Biology subjects

Hermes, D.

Publications and source records attributed to Hermes, D..

30 records · Page 2Linked to original sources

bioRxiv2022

Signatures of electrical stimulation driven network interactions in the human limbic system

Stimulation-evoked signals are starting to be used as biomarkers to indicate the state and health of brain networks. The human limbic network, often targeted for brain stimulation therapy, is involved in emotion and memory processing. Previous anatomical, neurophysiological and functional studies suggest distinct subsystems within the limbic network (Rolls, 2015). Previous studies using intracranial electrical stimulation, however, have emphasized the similarities of the evoked waveforms across the limbic network. We test whether these subsystems have distinct stimulation-driven signatures. In seven patients with drug-resistant epilepsy we stimulated the limbic system with single pulse electrical stimulation (SPES). Reliable cortico-cortical evoked potentials (CCEPs) were measured between hippocampus and the posterior cingulate cortex (PCC) and between the amygdala and the anterior cingulate cortex (ACC). However, the CCEP waveform in the PCC after hippocampal stimulation showed a unique and reliable morphology, which we term the limbic H-wave. This limbic H-wave was visually distinct and separately decoded from the amygdala to ACC waveform. Diffusion MRI data show that the measured endpoints in the PCC overlap with the endpoints of the parolfactory cingulum bundle rather than the parahippocampal cingulum, suggesting that the limbic H-wave may travel through fornix, mammillary bodies and the anterior nucleus of the thalamus (ANT). This was further confirmed by stimulating the ANT, which evoked the same limbic H-wave but with a shorter latency. Limbic subsystems have unique stimulation evoked signatures that may be used in the future to help develop stimulation therapies. Significance StatementThe limbic system is often compromised in diverse clinical conditions, such as epilepsy or Alzheimers disease, and it is important to characterize its typical circuit responses. Stimulation evoked waveforms have been used in the motor system to diagnose circuit pathology. We translate this framework to limbic subsystems using human intracranial stereo EEG (sEEG) recordings that measure deeper brain areas. Our sEEG recordings describe a stimulation evoked waveform characteristic to the memory and spatial subsystem of the limbic network that we term the limbic H-wave. The limbic H-wave follows anatomical white matter pathways from hippocampus to thalamus to the posterior cingulum and shows promise as a distinct biomarker of signaling in the human brain memory and spatial limbic network.

neuroscience↗

bioRxiv2022

Homunculus Interruptus: A motor association area in the depth of the central sulcus

Cells in the precentral gyrus of the human brain directly send signals to the periphery to generate movement and are topologically organized as a map of the body. We find that movement induced electrophysiological changes from implanted depth electrodes extend this map 3-dimensionally throughout the volume of the gyrus. Unexpectedly, this organization is interrupted by a motor association area in the depths of the central sulcus at its mid-lateral aspect that is active during many different types of movements from both sides of the body.

neuroscience↗

bioRxiv2022

Canonical Response Parameterization: Quantifying the structure of responses to single-pulse intracranial electrical brain stimulation

Single-pulse electrical stimulation in the nervous system, often called cortico-cortical evoked potential (CCEP) measurement, is an important technique to understand how brain regions interact with one another. Voltages are measured from implanted electrodes in one brain area while stimulating another with brief current impulses separated by several seconds. Historically, researchers have tried to understand the significance of evoked voltage polyphasic deflections by visual inspection, but no general-purpose tool has emerged to understand their shapes or describe them mathematically. We describe and illustrate a new technique to parameterize brain stimulation data, where voltage response traces are projected into one another using a semi-normalized dot product. The length of timepoints from stimulation included in the dot product is varied to obtain a temporal profile of structural significance, and the peak of the profile uniquely identifies the duration of the response. Using linear kernel PCA, a canonical response shape is obtained over this duration, and then single-trial traces are parameterized as a projection of this canonical shape with a residual term. Such parameterization allows for dissimilar trace shapes from different brain areas to be directly compared by quantifying cross-projection magnitudes, response duration, canonical shape projection amplitudes, signal-to-noise ratios, explained variance, and statistical significance. Artifactual trials are automatically identified by outliers in sub-distributions of cross-projection magnitude, and rejected. This technique, which we call "Canonical Response Parameterization" (CRP) dramatically simplifies the study of CCEP shapes, and may also be applied in a wide range of other settings involving event-triggered data. Author summaryWe introduce a new machine learning technique for quantifying the structure of responses to single-pulse intracranial electrical brain stimulation. This approach allows voltage response traces of very different shape to be compared with one another. A tool like this has been needed to replace the status quo, where researchers may understand their data in terms of discovered structure rather than in terms of a pre-assigned, hand-picked, feature. The method compares single-trial responses pairwise to understand if there is a reproducible shape and how long it lasts. When significant structure is identified, the shape underlying it is isolated and each trial is parameterized in terms of this shape. This simple parameterization enables quantification of statistical significance, signal-to-noise ratio, explained variance, and average voltage of the response. Differently-shaped voltage traces from any setting can be compared with any other in a succinct mathematical framework. This versatile tool to quantify single-pulse stimulation data should facilitate a blossoming in the study of brain connectivity using implanted electrodes.

