Synaptic network structure shapes cortically evoked spatio-temporal responses of STN and GPe neurons in a computational model.

Introduction: The basal ganglia (BG) are involved in motor control and play an essential role in movement disorders such as hemiballismus, dystonia, and Parkinson's disease. Neurons in the motor part of the BG respond to passive movement or stimulation of different body parts and to stimulation of corresponding cortical regions. Experimental evidence suggests that the BG are organized somatotopically, i.e., specific areas of the body are associated with specific regions in the BG nuclei. Signals related to the same body part that propagate along different pathways converge onto the same BG neurons, leading to characteristic shapes of cortically evoked responses. This suggests the existence of functional channels that allow for the processing of different motor commands or information related to different body parts in parallel. Neurological disorders such as Parkinson's disease are associated with pathological activity in the BG and impaired synaptic connectivity, together with reorganization of somatotopic maps. One hypothesis is that motor symptoms are, at least partly, caused by an impairment of network structure perturbing the organization of functional channels. Methods: We developed a computational model of the STN-GPe circuit, a central part of the BG. By removing individual synaptic connections, we analyzed the contribution of signals propagating along different pathways to cortically evoked responses. We studied how evoked responses are affected by systematic changes in the network structure. To quantify the BG's organization in the form of functional channels, we suggested a two-site stimulation protocol. Results: Our model reproduced the cortically evoked responses of STN and GPe neurons and the contributions of different pathways suggested by experimental studies. Cortical stimulation evokes spatio-temporal response patterns that are linked to the underlying synaptic network structure. Our two-site stimulation protocol yielded an approximate functional channel width. Discussion/conclusion: The presented results provide insight into the organization of BG synaptic connectivity, which is important for the development of computational models. The synaptic network structure strongly affects the processing of cortical signals and may impact the generation of pathological rhythms. Our work may motivate further experiments to analyze the network structure of BG nuclei and their organization in functional channels.

Shuffling Improves the Acute and Carryover Effect of Subthalamic Coordinated Reset Deep Brain Stimulation.

Coordinated reset deep brain stimulation (CR DBS) in the subthalamic nucleus (STN) has been demonstrated effective for the treatment of the motor signs associated with Parkinson's disease (PD). A critical CR parameter is an order in which stimulation is delivered across contacts. The relative effect of alternating vs. not alternating this order, i.e., shuffling vs. non-shuffling, however, has not been evaluated in vivo. The objective of this study is to compare the effect of shuffled vs. non-shuffled STN CR DBS on Parkinsonian motor signs. Two Parkinsonian non-human primates were implanted with a DBS lead in the STN. The effects of STN CR DBS with and without shuffling were compared with the traditional isochronal DBS (tDBS) using a within-subject design. For each stimulation setting, DBS was delivered for 2 or 4 h/day for 5 consecutive days. The severity of PD was assessed using a modified clinical rating scale immediately before, during, and 1 h after DBS, as well as on days following the discontinuation of the 5 days of daily stimulation, i.e., carryover effect. Shuffled STN CR DBS produced greater acute and carryover improvements on Parkinsonian motor signs compared with non-shuffled CR. Moreover, this difference was more pronounced when more effective stimulation intensity and burst frequency settings were used. tDBS showed limited carryover effects. Given the significant effect of shuffling on the effectiveness of CR DBS, it will be critical for future studies to further define the relative role of different CR parameters for the clinical implementation of this novel stimulation paradigm.

Proceedings of the Ninth Annual Deep Brain Stimulation Think Tank: Advances in Cutting Edge Technologies, Artificial Intelligence, Neuromodulation, Neuroethics, Pain, Interventional Psychiatry, Epilepsy, and Traumatic Brain Injury.

