Aperiodic components of local field potentials reflect inherent differences between cortical and subcortical activity.

Information flow in brain networks is reflected in local field potentials that have both periodic and aperiodic components. The 1/fχ aperiodic component of the power spectra tracks arousal and correlates with other physiological and pathophysiological states. Here we explored the aperiodic activity in the human thalamus and basal ganglia in relation to simultaneously recorded cortical activity. We elaborated on the parameterization of the aperiodic component implemented by specparam (formerly known as FOOOF) to avoid parameter unidentifiability and to obtain independent and more easily interpretable parameters. This allowed us to seamlessly fit spectra with and without an aperiodic knee, a parameter that captures a change in the slope of the aperiodic component. We found that the cortical aperiodic exponent χ, which reflects the decay of the aperiodic component with frequency, is correlated with Parkinson's disease symptom severity. Interestingly, no aperiodic knee was detected from the thalamus, the pallidum, or the subthalamic nucleus, which exhibited an aperiodic exponent significantly lower than in cortex. These differences were replicated in epilepsy patients undergoing intracranial monitoring that included thalamic recordings. The consistently lower aperiodic exponent and lack of an aperiodic knee from all subcortical recordings may reflect cytoarchitectonic and/or functional differences. SIGNIFICANCE STATEMENT: The aperiodic component of local field potentials can be modeled to produce useful and reproducible indices of neural activity. Here we refined a widely used phenomenological model for extracting aperiodic parameters (namely the exponent, offset and knee), with which we fit cortical, basal ganglia, and thalamic intracranial local field potentials, recorded from unique cohorts of movement disorders and epilepsy patients. We found that the aperiodic exponent in motor cortex is higher in Parkinson's disease patients with more severe motor symptoms, suggesting that aperiodic features may have potential as electrophysiological biomarkers for movement disorders symptoms. Remarkably, we found conspicuous differences in the aperiodic parameters of basal ganglia and thalamic signals compared to those from neocortex.

Protocol for FRAP-based estimation of nuclear import and export rates in single yeast cells.

Here, we present a protocol for estimating nuclear transport parameters in single cells. We describe steps for performing four consecutive fluorescence recovery after photobleaching experiments, fitting the obtained data to an ordinary differential equations model, and statistical analysis of the fittings using a specialized R package. This protocol permits the estimation of import and export rates, nuclear or cytosolic fixed fractions, and total number of molecules. For complete details on the use and execution of this protocol, please refer to Durrieu et al.1.

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.

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.

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

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

Lexicality-Modulated Influence of Auditory Cortex on Subthalamic Nucleus During Motor Planning for Speech.

Speech requires successful information transfer within cortical-basal ganglia loop circuits to produce the desired acoustic output. For this reason, up to 90% of Parkinson's disease patients experience impairments of speech articulation. Deep brain stimulation (DBS) is highly effective in controlling the symptoms of Parkinson's disease, sometimes alongside speech improvement, but subthalamic nucleus (STN) DBS can also lead to decreases in semantic and phonological fluency. This paradox demands better understanding of the interactions between the cortical speech network and the STN, which can be investigated with intracranial EEG recordings collected during DBS implantation surgery. We analyzed the propagation of high-gamma activity between STN, superior temporal gyrus (STG), and ventral sensorimotor cortices during reading aloud via event-related causality, a method that estimates strengths and directionalities of neural activity propagation. We employed a newly developed bivariate smoothing model based on a two-dimensional moving average, which is optimal for reducing random noise while retaining a sharp step response, to ensure precise embedding of statistical significance in the time-frequency space. Sustained and reciprocal neural interactions between STN and ventral sensorimotor cortex were observed. Moreover, high-gamma activity propagated from the STG to the STN prior to speech onset. The strength of this influence was affected by the lexical status of the utterance, with increased activity propagation during word versus pseudoword reading. These unique data suggest a potential role for the STN in the feedforward control of speech.

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.

Broadband aperiodic components of local field potentials reflect inherent differences between cortical and subcortical activity.