neuroscience↗

bioRxiv2022

Electrical stimulation of temporal and limbic circuitry produces distinct responses in human ventral temporal cortex

The human ventral temporal cortex (VTC) is highly connected to integrate visual perceptual inputs with feedback from cognitive and emotional networks. In this study, we used electrical brain stimulation to understand how different inputs from multiple brain regions drive unique electrophysiological responses in the VTC. We recorded intracranial EEG data in 6 patients implanted with intracranial electrodes for epilepsy surgery evaluation. Pairs of electrodes were stimulated with single pulse electrical stimulation, and corticocortical evoked potential (CCEP) responses were measured at electrodes in the collateral sulcus and lateral occipitotemporal sulcus of the VTC. Using a novel unsupervised machine learning method, we uncovered 2 to 4 distinct response shapes, termed basis profile curves (BPCs), at each recording electrode in the 11 to 500 ms post-stimulation interval. CCEPs of unique shape and high amplitude were elicited following stimulation of several regions and classified into a set of four consensus BPCs across subjects. One of the consensus BPCs was primarily elicited by stimulation of the hippocampus; another by stimulation of the amygdala; a third by stimulation of lateral cortical sites, such as the middle temporal gyrus; and the final one by stimulation of multiple distributed sites. Stimulation also produced sustained high frequency power decreases and low frequency power increases that spanned multiple BPC categories. Characterizing distinct shapes in stimulation responses provides a novel description of connectivity to the VTC and reveals significant differences in input from cortical and limbic structures. SIGNIFICANCE STATEMENTDisentangling the numerous input influences on highly connected areas in the brain is a critical step toward understanding how different brain networks work together to produce function. Single pulse electrical stimulation is an effective tool to accomplish this goal because the shapes and amplitudes of signals recorded from electrodes are informative of the synaptic physiology of the stimulation-driven inputs. We focused on targets in the ventral temporal cortex because it is an area strongly implicated in visual object perception. By using a data-driven clustering algorithm, we identified anatomical regions with distinct input connectivity profiles to the ventral temporal cortex. Examining high frequency power changes revealed possible modulation of excitability at the recording site induced by electrical stimulation of connected regions.

neuroscience↗

bioRxiv2022

The development of efficient communication in the human connectome.

The structure of the human connectome develops from childhood throughout adolescence to middle age, but how this affects the speed of neuronal signaling is not well described. To understand the maturation process of transmission speed across the connectome, we stimulated electrocorticographic (ECoG) electrode pairs in 75 human subjects of 4 to 51 years old. We then measured the latency of cortico-cortical evoked responses in other electrodes. Each year, responses got faster, until transmission speed reached a minimum latency at the age of 29-44 years old. Responses during middle age were about two times faster compared to childhood. These increases in speed were observed in both long- and short-range connections between and within frontal, central, parietal, and temporal areas. These findings indicate that long-range and short-range communication between brain regions increase in efficiency well throughout adolescence.

