Biology subjects

Sommer, M. A.

Publications and source records attributed to Sommer, M. A..

11 recordsLinked to original sources

bioRxiv2026

Computational investigation of circuit mechanisms underlying short-latency responses to cortical stimulation

Transcranial magnetic stimulation (TMS) over the primary motor cortex (M1) elicits a series of high frequency volleys termed D- and I-waves measured epidurally in the corticospinal tract of awake humans. Further, intracortical microstimulation (ICMS) in M1 of non-human primates evokes D- and I-wave responses similar to those observed in TMS. The cortical circuits and mechanisms involved in the generation of D- and I-waves by stimulation of M1 remain unclear. Here, we implemented computational models of cortical columns with laminarly-organized biophysically-based neurons, following existing models published in the literature: (1) M1 - single compartment (SC), (2) M1 - multi-compartment (MC), (3) primary auditory cortex (A1) - MC, and (4) primary somatosensory cortex (S1) - MC. The network connectivity of each model represented wiring found in the respective cortical regions. The direct effects of stimulation-induced electric fields were modeled as activation of different proportions of pyramidal neurons (PNs) across layers, and dose response curves were constructed for layer 5 (L5) PNs. Both the M1 and A1 models reproduced D- and I-waves, with the magnitude of I-waves increasing with higher recruitment of layer 2/3 and layer 5 PNs. The S1-MC model evoked rhythmic firing activity but with timings mismatched to experimental I-waves. The models replicated the experimentally observed effects of pharmacological agents on I-waves, and virtual lesions of specific neural populations across models revealed plausible microcircuit explanations for the first and later I-waves. This comprehensive comparison of models across multiple cortical regions identified consistent mechanisms underlying the cortical response to TMS and contributes to the refinement of computational strategies for optimizing stimulation paradigms.

neuroscience↗

bioRxiv2025

Retrograde Optogenetics Reveals Functional Convergence within a Corticotectal Pathway of Non-Human Primates

Understanding how the cerebral cortex communicates with subcortical areas to drive behavior remains a central question in system neuroscience. One key unresolved issue is whether prefrontal cortical outputs to motor-related subcortical regions carry predominantly motor commands or mixed sensory-motor signals. Retrograde optogenetics offers a powerful way to interrogate such projection-defined circuits, but its use in non-human primates has been limited. Here, we applied retrograde optogenetics in awake macaques to directly test the functional organization of the corticotectal projection from the frontal eye field (FEF) to the superior colliculus (SC). We asked whether the FEF output signals to SC are motor-dominant or broadly sensory-motor. Optical activation of this pathway evoked robust, contralateral saccades and selectively modulated reaction times, demonstrating its causal role in saccade generation. Optogenetically tagging FEF neurons projecting to SC revealed a heterogeneous population of visual, visuomotor, and motor neurons. This diverse output converged predominantly onto motor-related neurons in the SC. These findings support a visuomotor convergence model, in which diverse FEF outputs drive motor-selective SC neurons with activity sufficient for saccade generation, and thus resolve long-standing questions over the composition of FEF outputs. Additionally, our results establish retrograde optogenetics as a tool for dissecting projection-defined circuits in primates and for precisely probing the neural pathways that link perception to action. HighlightsRetrograde optogenetics enabled pronounced and selective modulation of behavior in macaques. Selective optical stimulation of the FEF-SC pathway evoked contralateral saccades and modulated reaction times. Opto-tagging revealed diverse visual, visuomotor, and motor FEF outputs converging onto motor-related SC neurons. Findings support a visuomotor convergence model and demonstrate the power of projection-specific optogenetic tools in primates.

neuroscience↗

bioRxiv2025

Projection Targeting with Phototagging to Study the Structure and Function of Retinal Ganglion Cells

