursuit and Saccade Visual Strategies to Predict the Arrival Position of a Target.

BACKGROUND: In this study, we used electroencephalogram (EEG) to investigate the activity pattern of the cerebral cortex related to visual pursuit and saccade strategies to predict the arrival position of a visual target. In addition, we clarified the differences in the EEG of those who could predict the arrival position well using the saccade strategy compared to those who were not proficient. METHODS: Sixteen participants performed two tasks: the "Pursuit Strategy Task (PST)" and the "Saccade Strategy Task (SST)" while undergoing EEG. For the PST, the participants were instructed to follow the target with their eyes throughout its trajectory and indicate when it reached the final point. For the SST, the participants were instructed to shift their gaze to the end point of arrival once they had predicted it. RESULTS: Low beta EEG activity at the Oz, Cz, and CP2 electrodes was significantly higher during the SST than during the PST. In addition, low beta EEG activity at P7 electrode was significantly higher in the group showing a small position error (PE) than in the group showing a large PE at response. CONCLUSIONS: EEG activity at the Oz, Cz, and CP2 electrodes during the SST may reflect visuospatial attention to the moving target, the tracking of moving targets, and the focus on the final destination position. In addition, EEG activity at P7 electrode may more accurately detect the speed and direction of the moving target by the small PE group at response.

Utilizing Phase Locking Value to Determine Neurofeedback Treatment Responsiveness in Attention Deficit Hyperactivity Disorder.

BACKGROUND: Neurofeedback is a non-invasive brain training technique used to enhance and treat hyperactivity disorder by altering the patterns of brain activity. Nonetheless, the extent of enhancement by neurofeedback varies among individuals/patients and many of them are irresponsive to this treatment technique. Therefore, several studies have been conducted to predict the effectiveness of neurofeedback training including the theta/beta protocol with a specific emphasize on slow cortical potential (SCP) before initiating treatment, as well as examining SCP criteria according to age and sex criteria in diverse populations. While some of these studies failed to make accurate predictions, others have demonstrated low success rates. This study explores functional connections within various brain lobes across different frequency bands of electroencephalogram (EEG) signals and the value of phase locking is used to predict the potential effectiveness of neurofeedback treatment before its initiation. METHODS: This study utilized EEG data from the Mendelian database. In this database, EEG signals were recorded during neurofeedback sessions involving 60 hyperactive students aged 7-14 years, irrespective of sex. These students were categorized into treatable and non-treatable. The proposed method includes a five-step algorithm. Initially, the data underwent preprocessing to reduce noise using a multi-stage filtering process. The second step involved extracting alpha and beta frequency bands from the preprocessed EEG signals, with a particular emphasis on the EEG recorded from sessions 10 to 20 of neurofeedback therapy. In the third step, the method assessed the disparity in brain signals between the two groups by evaluating functional relationships in different brain lobes using the phase lock value, a crucial data characteristic. The fourth step focused on reducing the feature space and identifying the most effective and optimal electrodes for neurofeedback treatment. Two methods, the probability index (p-value) via a t-test and the genetic algorithm, were employed. These methods showed that the optimal electrodes were in the frontal lobe and central cerebral cortex, notably channels C3, FZ, F4, CZ, C4, and F3, as they exhibited significant differences between the two groups. Finally, in the fifth step, machine learning classifiers were applied, and the results were combined to generate treatable and non-treatable labels for each dataset. RESULTS: Among the classifiers, the support vector machine and the boosting method demonstrated the highest accuracy when combined. Consequently, the proposed algorithm successfully predicted the treatability of individuals with hyperactivity in a short time and with limited data, achieving an accuracy of 90.6% in the neurofeedback method. Additionally, it effectively identified key electrodes in neurofeedback treatment, reducing their number from 32 to 6. CONCLUSIONS: This study introduces an algorithm with a 90.6% accuracy for predicting neurofeedback treatment outcomes in hyperactivity disorder, significantly enhancing treatment efficiency by identifying optimal electrodes and reducing their number from 32 to 6. The proposed method enables the prediction of patient responsiveness to neurofeedback therapy without the need for numerous sessions, thus conserving time and financial resources.

