Single Neurons Research Articles

Barcode activity in a recurrent network model of the hippocampus enables efficient memory binding.

Forming an episodic memory requires binding together disparate elements that co-occur in a single experience. One model of this process is that neurons representing different components of a memory bind to an "index" - a subset of neurons unique to that memory. Evidence for this model has recently been found in chickadees, which use hippocampal memory to store and recall locations of cached food. Chickadee hippocampus produces sparse, high-dimensional patterns ("barcodes") that uniquely specify each caching event. Unexpectedly, the same neurons that participate in barcodes also exhibit conventional place tuning. It is unknown how barcode activity is generated, and what role it plays in memory formation and retrieval. It is also unclear how a memory index (e.g. barcodes) could function in the same neural population that represents memory content (e.g. place). Here, we design a biologically plausible model that generates barcodes and uses them to bind experiential content. Our model generates barcodes from place inputs through the chaotic dynamics of a recurrent neural network and uses Hebbian plasticity to store barcodes as attractor states. The model matches experimental observations that memory indices (barcodes) and content signals (place tuning) are randomly intermixed in the activity of single neurons. We demonstrate that barcodes reduce memory interference between correlated experiences. We also show that place tuning plays a complementary role to barcodes, enabling flexible, contextually-appropriate memory retrieval. Finally, our model is compatible with previous models of the hippocampus as generating a predictive map. Distinct predictive and indexing functions of the network are achieved via an adjustment of global recurrent gain. Our results suggest how the hippocampus may use barcodes to resolve fundamental tensions between memory specificity (pattern separation) and flexible recall (pattern completion) in general memory systems.

Neuromodulation in Small Animal fMRI.

The integration of functional magnetic resonance imaging (fMRI) with advanced neuroscience technologies in experimental small animal models offers a unique path to interrogate the causal relationships between regional brain activity and brain-wide network measures-a goal challenging to accomplish in human subjects. This review traces the historical development of the neuromodulation techniques commonly used in rodents, such as electrical deep brain stimulation, optogenetics, and chemogenetics, and focuses on their application with fMRI. We discuss their advantageousness roles in uncovering the signaling architecture within the brain and the methodological considerations necessary when conducting these experiments. By presenting several rodent-based case studies, we aim to demonstrate the potential of the multimodal neuromodulation approach in shedding light on neurovascular coupling, the neural basis of brain network functions, and their connections to behaviors. Key findings highlight the cell-type and circuit-specific modulation of brain-wide activity patterns and their behavioral correlates. We also discuss several future directions and feature the use of mediation and moderation analytical models beyond the intuitive evoked response mapping, to better leverage the rich information available in fMRI data with neuromodulation. Using fMRI alongside neuromodulation techniques provide insights into the mesoscopic (relating to the intermediate scale between single neurons and large-scale brain networks) and macroscopic fMRI measures that correlate with specific neuronal events. This integration bridges the gap between different scales of neuroscience research, facilitating the exploration and testing of novel therapeutic strategies aimed at altering network-mediated behaviors. In conclusion, the combination of fMRI with neuromodulation techniques provides crucial insights into mesoscopic and macroscopic brain dynamics, advancing our understanding of brain function in health and disease. EVIDENCE LEVEL: 1 TECHNICAL EFFICACY: Stage 1.