Visual information from the retina is sent to diverse targets throughout the brain by different retinal ganglion cells (RGCs). Much of our knowledge about the different RGC types and how they are routed to these brain targets is based on mice, largely due to the extensive library of genetically modified mouse lines. To alleviate the need for using genetically modified animal models for studying retinal projections, we developed a high-throughput approach called projection targeting with phototagging that combines retrograde viral labeling, optogenetic identification, functional characterization using multi-electrode arrays, and morphological analysis. This method enables the simultaneous investigation of projections, physiology, and structure-function relationships across dozens to hundreds of cells in a single experiment. We validated this method in rats by targeting RGCs projecting to the superior colliculus, revealing multiple functionally defined cell types that align with prior studies in mice. By integrating established techniques into a scalable workflow, this framework enables comparative investigations of visual circuits across species, expanding beyond genetically tractable models. MotivationVisual information from the retina is distributed to diverse targets throughout the brain. Much of our knowledge about how visual information is processed and routed to these brain targets is based on mice because of the large library of genetically modified mouse lines. For most other species, such libraries are not available. Therefore, we were motivated to develop an approach for characterizing diverse retinal projections into the brain that can be applied to other species. We aimed to achieve projection targeting with retrograde viral vectors, followed by identification of circuit-specific retinal ganglion cells (RGCs) with optogenetics, functional characterization with multi-electrode array (MEA) recordings, and morphological description with in situ and confocal microscopy. The resulting high-throughput approach permits functional and morphological investigation of dozens to hundreds of cells in individual experiments. By replacing the need for genetically modified animals with a suite of standard techniques, this approach should improve cross-species comparisons of the initial stages of visual processing. SummaryUnderstanding the structure-function relationships across neurons is challenging, particularly when circuits are composed of dozens of distinct cell types. We refined an approach, called projection targeting with phototagging, that allows simultaneous elucidation of the projections, morphology, and visual response properties of diverse RGC types in the mammalian retina. The approach combines retrograde virally mediated phototagging of RGCs, microscopy, and large-scale MEA measurements. Importantly, the approach does not rely on transgenic animals and thus is generalizable across species. We validated this approach in rats by targeting retinal projections to the superior colliculus (SC). We showed that multiple RGC types project to the SC and that these results in rats align well with prior findings from transgenic mouse studies. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=194 HEIGHT=200 SRC="FIGDIR/small/661576v1_ufig1.gif" ALT="Figure 1"> View larger version (49K): org.highwire.dtl.DTLVardef@1003310org.highwire.dtl.DTLVardef@1881cdorg.highwire.dtl.DTLVardef@f1dba1org.highwire.dtl.DTLVardef@8a539f_HPS_FORMAT_FIGEXP M_FIG C_FIG HighlightsO_LIThe viral vector, AAV2retro, is effective for delivering opsins to retinal ganglion cells (RGCs) for phototagging and projection mapping. C_LIO_LIPhototagging allows matching RGC visual responses to morphology, while also identifying their central projections. C_LIO_LIThe approach does not rely on genetically modified animals and thus is generalizable across species. C_LI

neuroscience↗

bioRxiv2025

Frontal eye field neurons predict "anti-Bayesian" but not Bayesian judgments of visual stability across saccades

Expectations or prior beliefs about the world have been shown to modulate sensory processing both at the behavioral and neural levels. Bayesian models predict that such priors compensate for input uncertainty to optimize sensory judgments. Although Bayesian behavior is prevalent across sensorimotor systems, the relationship between priors and Bayesian inference is not obligatory. Priors may simply shift ones internal decision boundaries without interacting with sensory uncertainty at all. We recently showed that humans and monkeys use both Bayesian and non-Bayesian strategies when reporting judgments of visual stability across saccades, despite using priors in both cases. While they increased prior use to compensate for internal, movement-driven sensory uncertainty in a Bayesian manner, they decreased prior use when faced with external, visual image uncertainty. The latter, "anti-Bayesian" pattern was best explained by a model in which category boundaries were adjusted by the prior but susceptible to image noise. Here, we recorded neural activity in the frontal eye field (FEF), a prefrontal region important for visuosaccadic behavior, while toggling between subjects prior use for Bayesian and anti-Bayesian behavior via trial-by-trial manipulation of the two uncertainty conditions. First, we found that FEF activity signaled the priors in both conditions. The prior-related modulation of activity, however, predicted only the anti-Bayesian, categorization behavior. The results suggest that neural activity in the FEF reflects the use of a flexible decision boundary for the perception of visual stability and, more generally, that neural mechanisms for Bayesian inference and visual categorization are dissociable and distributed in the primate brain. SignificanceAppreciating a visual scene depends not only on retinal input, but also on priors about the world. Foreknowledge interacts with visual inputs to improve reactions and decisions. One way the brain combines priors and inputs is by using Bayes rule to model optimal outcomes. A simpler way is by categorizing inputs with prior-adjusted boundaries. Here, we tested how neurons in primate frontal cortex use priors: for Bayes rule, or for flexible categorization? A key feature of the study was to use a single perceptual task that was varied trial-by-trial to yield either Bayesian or categorization behaviors. We could then establish which behavior the neurons encoded. The implications extend beyond visuomotor behavior to broader neurocomputational mechanisms of prior use for cognition.