Information flow in brain networks is reflected in intracerebral local field potential (LFP) measurements that have both periodic and aperiodic components. The 1/fχ broadband aperiodic component of the power spectra has been shown to track arousal level and to correlate with other physiological and pathophysiological states, with consistent patterns across cortical regions. Previous studies have focused almost exclusively on cortical neurophysiology. Here we explored the aperiodic activity of subcortical nuclei from the human thalamus and basal ganglia, in relation to simultaneously recorded cortical activity. We elaborated on the FOOOF (fitting of one over f) method by creating a new parameterization of the aperiodic component with independent and more easily interpretable parameters, which allows seamlessly fitting spectra with and without an aperiodic knee, a component of the signal that reflects the dominant timescale of aperiodic fluctuations. First, we found that the aperiodic exponent from sensorimotor cortex in Parkinson's disease (PD) patients correlated with disease severity. Second, although the aperiodic knee frequency changed across cortical regions as previously reported, no aperiodic knee was detected from subcortical regions across movement disorders patients, including the ventral thalamus (VIM), globus pallidus internus (GPi) and subthalamic nucleus (STN). All subcortical region studied exhibited a relatively low aperiodic exponent (χSTN=1.3±0.2, χVIM=1.4±0.1, χGPi =1.4±0.1) that differed markedly from cortical values (χCortex=3.2±0.4, fkCortex=17±5 Hz). These differences were replicated in a second dataset from epilepsy patients undergoing intracranial monitoring that included thalamic recordings. The consistently lower aperiodic exponent and lack of an aperiodic knee from all subcortical recordings may reflect cytoarchitectonic and/or functional differences between subcortical nuclei and the cortex.

Characterization of cell-to-cell variation in nuclear transport rates and identification of its sources.

Nuclear transport is an essential part of eukaryotic cell function. Here, we present scFRAP, a model-assisted fluorescent recovery after photobleaching (FRAP)- based method to determine nuclear import and export rates independently in individual live cells. To overcome the inherent noise of single-cell measurements, we performed sequential FRAPs on the same cell. We found large cell-to-cell variation in transport rates within isogenic yeast populations. For passive transport, the variability in NPC number might explain most of the variability. Using this approach, we studied mother-daughter cell asymmetry in the active nuclear shuttling of the transcription factor Ace2, which is specifically concentrated in daughter cell nuclei in early G1. Rather than reduced export in the daughter cell, as previously hypothesized, we found that this asymmetry is mainly due to an increased import in daughters. These results shed light on cell-to-cell variation in cellular dynamics and its sources.

Accurate Deep Brain Stimulation Lead Placement Concurrent With Research Electrocorticography.

BACKGROUND: Using electrocorticography for research (R-ECoG) during deep brain stimulation (DBS) surgery has advanced our understanding of human cortical-basal ganglia neurophysiology and mechanisms of therapeutic circuit modulation. The safety of R-ECoG has been established, but potential effects of temporary ECoG strip placement on targeting accuracy have not been reported. OBJECTIVE: To determine whether temporary subdural electrode strip placement during DBS implantation surgery affects lead implantation accuracy. METHODS: Twenty-four consecutive patients enrolled in a prospective database who underwent awake DBS surgery were identified. Ten of 24 subjects participated in R-ECoG. Lead locations were determined after fusing postoperative computed tomography scans into the surgical planning software. The effect of brain shift was quantified using Lead-DBS and analyzed in a mixed-effects model controlling for time interval to postoperative computed tomography. Targeting accuracy was reported as radial and Euclidean distance errors and compared with Mann-Whitney tests. RESULTS: Neither radial error nor Euclidean distance error differed significantly between R-ECoG participants and nonparticipants. Pneumocephalus volume did not differ between the 2 groups, but brain shift was slightly greater with R-ECoG. Pneumocephalus volume correlated with brain shift, but neither of these measures significantly correlated with Euclidean distance error. There were no complications in either group. CONCLUSION: In addition to an excellent general safety profile as has been reported previously, these results suggest that performing R-ECoG during DBS implantation surgery does not affect the accuracy of lead placement.

Movement decoding using spatio-spectral features of cortical and subcortical local field potentials.