The Granular Retrosplenial Cortex Is Necessary in Male Rats for Object-Location Associative Learning and Memory, But Not Spatial Working Memory or Visual Discrimination and Reversal, in the Touchscreen Operant Chamber.

The retrosplenial cortex (RSC) is a hub of diverse afferent and efferent projections thought to be involved in associative learning. RSC shows early pathology in mild cognitive impairment and Alzheimer's disease (AD), which impairs associative learning. To understand and develop therapies for diseases such as AD, animal models are essential. Given the importance of human RSC in object-location associative learning and the success of object-location associative paradigms in human studies and in the clinic, it would be of considerable value to establish a translational model of object-location learning for the rodent. For this reason, we sought to test the role of RSC in object-location learning in male rats using the object-location paired-associates learning (PAL) touchscreen task. First, increased cFos immunoreactivity was observed in granular RSC following PAL training when compared with extended pretraining controls. Following this, RSC lesions following PAL acquisition were used to explore the necessity of the RSC in object-location associative learning and memory and two tasks involving only one modality: trial-unique nonmatching-to-location for spatial working memory and pairwise visual discrimination/reversal. RSC lesions impaired both memory for learned paired-associates and learning of new object-location associations but did not affect performance in either the spatial or visual single-modality tasks. These findings provide evidence that RSC is necessary for object-location learning and less so for learning and memory involving the individual modalities therein.

Causal evidence for cholinergic stabilization of attractor landscape dynamics.

There is substantial evidence that neuromodulatory systems critically influence brain state dynamics; however, most work has been purely descriptive. Here, we quantify, using data combining local inactivation of the basal forebrain with simultaneous measurement of resting-state fMRI activity in the macaque, the causal role of long-range cholinergic input to the stabilization of brain states in the cerebral cortex. Local inactivation of the nucleus basalis of Meynert (nbM) leads to a decrease in the energy barriers required for an fMRI state transition in cortical ongoing activity. Moreover, the inactivation of particular nbM sub-regions predominantly affects information transfer in cortical regions known to receive direct anatomical projections. We demonstrate these results in a simple neurodynamical model of cholinergic impact on neuronal firing rates and slow hyperpolarizing adaptation currents. We conclude that the cholinergic system plays a critical role in stabilizing macroscale brain state dynamics.

Simultaneous cortical, subcortical, and brainstem mapping of sensory activation.

Nonpainful tactile sensory stimuli are processed in the cortex, subcortex, and brainstem. Recent functional magnetic resonance imaging studies have highlighted the value of whole-brain, systems-level investigation for examining sensory processing. However, whole-brain functional magnetic resonance imaging studies are uncommon, in part due to challenges with signal to noise when studying the brainstem. Furthermore, differentiation of small sensory brainstem structures such as the cuneate and gracile nuclei necessitates high-resolution imaging. To address this gap in systems-level sensory investigation, we employed a whole-brain, multi-echo functional magnetic resonance imaging acquisition at 3T with multi-echo independent component analysis denoising and brainstem-specific modeling to enable detection of activation across the entire sensory system. In healthy participants, we examined patterns of activity in response to nonpainful brushing of the right hand, left hand, and right foot (n = 10 per location), and found the expected lateralization, with distinct cortical and subcortical responses for upper and lower limb stimulation. At the brainstem level, we differentiated the adjacent cuneate and gracile nuclei, corresponding to hand and foot stimulation respectively. Our findings demonstrate that simultaneous cortical, subcortical, and brainstem mapping at 3T could be a key tool to understand the sensory system in both healthy individuals and clinical cohorts with sensory deficits.

The cortical representation of transitivity: Insights from tractography-based inhibitory nTMS.