104 Non-invasive pregnancy diagnosis in Holstein dairy cows using computer vision analysis of thermal images

Abstract Thermal visualization of Holstein dairy cattle skin can be indicative of the pregnancy state (cyclic or pregnant) of the cow by observing the size and shape of skin thermal patterns. However, visual assessment of skin thermal patterns can be subjective and laborious. Further, infrared thermography implementation on-farm requires automated analysis to be feasible for dairy producers. The objectives of this study were to implement a machine learning convolutional neural network (CNN) to detect features in thermal images associated with pregnant (Pregnant) and non-pregnant (Cyclic) and to evaluate the accuracy of pregnancy diagnosis in dairy cows. A total of 938 images from 18 Pregnant cows and 982 images from 18 Cyclic were used to build the CNN model, and 120 images from Pregnant (60 images) and Cyclic (60 images) dairy cows were used to validate the model. Pregnant cows were confirmed to be pregnant 90 d after insemination via transrectal ultrasonography. Cyclic cows were confirmed to be non-pregnant using follicular dynamics and corpora lutea presence via transrectal ultrasonography. Images were recorded using a T620s infrared camera at 640 × 480 (307,200) to train the CNN model and images from an E40 FLIR camera at 320 x 240 (76,800 pixels) were used for the validation test. The CNN model was created using TensorFlow in Google Colab to binary classify images into Pregnant or Cyclic using a single output neuron (1: Pregnant and 0: Cyclic) with different Epochs (10, 25, and 100). The diagnosis accuracy (acc) was 0.72 with a validation accuracy (val_acc) of 0.41 for 10 Epochs, acc 0.76 with val_acc 0.37 for 25 Epochs, and acc 0.80 with val_acc 0.63. The acc and val_acc of the training model and validation test were optimized when using images from the T620s exclusively (Epochs 10: acc = 0.70 and val_acc = 0.64; Epoch 25: acc = 0.74 and val_acc = 0.70; Epoch 100: acc = 0.81 and val_acc = 0.80). Greater resolution of thermal images was proven to increase the accuracy of pregnancy diagnosis (307,200 compared with 76,800 pixels) and the greater number of Epoch used to train the model. Machine learning algorithms can diagnose pregnancy using thermal images with an acceptable evaluation accuracy (> 0.70).

Electrophysiological correlates of divergent projections in the avian superior olivary nucleus.

The physiological diversity of inhibitory neurons provides ample opportunity to influence a wide range of computational roles through their varied activity patterns, especially via feedback loops. In the avian auditory brainstem, inhibition originates primarily from the superior olivary nucleus (SON) and so it is critical to understand the intrinsic physiological properties and processing capabilities of these neurons. Neurons in the SON receive ascending input via the cochlear nuclei: directly from the intensity-coding cochlear nucleus angularis (NA) and indirectly via the interaural timing nucleus laminaris (NL), which itself receives input from cochlear nucleus magnocellularis. Two distinct populations of SON neurons provide either inhibitory feedback to ipsilateral NA, NL, and the timing cochlear nucleus NM, or to the contralateral SON. To determine whether these populations correspond to distinct response types, we investigated their electrophysiology in brain stem slices using patch clamp electrophysiology. We identified three phenotypes: single spiking, chattering tonic, and regular tonic neurons. The two tonic phenotypes displayed distinct firing patterns and different membrane properties. Fluctuating "noisy" currents used to probe the capability of SON neurons to encode temporal features showed that each phenotype differed in sensitivity to temporally modulated input. By using cell fills and anatomical reconstructions, we could correlate the firing phenotypes with their axonal projection patterns. We found that SON axons exited via three fiber tracts with each tract composed of specific phenotypes. These results provide a basis for understanding the role of specific inhibitory cell types in auditory function and elucidate the organization of the SON outputs.

Enhanced prediction of corrosion rates of pipeline steels using simulated annealing-optimized ANFIS models

Accurate prediction of corrosion rates is crucial for preventing infrastructure failures, reducing maintenance costs, and ensuring operational safety. Traditional models often struggle to account for the complex, non-linear interactions between environmental factors and material properties. This study presents a novel approach integrating Simulated Annealing (SA) with an Adaptive Network-based Fuzzy Inference System (ANFIS) to improve corrosion rate predictions for pipeline steels. The SA-ANFIS model features six input neurons representing temperature, H₂S pressure, CO₂ pressure, salinity, moisture content, and material type. These factors influence corrosion rates, represented by a single output neuron. The SA algorithm optimizes the ANFIS model's parameters, enhancing its ability to handle non-linear relationships. Historical corrosion data for P110SS, L80, and 2205 Duplex steel were used, incorporating environmental variables such as temperature, pH, and gas pressures. The SA-ANFIS model achieved superior accuracy, with a maximum error of 2.8424 % and an average error of 1.2536 %, outperforming the GA-ANFIS model and conventional ANFIS and SVR models. The SA-ANFIS model offers a robust, optimized tool for predicting corrosion in petroleum pipelines, significantly improving prediction accuracy under harsh conditions.

Rewireable Building Blocks for Enzyme-Powered DNA Computing Networks.