DBS Think Tank IX was held on August 25-27, 2021 in Orlando FL with US based participants largely in person and overseas participants joining by video conferencing technology. The DBS Think Tank was founded in 2012 and provides an open platform where clinicians, engineers and researchers (from industry and academia) can freely discuss current and emerging deep brain stimulation (DBS) technologies as well as the logistical and ethical issues facing the field. The consensus among the DBS Think Tank IX speakers was that DBS expanded in its scope and has been applied to multiple brain disorders in an effort to modulate neural circuitry. After collectively sharing our experiences, it was estimated that globally more than 230,000 DBS devices have been implanted for neurological and neuropsychiatric disorders. As such, this year's meeting was focused on advances in the following areas: neuromodulation in Europe, Asia and Australia; cutting-edge technologies, neuroethics, interventional psychiatry, adaptive DBS, neuromodulation for pain, network neuromodulation for epilepsy and neuromodulation for traumatic brain injury.

Periodic flashing coordinated reset stimulation paradigm reduces sensitivity to ON and OFF period durations.

Pathological synchronization in the basal ganglia network has been considered an important component of Parkinson's disease pathophysiology. An established treatment for some patients with Parkinson's disease is deep brain stimulation, in which a tonic high-frequency pulse train is delivered to target regions of the brain. In recent years, a novel neuromodulation paradigm called coordinated reset stimulation has been proposed, which aims to reverse the pathological synchrony by sequentially delivering short high-frequency bursts to distinct sub-regions of the pathologically synchronized network, with an average intra-burst interval for each sub-region corresponding to period of the pathological oscillation. It has further been proposed that the resultant desynchronization can be enhanced when stimulation is interrupted periodically, and that it is particularly beneficial to precisely tune the stimulation ON and OFF time-windows to the underlying pathological frequency. Pre-clinical and clinical studies of coordinated reset stimulation have relied on these proposals for their stimulation protocols. In this study, we present a modified ON-OFF coordinated reset stimulation paradigm called periodic flashing and study its behavior through computational modeling using the Kuramoto coupled phase oscillator model. We demonstrate that in contrast to conventional coordinated reset stimulation, the periodic flashing variation does not exhibit a need for precise turning of the ON-OFF periods to the pathological frequency, and demonstrates desynchronization for a wide range of ON and OFF periods. We provide a mechanistic explanation for the previously observed sensitivities and demonstrate that they are an artifact of the specific ON-OFF cycling paradigm used. As a practical consequence, the periodic flashing paradigm simplifies the tuning of optimal stimulation parameters by decreasing the dimension of the search space. It also suggests new, more flexible ways of delivering coordinated reset stimulation.

Model-Based Deep Brain Stimulation Programming for Parkinson's Disease: The GUIDE Pilot Study.

BACKGROUND: Achieving optimal results following deep brain stimulation (DBS) typically involves several months of programming sessions. The Graphical User Interface for DBS Evaluation (GUIDE) study explored whether a visual programming system could help clinicians accurately predetermine ideal stimulation settings in DBS patients with Parkinson's disease. METHODS: A multicenter prospective, observational study was designed that utilized a blinded Unified Parkinson's Disease Rating Scale (UPDRS)-III examination to prospectively assess whether DBS settings derived using a neuroanatomically based computer model (Model) could provide comparable efficacy to those determined through traditional, monopolar review-based programming (Clinical). We retrospectively compared the neuroanatomical regions of stimulation, power consumption and time spent on programming using both methods. RESULTS: The average improvement in UPDRS-III scores was 10.4 ± 7.8 for the Model settings and 11.7 ± 8.7 for the Clinical settings. The difference between the mean UPDRS-III scores with the Model versus the Clinical settings was 0.26 and not statistically significant (p = 0.9866). Power consumption for the Model settings was 48.7 ± 22 µW versus 76.1 ± 46.5 µW for the Clinical settings. The mean time spent programming using the Model approach was 31 ± 16 s versus 41.4 ± 29.1 min using the Clinical approach. CONCLUSION: The Model-based DBS settings provided similar benefit to the Clinical settings based on UPDRS-III scores and were often arrived at in less time and required less power than the Clinical settings.

Cell-type-based model explaining coexpression patterns of genes in the brain.

Spatial patterns of gene expression in the vertebrate brain are not independent, as pairs of genes can exhibit complex patterns of coexpression. Two genes may be similarly expressed in one region, but differentially expressed in other regions. These correlations have been studied quantitatively, particularly for the Allen Atlas of the adult mouse brain, but their biological meaning remains obscure. We propose a simple model of the coexpression patterns in terms of spatial distributions of underlying cell types and establish its plausibility using independently measured cell-type-specific transcriptomes. The model allows us to predict the spatial distribution of cell types in the mouse brain.