neuroscience↗

bioRxiv2022

Measuring brain beats: cardiac-aligned fast fMRI signals

Blood and cerebrospinal fluid (CSF) pulse and flow throughout the brain, driven by the cardiac cycle. These fluid dynamics, which are essential to healthy brain function, are characterized by several noninvasive magnetic resonance imaging (MRI) methods. Recent developments in fast MRI, specifically simultaneous multislice (SMS) acquisition methods, provide a new opportunity to rapidly and broadly assess cardiac-driven flow, including CSF spaces, surface vessels and parenchymal vessels. We use these techniques to assess blood and CSF flow dynamics in brief (3.5 minute) scans on a conventional 3T MRI scanner. Cardiac pulses are measured with a photoplethysmograph (PPG) on the index finger, along with fMRI signals in the brain. We retrospectively analyze the fMRI signals gated to the heart beat. Highly reliable cardiac-gated fMRI temporal signals are observed in CSF and blood on the timescale of one heartbeat (test-retest reliability within subjects R2>0.50). Cardiac pulsations with a local minimum following systole are observed in blood vessels, with earlier extrema in the carotid and basilar arteries and in branches of the anterior, posterior and middle cerebral arteries and extrema [~]200 ms later in the superior sagittal, transverse and straight sinuses. CSF spaces in the ventricles and subarachnoid space showed cardiac pulsations with a local maximum following systole instead. Similar responses are observed, with less temporal detail, in slower resting state scans with slice timing retrospectively aligned to the cardiac pulse in the same manner. The SMS measurements rapidly, noninvasively and reliably sample brain-wide fMRI signal pulsations aligned to the heartbeat. The measurements estimate the amplitude and phase of cardiac driven fMRI pulsations in the CSF relative to those in the arteries, which is thought to be an estimate of the local intracranial impedance. Cardiac aligned fMRI signals can provide new insights about fluid dynamics or diagnostics for diseases where these dynamics are important.

neuroscience↗

bioRxiv2021

Typical somatomotor physiology of the hand is preserved in a patient with an amputated arm

Electrophysiological signals in the human motor system may change in different ways after deafferentation, with some studies emphasizing reorganization while others propose retained physiology. Understanding whether motor electrophysiology is retained over longer periods of time can be invaluable for patients with paralysis (e.g. ALS or brainstem stroke) when signals from sensorimotor areas may be used for communication or control over neural prosthetic devices. In addition, a maintained electrophysiology can potentially benefit the treatment of phantom limb pains through prolonged use of these signals in a brain-machine interface (BCI). Here, we were presented with the unique opportunity to investigate the physiology of the sensorimotor cortex in a patient with an amputated arm using electrocorticographic (ECoG) measurements. While implanted with an ECoG grid for clinical evaluation of electrical stimulation for phantom limb pain, the patient performed attempted finger movements with the contralateral (lost) hand and executed finger movements with the ipsilateral (healthy) hand. The electrophysiology of the sensorimotor cortex contralateral to the amputated hand remained very similar to that of hand movement in healthy people, with a spatially focused increase of high-frequency band (65-175Hz; HFB) power over the hand region and a distributed decrease in low-frequency band (15-28Hz; LFB) power. The representation of the three different fingers (thumb, index and little) remained intact and HFB patterns could be decoded using support vector learning at single-trial classification accuracies of >90%, based on the first 1-3s of the HFB response. These results demonstrate that hand representations are largely retained in the motor cortex. The intact physiological response of the amputated hand, the high distinguishability of the fingers and fast temporal peak are encouraging for neural prosthetic devices that target the sensorimotor cortex.

neuroscience↗

bioRxiv2021

Basis profile curve identification to understand electrical stimulation effects in human brain networks

Brain networks can be explored by delivering brief pulses of electrical current in one area while measuring voltage responses in other areas. We propose a convergent paradigm to study brain dynamics, focusing on a single brain site to observe the average effect of stimulating each of many other brain sites. Viewed in this manner, visually-apparent motifs in the temporal response shape emerge from adjacent stimulation sites. This work constructs and illustrates a data-driven approach to determine characteristic spatiotemporal structure in these response shapes, summarized by a set of unique "basis profile curves" (BPCs). Each BPC may be mapped back to underlying anatomy in a natural way, quantifying projection strength from each stimulation site using simple metrics. Our technique is demonstrated for an array of implanted brain surface electrodes in a human patient. This framework enables straightforward interpretation of single-pulse brain stimulation data, and can be applied generically to explore the diverse milieu of interactions that comprise the connectome. Author summaryWe present a new machine learning framework to probe how brain regions interact using single-pulse electrical stimulation. Unlike previous studies, this approach does not assume a form for how one brain area will respond to stimulation in another area, but rather discovers the shape of the response in time from the data. We call the set of characteristic discovered response shapes "basis profile curves" (BPCs), and show how these can be mapped back onto the brain quantitatively. An illustrative example is included from one of our human patients to characterize inputs to the parahippocampal gyrus. A code package is downloadable from https://purl.stanford.edu/rc201dv0636 so the reader may explore the technique for own data, or study sample data provided to reproduce the illustrative case presented in the manuscript.

neuroscience↗

bioRxiv2020

Functional MRI based simulations of ECoG grid configurations for optimal measurement of spatially distributed hand-gesture information