Neural networks enable the processing of large, complex data sets with applications in disease diagnosis, cell profiling, and drug discovery. Beyond electronic computers, neural networks have been implemented using programmable biomolecules such as DNA; this confers unique advantages, such as greater portability, electricity-free operation, and direct analysis of patterns of biomolecules in solution. Analogous to bottlenecks in electronic computers, the computing power of DNA-based neural networks is limited by the ability to add more computing units, i.e., neurons. This limitation exists because current architectures require many nucleic acids to model a single neuron. Each additional neuron compounds existing problems such as long assembly times, high background signal, and cross-talk between components. Here, we test three strategies to solve this limitation and improve the scalability of DNA-based neural networks: (i) enzymatic synthesis for high-purity neurons, (ii) spatial patterning of neuron clusters based on their network position, and (iii) encoding neuron connectivity on a universal single-stranded DNA backbone. We show that neurons implemented via these strategies activate quickly, with a high signal-to-background ratio and process-weighted inputs. We rewired our modular neurons to demonstrate basic neural network motifs such as cascading, fan-in, and fan-out circuits. Finally, we designed a prototype two-layer microfluidic device to automate the operation of our circuits. We envision that our proposed design will help scale DNA-based neural networks due to its modularity, simplicity of synthesis, and compatibility with various neural network architectures. This will enable portable computing power for applications in portable diagnostics, compact data storage, and autonomous decision making for lab-on-a-chips.

Concept and location neurons in the human brain provide the ‘what’ and ‘where’ in memory formation

Our brains create new memories by capturing the ‘who/what’, ‘where’ and ‘when’ of everyday experiences. On a neuronal level, mechanisms facilitating a successful transfer into episodic memory are still unclear. We investigated this by measuring single neuron activity in the human medial temporal lobe during encoding of item-location associations. While previous research has found predictive effects in population activity in human MTL structures, we could attribute such effects to two specialized sub-groups of neurons: concept cells in the hippocampus, amygdala and entorhinal cortex (EC), and a second group of parahippocampal location-selective neurons. In both item- and location-selective populations, firing rates were significantly higher during successfully encoded trials. These findings are in line with theories of hippocampal indexing, since selective index neurons may act as pointers to neocortical representations. Overall, activation of distinct populations of neurons could directly support the connection of the ‘what’ and ‘where’ of episodic memory.

Dynamic Effects Analysis in Fractional Memristor-Based Rulkov Neuron Model.

Mathematical models such as Fitzhugh-Nagoma and Hodgkin-Huxley models have been used to understand complex nervous systems. Still, due to their complexity, these models have made it challenging to analyze neural function. The discrete Rulkov model allows the analysis of neural function to facilitate the investigation of neuronal dynamics or others. This paper introduces a fractional memristor Rulkov neuron model and analyzes its dynamic effects, investigating how to improve neuron models by combining discrete memristors and fractional derivatives. These improvements include the more accurate generation of heritable properties compared to full-order models, the treatment of dynamic firing activity at multiple time scales for a single neuron, and the better performance of firing frequency responses in fractional designs compared to integer models. Initially, we combined a Rulkov neuron model with a memristor and evaluated all system parameters using bifurcation diagrams and the 0-1 chaos test. Subsequently, we applied a discrete fractional-order approach to the Rulkov memristor map. We investigated the impact of all parameters and the fractional order on the model and observed that the system exhibited various behaviors, including tonic firing, periodic firing, and chaotic firing. We also found that the more I tend towards the correct order, the more chaotic modes in the range of parameters. Following this, we coupled the proposed model with a similar one and assessed how the fractional order influences synchronization. Our results demonstrated that the fractional order significantly improves synchronization. The results of this research emphasize that the combination of memristor and discrete neurons provides an effective tool for modeling and estimating biophysical effects in neurons and artificial neural networks.

2P-NucTag: on-demand phototagging for molecular analysis of functionally identified cortical neurons.

Neural circuits are characterized by genetically and functionally diverse cell types. A mechanistic understanding of circuit function is predicated on linking the genetic and physiological properties of individual neurons. However, it remains highly challenging to map the transcriptional properties to functionally heterogeneous neuronal subtypes in mammalian cortical circuits in vivo. Here, we introduce a high-throughput two-photon nuclear phototagging (2P-NucTag) approach optimized for on-demand and indelible labeling of single neurons via a photoactivatable red fluorescent protein following in vivo functional characterization in behaving mice. We demonstrate the utility of this function-forward pipeline by selectively labeling and transcriptionally profiling previously inaccessible 'place' and 'silent' cells in the mouse hippocampus. Our results reveal unexpected differences in gene expression between these hippocampal pyramidal neurons with distinct spatial coding properties. Thus, 2P-NucTag opens a new way to uncover the molecular principles that govern the functional organization of neural circuits.