Chronux: a platform for analyzing neural signals.

Chronux is an open-source software package developed for the analysis of neural data. The current version of Chronux includes software for signal processing of neural time-series data including several specialized mini-packages for spike-sorting, local regression, audio segmentation, and other data-analysis tasks typically encountered by a neuroscientist. Chronux is freely available along with user tutorials, sample data, and extensive documentation from http://chronux.org/.

Characterization of trial-to-trial fluctuations in local field potentials recorded in cerebral cortex of awake behaving macaque.

In analyzing neurophysiologic data, individual experimental trials are usually assumed to be statistically independent. However, many studies employing functional imaging and electrophysiology have shown that brain activity during behavioral tasks includes temporally correlated trial-to-trial fluctuations. This could lead to spurious results in statistical significance tests used to compare data from different interleaved behavioral conditions presented throughout an experiment. We characterize trial-to-trial fluctuations in local field potentials recorded from the frontal cortex of a macaque monkey performing an oculomotor delayed response task. Our analysis identifies slow fluctuations (<0.1 Hz) of spectral power in 22/27 recording sessions. These trial-to-trial fluctuations are non-Gaussian, and call into question the statistical utility of standard trial shuffling. We compare our results with evidence for slow fluctuations in human functional imaging studies and other electrophysiologic studies in nonhuman primates.

Clustering of spatial gene expression patterns in the mouse brain and comparison with classical neuroanatomy.

Spatial gene expression profiles provide a novel means of exploring the structural organization of the brain. Computational analysis of these patterns is made possible by genome-scale mapping of the C57BL/6J mouse brain in the Allen Brain Atlas. Here we describe methodology used to explore the spatial structure of gene expression patterns across a set of 3041 genes chosen on the basis of consistency across experimental observations (N=2). The analysis was performed on smoothed, co-registered 3D expression volumes for each gene obtained by aggregating cellular resolution image data. Following dimensionality and noise reduction, voxels were clustered according to similarity of expression across the gene set. We illustrate the resulting parcellations of the mouse brain for different numbers of clusters (K) and quantitatively compare these parcellations with a classically-defined anatomical reference atlas at different levels of granularity, revealing a high degree of correspondence. These observations suggest that spatial localization of gene expression offers substantial promise in connecting knowledge at the molecular level with higher-level information about brain organization.

The brain atlas concordance problem: quantitative comparison of anatomical parcellations.

Many neuroscientific reports reference discrete macro-anatomical regions of the brain which were delineated according to a brain atlas or parcellation protocol. Currently, however, no widely accepted standards exist for partitioning the cortex and subcortical structures, or for assigning labels to the resulting regions, and many procedures are being actively used. Previous attempts to reconcile neuroanatomical nomenclatures have been largely qualitative, focusing on the development of thesauri or simple semantic mappings between terms. Here we take a fundamentally different approach, discounting the names of regions and instead comparing their definitions as spatial entities in an effort to provide more precise quantitative mappings between anatomical entities as defined by different atlases. We develop an analytical framework for studying this brain atlas concordance problem, and apply these methods in a comparison of eight diverse labeling methods used by the neuroimaging community. These analyses result in conditional probabilities that enable mapping between regions across atlases, which also form the input to graph-based methods for extracting higher-order relationships between sets of regions and to procedures for assessing the global similarity between different parcellations of the same brain. At a global scale, the overall results demonstrate a considerable lack of concordance between available parcellation schemes, falling within chance levels for some atlas pairs. At a finer level, this study reveals spatial relationships between sets of defined regions that are not obviously apparent; these are of high potential interest to researchers faced with the challenge of comparing results that were based on these different anatomical models, particularly when coordinate-based data are not available. The complexity of the spatial overlap patterns revealed points to problems for attempts to reconcile anatomical parcellations and nomenclatures using strictly qualitative and/or categorical methods. Detailed results from this study are made available via an interactive web site at http://obart.info.

A proposal for a coordinated effort for the determination of brainwide neuroanatomical connectivity in model organisms at a mesoscopic scale.