In electrocorticography (ECoG), the physical characteristics of the electrode grid determine which aspect of the neurophysiology is measured. For particular cases, the ECoG grid may be tailored to capture specific features, such as in the development and use of brain-computer-interfaces (BCI). Neural representations of hand movement are increasingly used to control ECoG based BCIs. However, it remains unclear which grid configurations are the most optimal to capture the dynamics of hand gesture information. Here, we investigate how the design and surgical placement of grids would affect the usability of ECoG measurements. High resolution 7T functional MRI was used as a proxy for neural activity in ten healthy participants to simulate various grid configurations, and evaluated the performance of each configuration for decoding hand gestures. The grid configurations varied in number of electrodes, electrode distance and electrode size. Optimal decoding of hand gestures occurred in grid configurations with a higher number of densely-packed, large-size, electrodes up to a grid of ~5x5 electrodes. When restricting the grid placement to a highly informative region of primary sensorimotor cortex, optimal parameters converged to about 3x3 electrodes, an inter-electrode distance of 8mm, and an electrode size of 3mm radius (performing at ~70% 3-class classification accuracy). Our approach might be used to identify the most informative region, find the optimal grid configuration and assist in positioning of the grid to achieve high BCI performance for the decoding of hand-gestures prior to surgical implantation.

bioengineering↗

bioRxiv2020

SK3-overexpression drives sex-dimorphic working and social memory impairment in mice.

Although sex differences in memory tasks dependent on hippocampal function have been described in several species, including rodents and humans, the exact mechanisms involved remain debatable. The function of the small-conductance Ca2+-activated K+ channel type 3 has been associated with cognitive deficits, and its overexpression in male mice (T/T) induces shrinkage of the hippocampus. Here we describe that opposite to the observation in males, in female mice, SK3-induced-reduction in the volume of the hippocampal formation does not interfere with working and social memory performance. Male, but not female T/T mice showed decreased adult hippocampal neurogenesis and down-regulation of the expression of the genes related to Akt/mTOR and MAP kinase pathways. T/T male mice exhibit impaired estrogen and Neurogulin 1 signaling. An increased number of filopodia spines is observed in the dentate gyrus (DG). Our results suggest a fine-tune modulation of SK3 expression participates in the sex-dependent function of the hippocampus via estrogen signaling and neuroplasticity in the DG. Our results reinforce the importance of testing male and female mice while conducting experiments with transgenic mice.

neuroscience↗

bioRxiv2019

An image-computable model for the stimulus selectivity of gamma oscillations

Gamma oscillations in visual cortex have been hypothesized to be critical for perception, cognition, and information transfer. However, observations of these oscillations in visual cortex vary widely; some studies report little to no stimulus-induced narrowband gamma oscillations, others report oscillations for only some stimuli, and yet others report large oscillations for most stimuli. To reconcile these findings and better understand this signal, we developed a model that predicts gamma responses for arbitrary images and validated this model on electrocorticography (ECoG) data from human visual cortex. The model computes variance across the outputs of spatially pooled orientation channels, and accurately predicts gamma amplitude across 86 images. Gamma responses were large for a small subset of stimuli, differing dramatically from fMRI and ECoG broadband (non-oscillatory) responses. We suggest that gamma oscillations in visual cortex serve as a biomarker of gain control rather than being a fundamental mechanism for communicating visual information.

neuroscience↗

bioRxiv2016

Neuronal synchrony and the relation between the BOLD response and the local field potential.

The most widespread measures of human brain activity are the blood oxygen level dependent (BOLD) signal and surface field potential. Prior studies report a variety of relationships between these signals. To develop an understanding of how to interpret these signals and the relationship between them, we developed a model of (a) neuronal population responses, and (b) transformations from neuronal responses into the fMRI BOLD signal and electrocorticographic (ECoG) field potential. Rather than seeking a transformation between the two measures directly, this approach interprets each measure with respect to the underlying neuronal population responses. This model accounts for the relationship between BOLD and ECoG data from human visual cortex in V1-V3, with the model predictions and data matching in three ways: Across stimuli, the BOLD amplitude and ECoG broadband power were positively correlated, the BOLD amplitude and alpha power (8-13 Hz) were negatively correlated, and the BOLD amplitude and narrowband gamma power (30-80 Hz) were uncorrelated. The two measures provide complementary information about human brain activity and we infer that features of the field potential that are uncorrelated with BOLD arise largely from changes in synchrony, rather than level, of neuronal activity.

neuroscience↗