Exploration on the spiking response of a single compartment neuron with multiple active properties under electrical stimuli

The study of modulating the neuronal signals by electrical stimuli is important to intervene the abnormal neuronal firing and bring them to a normal state. Spike trains, which are the highest quality of brain signals, have been deficiently explored and analyzed owing to the challenges of obtaining them in reality. In this regard, this paper aims to investigate and analyze the effect of electrical stimuli on the spiking response of neurons, and the following work is to be carried out. The relationships between the spiking response and three parameters (namely, the amplitude of the electrode current (EC), the angular velocity of the electric field current (EFC), and the signal-noise ratio (SNR)) are examined on a neuronal model with spatial length and multiple active properties. When specific currents with different SNRs are imposed on the neurons, their influence on the spiking response is further explored. With regard to the spiking response, the main focus is on three characteristics, i.e., the spiking pattern, the spike count (SC), and the spiking arrangement. An algorithm, called the return map distance (RMD) algorithm, is proposed in this paper, and gives the classification of spiking patterns a quantitative criterion. Based on it, the spiking patterns are classified in this paper as busting spike train, regular spike train (RST), and meager spike train (MST). Simulation results indicate that both the amplitude of the EC and the angular velocity of the EFC change the neuronal spiking patterns. As the amplitude (angular velocity) of the EC (EFC) increases, the spiking pattern of the Soldado-Magraner model (SMM) eventually tends to RST (MST). In addition, the SC increases with the amplitude of the EC, while it does not hold for the SC with respect to the angular velocity of the EFC. Furthermore, the spiking arrangement and the SC are severely destroyed for the EC with low SNRs, while three spiking features of the SMM under EFC are all robust to the different SNRs, which implies that compared with the EC, the spiking responses of the SMM under EFC are more stable. The findings in this paper may provide some theoretical guidance to the fields related to neuronal firing, such as brain-computer interfaces and electrotherapy. The RMD algorithm proposed here can be applied to more individual neurons, and the spiking arrangement discussed here could be regarded as an effective encoding way for spike trains.

Slc35a2 mosaic knockout impacts cortical development, dendritic arborisation, and neuronal firing

Mild malformation of cortical development with oligodendroglial hyperplasia in epilepsy (MOGHE) is an important cause of drug-resistant epilepsy. A significant subset of individuals diagnosed with MOGHE display somatic mosaicism for loss-of-function variants in SLC35A2, which encodes the UDP-galactose transporter. We developed a mouse model to investigate how disruption of this transporter leads to a malformation of cortical development. We used in utero electroporation and CRISPR/Cas9 to knockout Slc35a2 in a subset of layer 2/3 cortical neuronal progenitors in the developing brains of male and female fetal mice to model mosaic expression. Mosaic Slc35a2 knockout was verified through next-generation sequencing and immunohistochemistry of GFP-labelled transfected cells. Histology of brain tissue in mosaic Slc35a2 knockout mice revealed the presence of upper layer-derived cortical neurons in the white matter. Reconstruction of single filled neurons identified altered dendritic arborisation with Slc35a2 knockout neurons having increased complexity. Whole-cell electrophysiological recordings revealed that Slc35a2 knockout neurons display reduced action potential firing, increased afterhyperpolarisation duration and reduced burst-firing when compared with control neurons. Mosaic Slc35a2 knockout mice also exhibited significantly increased epileptiform spiking and increased locomotor activity. We successfully generated a mouse model of mosaic Slc35a2 deficiency, which recapitulates features of the human phenotype, including impaired neuronal migration. We show that knockout in layer 2/3 cortical neuron progenitors is sufficient to disrupt neuronal excitability, increase epileptiform activity and cause hyperactivity in mosaic mice. Our mouse model provides an opportunity to further investigate the disease mechanisms that contribute to MOGHE and facilitate the development of precision therapies.

