Cingulate dynamics track depression recovery with deep brain stimulation.

Deep brain stimulation (DBS) of the subcallosal cingulate (SCC) can provide long-term symptom relief for treatment-resistant depression (TRD)1. However, achieving stable recovery is unpredictable2, typically requiring trial-and-error stimulation adjustments due to individual recovery trajectories and subjective symptom reporting3. We currently lack objective brain-based biomarkers to guide clinical decisions by distinguishing natural transient mood fluctuations from situations requiring intervention. To address this gap, we used a new device enabling electrophysiology recording to deliver SCC DBS to ten TRD participants (ClinicalTrials.gov identifier NCT01984710). At the study endpoint of 24 weeks, 90% of participants demonstrated robust clinical response, and 70% achieved remission. Using SCC local field potentials available from six participants, we deployed an explainable artificial intelligence approach to identify SCC local field potential changes indicating the patient's current clinical state. This biomarker is distinct from transient stimulation effects, sensitive to therapeutic adjustments and accurate at capturing individual recovery states. Variable recovery trajectories are predicted by the degree of preoperative damage to the structural integrity and functional connectivity within the targeted white matter treatment network, and are matched by objective facial expression changes detected using data-driven video analysis. Our results demonstrate the utility of objective biomarkers in the management of personalized SCC DBS and provide new insight into the relationship between multifaceted (functional, anatomical and behavioural) features of TRD pathology, motivating further research into causes of variability in depression treatment.

Subcallosal cingulate deep brain stimulation evokes two distinct cortical responses via differential white matter activation.

Subcallosal cingulate (SCC) deep brain stimulation (DBS) is an emerging therapy for refractory depression. Good clinical outcomes are associated with the activation of white matter adjacent to the SCC. This activation produces a signature cortical evoked potential (EP), but it is unclear which of the many pathways in the vicinity of SCC is responsible for driving this response. Individualized biophysical models were built to achieve selective engagement of two target bundles: either the forceps minor (FM) or cingulum bundle (CB). Unilateral 2 Hz stimulation was performed in seven patients with treatment-resistant depression who responded to SCC DBS, and EPs were recorded using 256-sensor scalp electroencephalography. Two distinct EPs were observed: a 120 ms symmetric response spanning both hemispheres and a 60 ms asymmetrical EP. Activation of FM correlated with the symmetrical EPs, while activation of CB was correlated with the asymmetrical EPs. These results support prior model predictions that these two pathways are predominantly activated by clinical SCC DBS and provide first evidence of a link between cortical EPs and selective fiber bundle activation.

Constrained brain volume in an efficient coding model explains the fraction of excitatory and inhibitory neurons in sensory cortices.

The number of neurons in mammalian cortex varies by multiple orders of magnitude across different species. In contrast, the ratio of excitatory to inhibitory neurons (E:I ratio) varies in a much smaller range, from 3:1 to 9:1 and remains roughly constant for different sensory areas within a species. Despite this structure being important for understanding the function of neural circuits, the reason for this consistency is not yet understood. While recent models of vision based on the efficient coding hypothesis show that increasing the number of both excitatory and inhibitory cells improves stimulus representation, the two cannot increase simultaneously due to constraints on brain volume. In this work, we implement an efficient coding model of vision under a constraint on the volume (using number of neurons as a surrogate) while varying the E:I ratio. We show that the performance of the model is optimal at biologically observed E:I ratios under several metrics. We argue that this happens due to trade-offs between the computational accuracy and the representation capacity for natural stimuli. Further, we make experimentally testable predictions that 1) the optimal E:I ratio should be higher for species with a higher sparsity in the neural activity and 2) the character of inhibitory synaptic distributions and firing rates should change depending on E:I ratio. Our findings, which are supported by our new preliminary analyses of publicly available data, provide the first quantitative and testable hypothesis based on optimal coding models for the distribution of excitatory and inhibitory neural types in the mammalian sensory cortices.

Electrical and Optical Activation of Mesoscale Neural Circuits with Implications for Coding.