neuroscience↗

bioRxiv2024

Circuits and mechanisms for TMS-induced corticospinal waves: Connecting sensitivityanalysis to the network graph

Transcranial magnetic stimulation (TMS) is a non-invasive, FDA-cleared treatment for neuropsychiatric disorders with broad potential for new applications, but the neural circuits that are engaged during TMS are still poorly understood. Recordings of neural activity from the corticospinal tract provide a direct readout of the response of motor cortex to TMS, and therefore a new opportunity to model neural circuit dynamics. The study goal was to use epidural recordings from the cervical spine of human subjects to develop a computational model of a motor cortical macrocolumn through which the mechanisms underlying the response to TMS, including direct and indirect waves, could be investigated. An in-depth sensitivity analysis was conducted to identify important pathways, and machine learning was used to identify common circuit features among these pathways. Sensitivity analysis identified neuron types that preferentially contributed to single corticospinal waves. Single wave preference could be predicted using the average connection probability of all possible paths between the activated neuron type and L5 pyramidal tract neurons (PTNs). For these activations, the total conduction delay of the shortest path to L5 PTNs determined the latency of the corticospinal wave. Finally, there were multiple neuron type activations that could preferentially modulate a particular corticospinal wave. The results support the hypothesis that different pathways of circuit activation contribute to different corticospinal waves with participation of both excitatory and inhibitory neurons. Moreover, activation of both afferents to the motor cortex as well as specific neuron types within the motor cortex initiated different I-waves, and the results were interpreted to propose the cortical origins of afferents that may give rise to certain I-waves. The methodology provides a workflow for performing computationally tractable sensitivity analyses on complex models and relating the results to the network structure to both identify and understand mechanisms underlying the response to acute stimulation. AUTHOR SUMMARYUnderstanding circuit mechanisms underlying the response to transcranial magnetic stimulation remains a significant challenge for translational and clinical research. Computational models can reconstruct network activity in response to stimulation, but basic sensitivity analyses are insufficient to identify the fundamental circuit properties that underly an evoked response. We developed a data-driven neuronal network model of motor cortex, constrained with human recordings, that reproduced the corticospinal response to magnetic stimulation. The model supported several hypotheses, e.g., the importance of stimulating incoming fibers as well as neurons within the cortical column and the relevance of both excitatory and inhibitory neurons. Following a sensitivity analysis, we conducted a secondary structural analysis that linked the results of the sensitivity analysis to the network using machine learning. The structural analysis pointed to anatomical mechanisms that contributed to specific peaks in the response. Generally, given the anatomy and circuit of a neural region, identifying strongly connected paths in the network and the conduction delays of these paths can screen for important contributors to response peaks. This work supports and expands on hypotheses explaining the response to transcranial magnetic stimulation and adds a novel method for identifying generalizable neural circuit mechanisms.

neuroscience↗

bioRxiv2022

Direct comparison of epifluorescence and immunostaining for assessing viral mediated gene expression in the primate brain