The first commercially sensing enabled deep brain stimulation (DBS) devices for the treatment of movement disorders have recently become available. In the future, such devices could leverage machine learning based brain signal decoding strategies to individualize and adapt therapy in real-time. As multi-channel recordings become available, spatial information may provide an additional advantage for informing machine learning models. To investigate this concept, we compared decoding performances from single channels vs. spatial filtering techniques using intracerebral multitarget electrophysiology in Parkinson's disease patients undergoing DBS implantation. We investigated the feasibility of spatial filtering in invasive neurophysiology and the putative utility of combined cortical ECoG and subthalamic local field potential signals for decoding grip-force, a well-defined and continuous motor readout. We found that adding spatial information to the model can improve decoding (6% gain in decoding), but the spatial patterns and additional benefit was highly individual. Beyond decoding performance results, spatial filters and patterns can be used to obtain meaningful neurophysiological information about the brain networks involved in target behavior. Our results highlight the importance of individualized approaches for brain signal decoding, for which multielectrode recordings and spatial filtering can improve precision medicine approaches for clinical brain computer interfaces.

Lateralized and Region-Specific Thalamic Processing of Lexical Status during Reading Aloud.

To explore whether the thalamus participates in lexical status (word vs nonword) processing during spoken word production, we recorded local field potentials from the ventral lateral thalamus in 11 essential tremor patients (three females) undergoing thalamic deep-brain stimulation lead implantation during a visually cued word and nonword reading-aloud task. We observed task-related beta (12-30 Hz) activity decreases that were preferentially time locked to stimulus presentation, and broadband gamma (70-150 Hz) activity increases, which are thought to index increased multiunit spiking activity, occurring shortly before and predominantly time locked to speech onset. We further found that thalamic beta activity decreases bilaterally were greater when nonwords were read, demonstrating bilateral sensitivity to lexical status that likely reflects the tracking of task effort; in contrast, greater nonword-related increases in broadband gamma activity were observed only on the left, demonstrating lateralization of thalamic broadband gamma selectivity for lexical status. In addition, this lateralized lexicality effect on broadband gamma activity was strongest in more anterior thalamic locations, regions which are more likely to receive basal ganglia than cerebellar afferents and have extensive connections with prefrontal cortex including Brodmann's areas 44 and 45, regions consistently associated with grapheme-to-phoneme conversions. These results demonstrate active thalamic participation in reading aloud and provide direct evidence from intracranial thalamic recordings for the lateralization and topography of subcortical lexical status processing.SIGNIFICANCE STATEMENT Despite the corticocentric focus of most experimental work and accompanying models, there is increasing recognition of the role of subcortical structures in speech and language. Using local field potential recordings in neurosurgical patients, we demonstrated that the thalamus participates in lexical status (word vs nonword) processing during spoken word production, in a lateralized and region-specific manner. These results provide direct evidence from intracranial thalamic recordings for the lateralization and topography of subcortical lexical status processing.

Differentiation of speech-induced artifacts from physiological high gamma activity in intracranial recordings.

There is great interest in identifying the neurophysiological underpinnings of speech production. Deep brain stimulation (DBS) surgery is unique in that it allows intracranial recordings from both cortical and subcortical regions in patients who are awake and speaking. The quality of these recordings, however, may be affected to various degrees by mechanical forces resulting from speech itself. Here we describe the presence of speech-induced artifacts in local-field potential (LFP) recordings obtained from mapping electrodes, DBS leads, and cortical electrodes. In addition to expected physiological increases in high gamma (60-200 Hz) activity during speech production, time-frequency analysis in many channels revealed a narrowband gamma component that exhibited a pattern similar to that observed in the speech audio spectrogram. This component was present to different degrees in multiple types of neural recordings. We show that this component tracks the fundamental frequency of the participant's voice, correlates with the power spectrum of speech and has coherence with the produced speech audio. A vibration sensor attached to the stereotactic frame recorded speech-induced vibrations with the same pattern observed in the LFPs. No corresponding component was identified in any neural channel during the listening epoch of a syllable repetition task. These observations demonstrate how speech-induced vibrations can create artifacts in the primary frequency band of interest. Identifying and accounting for these artifacts is crucial for establishing the validity and reproducibility of speech-related data obtained from intracranial recordings during DBS surgery.

Barques are generated in posterior hippocampus and phase reverse over lateral posterior hippocampal surface.