Artificial activation of neural circuitry through electrical microstimulation and optogenetic techniques is important for both scientific discovery of circuit function and for engineered approaches to alleviate various disorders of the nervous system. However, evidence suggests that neural activity generated by artificial stimuli differs dramatically from normal circuit function, in terms of both the local neuronal population activity at the site of activation and the propagation to downstream brain structures. The precise nature of these differences and the implications for information processing remain unknown. Here, we used voltage-sensitive dye imaging of primary somatosensory cortex in the anesthetized rat in response to deflections of the facial vibrissae and electrical or optogenetic stimulation of thalamic neurons that project directly to the somatosensory cortex. Although the different inputs produced responses that were similar in terms of the average cortical activation, the variability of the cortical response was strikingly different for artificial versus sensory inputs. Furthermore, electrical microstimulation resulted in highly unnatural spatial activation of cortex, whereas optical input resulted in spatial cortical activation that was similar to that induced by sensory inputs. A thalamocortical network model suggested that observed differences could be explained by differences in the way in which artificial and natural inputs modulate the magnitude and synchrony of population activity. Finally, the variability structure in the response for each case strongly influenced the optimal inputs for driving the pathway from the perspective of an ideal observer of cortical activation when considered in the context of information transmission. SIGNIFICANCE STATEMENT: Artificial activation of neural circuitry through electrical microstimulation and optogenetic techniques is important for both scientific discovery and clinical translation. However, neural activity generated by these artificial means differs dramatically from normal circuit function, both locally and in the propagation to downstream brain structures. The precise nature of these differences and the implications for information processing remain unknown. The significance of this work is in quantifying the differences, elucidating likely mechanisms underlying the differences, and determining the implications for information processing.

Modeling Inhibitory Interneurons in Efficient Sensory Coding Models.

There is still much unknown regarding the computational role of inhibitory cells in the sensory cortex. While modeling studies could potentially shed light on the critical role played by inhibition in cortical computation, there is a gap between the simplicity of many models of sensory coding and the biological complexity of the inhibitory subpopulation. In particular, many models do not respect that inhibition must be implemented in a separate subpopulation, with those inhibitory interneurons having a diversity of tuning properties and characteristic E/I cell ratios. In this study we demonstrate a computational framework for implementing inhibition in dynamical systems models that better respects these biophysical observations about inhibitory interneurons. The main approach leverages recent work related to decomposing matrices into low-rank and sparse components via convex optimization, and explicitly exploits the fact that models and input statistics often have low-dimensional structure that can be exploited for efficient implementations. While this approach is applicable to a wide range of sensory coding models (including a family of models based on Bayesian inference in a linear generative model), for concreteness we demonstrate the approach on a network implementing sparse coding. We show that the resulting implementation stays faithful to the original coding goals while using inhibitory interneurons that are much more biophysically plausible.

Outcome measures based on classification performance fail to predict the intelligibility of binary-masked speech.

To date, the most commonly used outcome measure for assessing ideal binary mask estimation algorithms is based on the difference between the hit rate and the false alarm rate (H-FA). Recently, the error distribution has been shown to substantially affect intelligibility. However, H-FA treats each mask unit independently and does not take into account how errors are distributed. Alternatively, algorithms can be evaluated with the short-time objective intelligibility (STOI) metric using the reconstructed speech. This study investigates the ability of H-FA and STOI to predict intelligibility for binary-masked speech using masks with different error distributions. The results demonstrate the inability of H-FA to predict the behavioral intelligibility and also illustrate the limitations of STOI. Since every estimation algorithm will make errors that are distributed in different ways, performance evaluations should not be made solely on the basis of these metrics.

Cochlear implant speech intelligibility outcomes with structured and unstructured binary mask errors.