Navigated Transcranial Magnetic Stimulation (nTMS) is commonly used to causally identify cortical regions involved in language processing. Combining tractography with nTMS has been shown to increase induced error rates by targeting stimulation of cortical terminations of white matter fibers. According to functional Magnetic Resonance Imaging (fMRI) data, bilateral cortical areas connected by the arcuate fasciculus (AF) have been implicated in the processing of transitive compared to unergative verbs. To test this connection between transitivity and bilateral perisylvian regions, we administered a tractography-based inhibitory nTMS protocol during action naming of finite transitive (The man reads) and unergative (The man sails) verbs. After tracking the left and right AF, we stimulated the cortical terminations of the tract in frontal, parietal and temporal regions in 20 neurologically healthy native speakers of German. Results revealed that nTMS induced more errors during transitive compared to unergative verb naming when stimulating the left (vs right) AF terminations. This effect was specific to the left temporal terminations of the AF, whereas no differences between the two verb types were identified when stimulating inferior parietal and frontal AF terminations. Induced errors for transitive verbs over left temporal terminations mostly manifested as access errors (i.e., hesitations). Given the inhibitory nature of our nTMS protocol, these results suggest that temporal regions of the left hemisphere play a crucial role in argument structure processing. Our findings align with previous data on the role of left posterior temporal regions in language processing and by providing further evidence from a language production experiment using tractography-based inhibitory nTMS.

Efficient models of cortical activity via local dynamic equilibria and coarse-grained interactions.

Biologically detailed models of brain circuitry are challenging to build and simulate due to the large number of neurons, their complex interactions, and the many unknown physiological parameters. Simplified mathematical models are more tractable, but harder to evaluate when too far removed from neuroanatomy/physiology. We propose that a multiscale model, coarse-grained (CG) while preserving local biological details, offers the best balance between biological realism and computability. This paper presents such a model. Generally, CG models focus on the interaction between groups of neurons-here termed "pixels"-rather than individual cells. In our case, dynamics are alternately updated at intra- and interpixel scales, with one informing the other, until convergence to equilibrium is achieved on both scales. An innovation is how we exploit the underlying biology: Taking advantage of the similarity in local anatomical structures across large regions of the cortex, we model intrapixel dynamics as a single dynamical system driven by "external" inputs. These inputs vary with events external to the pixel, but their ranges can be estimated a priori. Precomputing and tabulating all potential local responses speed up the updating procedure significantly compared to direct multiscale simulation. We illustrate our methodology using a model of the primate visual cortex. Except for local neuron-to-neuron variability (necessarily lost in any CG approximation) our model reproduces various features of large-scale network models at a tiny fraction of the computational cost. These include neuronal responses as a consequence of their orientation selectivity, a primary function of visual neurons.

Effects of peer victimization on cortical processing of social-evaluative stress in patients with major depressive disorder.

Peer victimization contributes to the development of major depressive disorders (MDDs). While previous studies reported differentiated peripheral physiological responses in peer-victimized individuals with depression, little is known about potential alterations of cortical event-related potentials (ERPs) in response to social stimuli in depressive patients with a history of peer victimization. Using a social condition paradigm, the present study examined whether peer victimization alters conditioned cortical responses to potentially threatening social stimuli in MDD patients and healthy controls. In the task, we studied ERPs to conditioned stimuli (CSs), i.e. still images of faces, that were coupled to unconditioned socially negative and neutral evaluative video statements. Peer victimization was related to more pronounced P100 amplitudes in reaction to negative and neutral CSs. Attenuated P200 amplitudes in peer-victimized individuals were found in response to negative CSs. Cortical responses to CSs were not influenced by a diagnosis of MDD. The results suggest altered responsiveness to interpersonal information in peer-victimized individuals. Facilitated early processing of social threat indicators may prevent peer-victimized individuals from adaptive responses to social cues, increasing their vulnerability for depression.

Intracortical mechanisms of single pulse electrical stimulation (SPES) evoked excitations and inhibitions in humans.