OBJECTIVE: To investigate whether barques can be localized across the hippocampal longitudinal axis with sufficient specificity. METHODS: We identified 51 focal epilepsy patients implanted with a minimum of two electrodes - unilateral anterior and posterior - in either hippocampus. We used visual inspection of the intracranial electroencephalogram (iEEG) and 3D brain volume spectrum-based statistical parametric mapping (SPM) to localize barques. RESULTS: In 18/51 patients (35.29%), barques were identified in 22/70 (31.42%) hippocampi. In all hippocampi (100%), barques were present in the posterior hippocampus, while 9 (40.90%) showed concurrent non-independent barque activity anteriorly (P < 0.0001). Statistical parametric mapping confirmed the posterior barque localization, with significant differences in t-values (t(27) = 8.08, P < 0.0001) and z-scores (t(24) = 6.85, P < 0.0001) between anterior and posterior hippocampal barque activity. Posterior lateral extrahippocampal contacts demonstrated phase reversals of positive polarity during barque activity (P = 0.0092, compared to anterior extrahippocampal contacts). CONCLUSIONS: This study highlights the posterior hippocampal predominance of barques. Our findings are concordant with the posterior distribution of the scalp manifestation of barques as "14&6/sec positive spikes". The posterio-lateral hippocampal barque phase reversal can explain the positive polarity of scalp 14&6/sec spikes. SIGNIFICANCE: Understanding the properties of barques is critical for the iEEG interpretation in epilepsy surgery evaluations that include the hippocampus.

Hippocampal spindles and barques are normal intracranial electroencephalographic entities.

OBJECTIVE: To assess whether hippocampal spindles and barques are markers of epileptogenicity. METHODS: Focal epilepsy patients that underwent stereo-electroencephalography implantation with at least one electrode in their hippocampus were selected (n = 75). The occurrence of spindles and barques in the hippocampus was evaluated in each patient. We created pairs of pathologic and pathology-free groups according to two sets of criteria: 1. Non-invasive diagnostic criteria (patients grouped according to focal epilepsy classification). 2. Intracranial neurophysiological criteria (patient's hippocampi grouped according to their seizure onset involvement). RESULTS: Hippocampal spindles and barques appear equally often in both pathologic and pathology-free groups, both for non-invasive (Pspindles = 0.73; Pbarques = 0.46) and intracranial criteria (Pspindles = 0.08; Pbarques = 0.26). In Engel Class I patients, spindles occurred with similar incidence both within the non-invasive (P = 0.67) and the intracranial criteria group (P = 0.20). Barques were significantly more frequent in extra-temporal lobe epilepsy defined by either non-invasive (P = 0.01) or intracranial (P = 0.01) criteria. CONCLUSIONS: Both spindles and barques are normal entities of the hippocampal intracranial electroencephalogram. The presence of barques may also signify lack of epileptogenic properties in the hippocampus. SIGNIFICANCE: Understanding that hippocampal spindles and barques do not reflect epileptogenicity is critical for correct interpretation of epilepsy surgery evaluations and appropriate surgical treatment selection.

Sylvian fissure development is linked to differential genetic expression in the pre-folded brain.

The mechanisms by which the human cerebral cortex folds into its final form remain poorly understood. With most of the current models and evidence addressing secondary folds, we sought to focus on the global geometry of the mature brain by studying its most distinctive feature, the Sylvian fissure. A digital human fetal brain atlas was developed using previously obtained MRI imaging of 81 healthy fetuses between gestational ages 21 and 38 weeks. To account for the development of the Sylvian fissure, we compared the growth of the frontotemporal opercula over the insular cortex and compared the transcriptome of the developing cortices for both regions. Spatiotemporal mapping of the lateral hemispheric surface showed the highest rate of organized growth in regions bordering the Sylvian fissure of the frontal, parietal and temporal lobes. Volumetric changes were first observed in the posterior aspect of the fissure moving anteriorly to the frontal lobe and laterally in the direction of the temporal pole. The insular region, delineated by the limiting insular gyri, expanded to a much lesser degree. The gene expression profile, before folding begins in the maturing brain, was significantly different in the developing opercular cortex compared to the insula. The Sylvian fissure forms by the relative overgrowth of the frontal and temporal lobes over the insula, corresponding to domains of highly expressed transcription factors involved in neuroepithelial cell differentiation.

Different Principles Govern Different Scales of Brain Folding.