It has been shown that intelligibility can be improved for cochlear implant (CI) recipients with the ideal binary mask (IBM). In realistic scenarios where prior information is unavailable, however, the IBM must be estimated, and these estimations will inevitably contain errors. Although the effects of both unstructured and structured binary mask errors have been investigated with normal-hearing (NH) listeners, they have not been investigated with CI recipients. This study assesses these effects with CI recipients using masks that have been generated systematically with a statistical model. The results demonstrate that clustering of mask errors substantially decreases the tolerance of errors, that incorrectly removing target-dominated regions can be as detrimental to intelligibility as incorrectly adding interferer-dominated regions, and that the individual tolerances of the different types of errors can change when both are present. These trends follow those of NH listeners. However, analysis with a mixed effects model suggests that CI recipients tend to be less tolerant than NH listeners to mask errors in most conditions, at least with respect to the testing methods in each of the studies. This study clearly demonstrates that structure influences the tolerance of errors and therefore should be considered when analyzing binary-masking algorithms.

Structure in time-frequency binary masking errors and its impact on speech intelligibility.

Although requiring prior knowledge makes the ideal binary mask an impractical algorithm, substantial increases in measured intelligibility make it a desirable benchmark. While this benchmark has been studied extensively, many questions remain about the factors that influence the intelligibility of binary-masked speech with non-ideal masks. To date, researchers have used primarily uniformly random, uncorrelated mask errors and independently presented error types (i.e., false positives and negatives) to characterize the influence of estimation errors on intelligibility. However, practical estimation algorithms produce masks that contain errors of both types and with non-trivial amounts of structure. This paper introduces an investigation framework for binary masks and presents listener studies that use this framework to illustrate how interactions between error types and structure affect intelligibility. First, this study demonstrates that clustering (i.e., a form of structure) of mask errors reduces intelligibility. Furthermore, while previous research has suggested that false positives are more detrimental to intelligibility than false negatives, this study indicates that false negatives can be equally detrimental to intelligibility when they contain structure or when both error types are present. Finally, this study shows that listeners tolerate fewer mask errors when both types of errors are present, especially when the errors contain structure.

Short-term memory capacity in networks via the restricted isometry property.

Cortical networks are hypothesized to rely on transient network activity to support short-term memory (STM). In this letter, we study the capacity of randomly connected recurrent linear networks for performing STM when the input signals are approximately sparse in some basis. We leverage results from compressed sensing to provide rigorous nonasymptotic recovery guarantees, quantifying the impact of the input sparsity level, the input sparsity basis, and the network characteristics on the system capacity. Our analysis demonstrates that network memory capacities can scale superlinearly with the number of nodes and in some situations can achieve STM capacities that are much larger than the network size. We provide perfect recovery guarantees for finite sequences and recovery bounds for infinite sequences. The latter analysis predicts that network STM systems may have an optimal recovery length that balances errors due to omission and recall mistakes. Furthermore, we show that the conditions yielding optimal STM capacity can be embodied in several network topologies, including networks with sparse or dense connectivities.

Visual nonclassical receptive field effects emerge from sparse coding in a dynamical system.

Extensive electrophysiology studies have shown that many V1 simple cells have nonlinear response properties to stimuli within their classical receptive field (CRF) and receive contextual influence from stimuli outside the CRF modulating the cell's response. Models seeking to explain these non-classical receptive field (nCRF) effects in terms of circuit mechanisms, input-output descriptions, or individual visual tasks provide limited insight into the functional significance of these response properties, because they do not connect the full range of nCRF effects to optimal sensory coding strategies. The (population) sparse coding hypothesis conjectures an optimal sensory coding approach where a neural population uses as few active units as possible to represent a stimulus. We demonstrate that a wide variety of nCRF effects are emergent properties of a single sparse coding model implemented in a neurally plausible network structure (requiring no parameter tuning to produce different effects). Specifically, we replicate a wide variety of nCRF electrophysiology experiments (e.g., end-stopping, surround suppression, contrast invariance of orientation tuning, cross-orientation suppression, etc.) on a dynamical system implementing sparse coding, showing that this model produces individual units that reproduce the canonical nCRF effects. Furthermore, when the population diversity of an nCRF effect has also been reported in the literature, we show that this model produces many of the same population characteristics. These results show that the sparse coding hypothesis, when coupled with a biophysically plausible implementation, can provide a unified high-level functional interpretation to many response properties that have generally been viewed through distinct mechanistic or phenomenological models.