Cortico-cortical evoked potentials (CCEPs) elicited by single-pulse electric stimulation (SPES) are widely used to assess effective connectivity between cortical areas and are also implemented in the presurgical evaluation of epileptic patients. Nevertheless, the cortical generators underlying the various components of CCEPs in humans have not yet been elucidated. Our aim was to describe the laminar pattern arising under SPES evoked CCEP components (P1, N1, P2, N2, P3) and to evaluate the similarities between N2 and the downstate of sleep slow waves. We used intra-cortical laminar microelectrodes (LMEs) to record CCEPs evoked by 10 mA bipolar 0.5 Hz electric pulses in seven patients with medically intractable epilepsy implanted with subdural grids. Based on the laminar profile of CCEPs, the latency of components is not layer-dependent, however their rate of appearance varies across cortical depth and stimulation distance, while the seizure onset zone does not seem to affect the emergence of components. Early neural excitation primarily engages middle and deep layers, propagating to the superficial layers, followed by mainly superficial inhibition, concluding in a sleep slow wave-like inhibition and excitation sequence.

Population-level normative models reveal race- and socioeconomic-related variability in cortical thickness of threat neurocircuitry.

The inequitable distribution of economic resources and exposure to adversity between racial groups contributes to mental health disparities within the United States. Consideration of the potential neurodevelopmental consequences, however, has been limited particularly for neurocircuitry known to regulate the emotional response to threat. Characterizing the consequences of inequity on threat neurocircuitry is critical for robust and generalizable neurobiological models of psychiatric illness. Here we use data from the Adolescent Brain and Cognitive Development Study 4.0 release to investigate the contributions of individual and neighborhood-level economic resources and exposure to discrimination. We investigate the potential appearance of race-related differences using both standard methods and through population-level normative modeling. We show that, in a sample of white and Black adolescents, racial inequities in socioeconomic factors largely contribute to the appearance of race-related differences in cortical thickness of threat neurocircuitry. The race-related differences are preserved through the use of population-level models and such models also preserve associations between cortical thickness and specific socioeconomic factors. The present findings highlight that such socioeconomic inequities largely underlie race-related differences in brain morphology. The present findings provide important new insight for the generation of generalizable neurobiological models of psychiatric illness.

Robust adaptive deep brain stimulation control of in-silico non-stationary Parkinsonian neural oscillatory dynamics.

Objective. Closed-loop deep brain stimulation (DBS) is a promising therapy for Parkinson's disease (PD) that works by adjusting DBS patterns in real time from the guidance of feedback neural activity. Current closed-loop DBS mainly uses threshold-crossing on-off controllers or linear time-invariant (LTI) controllers to regulate the basal ganglia (BG) Parkinsonian beta band oscillation power. However, the critical cortex-BG-thalamus network dynamics underlying PD are nonlinear, non-stationary, and noisy, hindering accurate and robust control of Parkinsonian neural oscillatory dynamics.Approach. Here, we develop a new robust adaptive closed-loop DBS method for regulating the Parkinsonian beta oscillatory dynamics of the cortex-BG-thalamus network. We first build an adaptive state-space model to quantify the dynamic, nonlinear, and non-stationary neural activity. We then construct an adaptive estimator to track the nonlinearity and non-stationarity in real time. We next design a robust controller to automatically determine the DBS frequency based on the estimated Parkinsonian neural state while reducing the system's sensitivity to high-frequency noise. We adopt and tune a biophysical cortex-BG-thalamus network model as an in-silico simulation testbed to generate nonlinear and non-stationary Parkinsonian neural dynamics for evaluating DBS methods.Main results. We find that under different nonlinear and non-stationary neural dynamics, our robust adaptive DBS method achieved accurate regulation of the BG Parkinsonian beta band oscillation power with small control error, bias, and deviation. Moreover, the accurate regulation generalizes across different therapeutic targets and consistently outperforms current on-off and LTI DBS methods.Significance. These results have implications for future designs of closed-loop DBS systems to treat PD.