The signature folds of the human brain are formed through a complex and developmentally regulated process. In vitro and in silico models of this process demonstrate a random pattern of sulci and gyri, unlike the highly ordered and conserved structure seen in the human cortex. Here, we account for the large-scale pattern of cortical folding by combining advanced fetal magnetic resonance imaging with nonlinear diffeomorphic registration and volumetric analysis. Our analysis demonstrates that in utero brain growth follows a logistic curve, in the absence of an external volume constraint. The Sylvian fissure forms from interlobar folding, where separate lobes overgrow and close an existing subarachnoid space. In contrast, other large sulci, which are the ones represented in existing models, fold through an invagination of a flat surface, a mechanistically different process. Cortical folding is driven by multiple spatially and temporally different mechanisms; therefore regionally distinct biological process may be responsible for the global geometry of the adult brain.

Spatial convergence of distant cortical regions during folding explains why arteries do not cross the sylvian fissure.

OBJECTIVE: Cortical folding places regions that are separated by a large distance along the cortical surface in close proximity. This process is not homogeneous; regions such as the insular opercula have a much higher cortical surface distance (CSD) to euclidean distance (ED) than others. Here the authors explore the hypothesis that in the folded brain the CSD, and not the ED, determines regions of common irrigation, because this measure corresponds more closely with the distance along the prefolded brain, where the subarachnoid arterial vascular network starts forming. METHODS: The authors defined a convergence index that compared the ED to the CSD and applied it to the cortical surface reconstruction of an average brain. They then compared cortical convergence to the irrigation patterns of major sulci and fissures of the brain, by assessing whether these structures were crossed or not crossed by arterial vessels in 20 fixed hemispheres. RESULTS: The regions of highest convergence (top 1%) were clustered around the sylvian fissure, which is the only brain depression with high convergence values along its edges. Arterial crossings were commonly observed in every major sulcus of the brain, with the exception of the sylvian fissure, constituting a highly significant difference (p < 10-4). CONCLUSIONS: Arteries do not cross regions of high convergence. In the adult brain the CSD, rather than the ED, predicts the regional irrigation pattern. The distant origin of the frontal and temporal lobes creates a region of high cortical convergence, which explains why arteries do not cross the sylvian fissure.

Subthalamic Nucleus and Sensorimotor Cortex Activity During Speech Production.

The sensorimotor cortex is somatotopically organized to represent the vocal tract articulators such as lips, tongue, larynx, and jaw. How speech and articulatory features are encoded at the subcortical level, however, remains largely unknown. We analyzed LFP recordings from the subthalamic nucleus (STN) and simultaneous electrocorticography recordings from the sensorimotor cortex of 11 human subjects (1 female) with Parkinson's disease during implantation of deep-brain stimulation (DBS) electrodes while they read aloud three-phoneme words. The initial phonemes involved either articulation primarily with the tongue (coronal consonants) or the lips (labial consonants). We observed significant increases in high-gamma (60-150 Hz) power in both the STN and the sensorimotor cortex that began before speech onset and persisted for the duration of speech articulation. As expected from previous reports, in the sensorimotor cortex, the primary articulators involved in the production of the initial consonants were topographically represented by high-gamma activity. We found that STN high-gamma activity also demonstrated specificity for the primary articulator, although no clear topography was observed. In general, subthalamic high-gamma activity varied along the ventral-dorsal trajectory of the electrodes, with greater high-gamma power recorded in the dorsal locations of the STN. Interestingly, the majority of significant articulator-discriminative activity in the STN occurred before that in sensorimotor cortex. These results demonstrate that articulator-specific speech information is contained within high-gamma activity of the STN, but with different spatial and temporal organization compared with similar information encoded in the sensorimotor cortex.SIGNIFICANCE STATEMENT Clinical and electrophysiological evidence suggest that the subthalamic nucleus (STN) is involved in speech; however, this important basal ganglia node is ignored in current models of speech production. We previously showed that STN neurons differentially encode early and late aspects of speech production, but no previous studies have examined subthalamic functional organization for speech articulators. Using simultaneous LFP recordings from the sensorimotor cortex and the STN in patients with Parkinson's disease undergoing deep-brain stimulation surgery, we discovered that STN high-gamma activity tracks speech production at the level of vocal tract articulators before the onset of vocalization and often before related cortical encoding.