Viral vector technologies are commonly used in neuroscience research to understand and manipulate neural circuits, but successful applications of these technologies in non-human primate models have been inconsistent. An essential component to improve these technologies is an impartial and accurate assessment of the effectiveness of different viral constructs in the primate brain. We tested a diverse array of viral vectors delivered to the brain and extraocular muscles of macaques and compared three methods for histological assessment of viral-mediated fluorescent transgene expression: epifluorescence (Epi), immunofluorescence (IF), and immunohistochemistry (IHC). Importantly, IF and IHC identified a greater number of transduced neurons compared to Epi. Furthermore, IF and IHC reliably provided enhanced visualization of transgene in most cellular compartments (i.e., dendritic, axonal, and terminal fields), whereas the degree of labeling provided by Epi was inconsistent and predominantly restricted to somas and apical dendrites. Because Epi signals are unamplified (in contrast to IF and IHC), Epi likely provides a more veridical assessment for the amount of accumulated transgene and, thus, the potential to chemo- or optogenetically manipulate neuronal activity. The comparatively weak Epi signals suggest that the current generations of viral constructs, regardless of delivered transgene, are not optimized for primates. This reinforces an emerging viewpoint that viral vectors tailored for the primate brain are necessary for basic research and human gene therapy.

neuroscience↗

bioRxiv2022

Contributions of Bayesian and Discriminative Models to Active Visual Perception across Saccades

The brain interprets sensory inputs to guide behavior, but behavior disrupts sensory inputs. In primates, saccadic eye movements displace visual images on the retina and yet the brain perceives visual stability, a process called active vision. We studied whether active vision is Bayesian. Humans and monkeys reported whether an image moved during saccades. We tested whether they used prior expectations to account for sensory uncertainty in a Bayesian manner. For continuous judgments, subjects were Bayesian. For categorical judgments, they were anti-Bayesian for uncertainty due to external, image noise but Bayesian for uncertainty due to internal, motor-driven noise. A discriminative learning model explained the anti-Bayesian effect. Therefore, active vision uses both Bayesian and discriminative models depending on task requirements (continuous vs. categorical) and the source of uncertainty (image noise vs. motor-driven noise), suggesting that active perceptual mechanisms are governed by the interaction of both models.

neuroscience↗

bioRxiv2022

Concurrent fMRI demonstrates propagation of TMS effects across task-related networks

Transcranial magnetic stimulation (TMS) has become an important technique in both scientific and clinical practices, and yet our understanding of how the brain responds to TMS is still limited. Concurrent neuroimaging during TMS may bridge this gap, and emerging evidence suggests widespread that modulatory effects of TMS may be best captured through changes in functional connectivity between distributed networks, rather than local changes in cortical activity. However, the relationship between TMS stimulation parameters and evoked changes in functional connectivity is unknown. In this study, 24 healthy volunteers received concurrent TMS-fMRI while performing a dot-motion direction discrimination task. An MR-compatible coil was used to apply trains of three pulses at 10 Hz rTMS over the primary visual cortex (V1) at the onset of the dot stimuli with four levels of stimulation intensity (20%, 40%, 80%, and 120% of resting motor threshold, RMT). Behavioral results demonstrated impairment of motion discrimination at 80% RMT. FMRI results yielded three findings. First, functional connectivity between visual and non-visual areas increased as a function of rTMS intensity. Second, connectivity within the visual network was positively associated with motion accuracy, while the connectivity between visual and non-visual regions was negatively associated with motion accuracy. Lastly, we found that reductions in the similarity between functional and structural connectivity associated with increasing TMS intensity were constrained to the visual network. These findings demonstrate spatially dependent nonlinear effects of TMS intensity on brain functional connectivity that proceed beyond the site of stimulation and influence associated behavior.

neuroscience↗

bioRxiv2020

Dose-dependent enhancement of motion direction discrimination with transcranial magnetic stimulation of visual cortex