In this era of complete genomes, our knowledge of neuroanatomical circuitry remains surprisingly sparse. Such knowledge is critical, however, for both basic and clinical research into brain function. Here we advocate for a concerted effort to fill this gap, through systematic, experimental mapping of neural circuits at a mesoscopic scale of resolution suitable for comprehensive, brainwide coverage, using injections of tracers or viral vectors. We detail the scientific and medical rationale and briefly review existing knowledge and experimental techniques. We define a set of desiderata, including brainwide coverage; validated and extensible experimental techniques suitable for standardization and automation; centralized, open-access data repository; compatibility with existing resources; and tractability with current informatics technology. We discuss a hypothetical but tractable plan for mouse, additional efforts for the macaque, and technique development for human. We estimate that the mouse connectivity project could be completed within five years with a comparatively modest budget.

An anatomic gene expression atlas of the adult mouse brain.

Studying gene expression provides a powerful means of understanding structure-function relationships in the nervous system. The availability of genome-scale in situ hybridization datasets enables new possibilities for understanding brain organization based on gene expression patterns. The Anatomic Gene Expression Atlas (AGEA) is a new relational atlas revealing the genetic architecture of the adult C57Bl/6J mouse brain based on spatial correlations across expression data for thousands of genes in the Allen Brain Atlas (ABA). The AGEA includes three discovery tools for examining neuroanatomical relationships and boundaries: (1) three-dimensional expression-based correlation maps, (2) a hierarchical transcriptome-based parcellation of the brain and (3) a facility to retrieve from the ABA specific genes showing enriched expression in local correlated domains. The utility of this atlas is illustrated by analysis of genetic organization in the thalamus, striatum and cerebral cortex. The AGEA is a publicly accessible online computational tool integrated with the ABA (http://mouse.brain-map.org/agea).

Comparing spectra and coherences for groups of unequal size.

Spectra and coherences are standard measures of association within and between time series. These measures have several advantages over their time-domain counterparts, not the least of which is the ability to derive and estimate confidence intervals. However, comparing spectra and coherences between two groups of observation is a problem that has not received much attention. This problem is important in neuroscience since it is often of great interest to determine whether the estimates differ between distinct experimental/behavioral conditions. Here we propose one approach to this problem. Based on the known distributional properties of spectral and coherence estimates, we derive a test for equality of two spectral or coherence estimates. The test is applicable to unequal sample sizes. We also derive jackknifed estimates of the variance of the proposed test statistic. We suggest that comparing the estimates obtained from the jackknife procedure with the theoretical estimates provides a robust means of determining whether the data in question shows non-Gaussian or non-stationary behavior. Finally, we present applications of the method to simulated and real data.

A method for detection and classification of events in neural activity.

We present a method for the real time prediction of punctuate events in neural activity, based on the time-frequency spectrum of the signal, applicable both to continuous processes like local field potentials (LFPs) as well as to spike trains. We test it on recordings of LFP and spiking activity acquired previously from the lateral intraparietal area (LIP) of macaque monkeys performing a memory-saccade task. In contrast to earlier work, where trials with known start times were classified, our method detects and classifies trials directly from the data. It provides a means to quantitatively compare and contrast the content of LFP signals and spike trains: we find that the detector performance based on the LFP matches the performance based on spike rates. The method should find application in the development of neural prosthetics based on the LFP signal. Our approach uses a new feature vector, which we call the 2d cepstrum.

Dynamic phenotypes: time series analysis techniques for characterizing neuronal and behavioral dynamics.

We consider quantitative measures of behavioral and neuronal dynamics as a means of characterizing phenotypes. Such measures are important from a scientific perspective; because understanding brain function is contingent on understanding the link between the dynamics of the nervous system and behavioral dynamics. They are also important from a biomedical perspective because they provide a contrast to purely psychological characterizations of phenotype or characterizations via static brain images or maps, and are a potential means for differential diagnoses of neuropsychiatric illnesses. After a brief presentation of background work and some current advances, we suggest that more attention needs to be paid to dynamic characterizations of phenotypes. We will discuss some of the relevant time series analysis tools.