Despite the widespread use of transcranial magnetic stimulation (TMS) in research and clinical care, the underlying mechanisms-of-actions that mediate modulatory effects remain poorly understood. To fill this gap, we studied dose-response functions of TMS for modulation of visual processing. Our approach combined electroencephalography (EEG) with application of single pulse TMS to visual cortex as participants performed a motion perception task. During participants first visit, motion coherence thresholds, 64-channel visual evoked potentials (VEPs), and TMS resting motor thresholds (RMT) were measured. In second and third visits, single pulse TMS was delivered 30 ms before the onset of motion or at the onset latency of the N2 VEP component derived from the first session. TMS was delivered at 0%, 80%, 100%, or 120% of RMT over the site of N2 peak activity, or at 120% over vertex. Behavioral results demonstrated a significant main effect of TMS timing on accuracy, with better performance when TMS was applied at N2-Onset timing versus Pre-Onset, as well as a significant interaction, indicating that 80% intensity produced higher accuracy than other conditions. TMS effects on VEPs showed reduced amplitudes in the 80% Pre-Onset condition, an increase for the 120% N2-Onset condition, and monotonic amplitude scaling with stimulation intensity. The N2 component was not affected by TMS. These findings reveal dose-response relationships between intensity and timing of TMS on visual perception and electrophysiological brain activity, generally indicating greater facilitation at stimulation intensities below RMT.

neuroscience↗

bioRxiv2019

Application of long-interval paired-pulse transcranial magnetic stimulation to motion-sensitive visual cortex does not lead to changes in motion perception

The perception of visual motion is dependent on a set of occipitotemporal regions which are readily accessible to neuromodulation. Previous studies using paired-pulse Transcranial Magnetic Stimulation (ppTMS) have provided evidence of the capacity of this type of protocols to modulate cognitive processes. To test whether such cortical modulation can be observed in the visual system, particularly during motion perception, ppTMS was applied to the occipital cortex using both scalp-based and meta-analytic targeting coordinates. In this within-subject, sham-controlled study, fifteen subjects completed two sessions in two consecutive weeks. On the first visit, subject-specific resting motor threshold (RMT) was determined and participants performed an adaptive motion discrimination task to determine individual motion sensitivity. During the second visit, subjects performed the same task with three individualized difficulty levels as two TMS pulses were delivered respectively -150 and -50 ms prior to motion stimulus onset at 120% RMT, under the logic that the cumulative inhibitory effect of these two pulses would alter motion sensitivity as measured by the individually calibrated task. The ppTMS was delivered at one of two locations: 3 cm dorsal and 5 cm lateral to inion (scalp-based coordinate), or at the site of peak activation for \"motion\" according to the NeuroSynth fMRI database (meta-analytic coordinate). Sham stimulation was delivered on one-third of trials and evenly between the two targets. Analyses showed no significant active-versus-sham effects of ppTMS when stimulation was delivered to the meta-analytic (p = 0.15) or scalp-based coordinates (p = 0.17), which were separated by 29 mm on average. Additionally, there was no was significant interaction between ppTMS at either location and task difficulty level (p = 0.12 and p = 0.33, respectively). These findings fail to support the hypothesis that long-interval ppTMS recruits inhibitory processes in motion-sensitive cortex, but must be considered within the limits of the current design choices.\n\nHIGHLIGHTSO_LILong-interval paired-pulse TMS was applied to visual cortex during a motion task\nC_LIO_LIThe ppTMS was delivered according to scalp and meta-analytic coordinates, as well as sham\nC_LIO_LINo effects of active-versus-sham stimulation were observed on motion task performance\nC_LI

neuroscience↗

bioRxiv2019

Properties of decision-making tasks govern the tradeoff between model-based and model-free learning

When decisions must be made between uncertain options, optimal behavior depends on accurate estimations of the likelihoods of different outcomes. The contextual factors that govern whether these estimations depend on model-free learning (tracking outcomes) vs. model-based learning (learning generative stimulus distributions) are poorly understood. We studied model-free and model-based learning using serial decision-making tasks in which subjects selected a rule and then used it to flexibly act on visual stimuli. A factorial approach defined a family of behavioral models that could integrate model-free and model-based strategies to predict rule selection trial-by-trial. Bayesian model selection demonstrated that the subjects strategies varied depending on lower-level task characteristics such as the identities of the rule options. In certain conditions, subjects integrated learned stimulus distributions and tracked reward rates to guide their behavior. The results thus identify tradeoffs between model-based and model-free decision strategies, and in some cases parallel utilization, depending on task context.

neuroscience↗