Parametric controllable planar multi-scroll chaotic attractors in a 3-D memristive tabu learning single neuron model

In this paper, a three-dimensional (3D) autonomous tabu learning single neuron model is proposed, which is achieved by using a sinusoidal activation function and introducing a memristor synapse. This model exhibits the remarkable capability to produce a series of planar multi-scroll chaotic attractors, and its unique feature lies in the ability to control the number of scrolls. The investigation of the planar multi-scroll chaotic attractors and its dynamical behaviors is conducted through the analysis of phase plane portraits, bifurcation diagrams, and spectral entropies. The numerical simulations unveil a compelling relationship between the number of chaotic scrolls and specific control parameters governing the model. To further validate the findings, a 3D autonomous tabu learning single neuron model is implemented on a digital hardware platform. In an effort to extend the practical significance of this research, the multi-scroll chaotic phenomenon generated by the proposed model is deployed for image encryption. The fusion of mathematical modeling, digital hardware implementation, and practical application underscores the universality and significance of the proposed single neuron model in the fields of chaotic systems and engineering applications.

Synaptic imbalance and increased inhibition impair motor function in SMA.

Movement is executed through the balanced action of excitatory and inhibitory neurotransmission in motor circuits of the spinal cord. Short-term perturbations in one of the two types of transmission are counteracted by homeostatic changes of the opposing type. Prolonged failure to balance excitatory and inhibitory drive results in dysfunction at the single neuron, as well as neuronal network levels. However, whether dysfunction in one or both types of neurotransmission leads to pathogenicity in neurodegenerative diseases characterized by select synaptic deficits is not known. Here, we used mouse genetics, functional assays, morphological methods, and viral-mediated approaches to uncover the pathogenic contribution of unbalanced excitation-inhibition neurotransmission in a mouse model of spinal muscular atrophy (SMA). We show that vulnerable motor circuits in the SMA spinal cord fail to respond homeostatically to the reduction of excitatory drive and instead increase inhibition. This imposes an excessive burden on motor neurons and further restricts their recruitment to activate muscle contraction. Importantly, genetic or pharmacological reduction of inhibitory synaptic drive improves neuronal function and provides behavioural benefit in SMA mice. Our findings identify the lack of excitation-inhibition homeostasis as a major maladaptive mechanism in SMA, by which the combined effects of reduced excitation and increased inhibition diminish the capacity of premotor commands to recruit motor neurons and elicit muscle contractions.

Model-agnostic neural mean field with a data-driven transfer function

As one of the most complex systems known to science, modeling brain behavior and function is both fascinating and extremely difficult. Empirical data is increasingly available from ex vivo human brain organoids and surgical samples, as well as in vivo animal models, so the problem of modeling the behavior of large-scale neuronal systems is more relevant than ever. The statistical physics concept of a mean-field model offers a tractable way to bridge the gap between single-neuron and population-level descriptions of neuronal activity, by modeling the behavior of a single representative neuron and extending this to the population. However, existing neural mean-field methods typically either take the limit of small interaction sizes, or are applicable only to the specific neuron models for which they were derived. This paper derives a mean-field model by fitting a transfer function called Refractory SoftPlus, which is simple yet applicable to a broad variety of neuron types. The transfer function is fitted numerically to simulated spike time data, and is entirely agnostic to the underlying neuronal dynamics. The resulting mean-field model predicts the response of a network of randomly connected neurons to a time-varying external stimulus with a high degree of accuracy. Furthermore, it enables an accurate approximate bifurcation analysis as a function of the level of recurrent input. This model does not assume large presynaptic rates or small postsynaptic potential size, allowing mean-field models to be developed even for populations with large interaction terms.

Functional segregation and dynamic integration of the corticotectal descending signal in rat

The superior colliculus (SC) receives inputs from various brain regions in a layer- and radial subregion-specific manner, but whether the SC exhibits subregion-specific dynamics remains unclear. To address this issue, we recorded the spiking activity of single SC neurons while photoactivating cortical areas in awake head-fixed Thy1-ChR2 rats. We classified 309 neurons that responded significantly into 8 clusters according to the response dynamics. Among them, neurons with monophasic excitatory responses (7–12 ms latency) that returned to baseline within 20 ms were commonly observed in the optic and intermediate gray layers of centromedial and centrolateral SC. In contrast, neurons with complex polyphasic responses were commonly observed in the deep layers of the anterolateral SC. Cross-correlation analysis suggested that the complex pattern could be only partly explained by an internal circuit of the deep gray layer. Our results indicate that medial to centrolateral SC neurons simply relay cortical activity, whereas neurons in the deep layers of the anterolateral SC dynamically integrate inputs from the cortex, SNr, CN, and local circuits. These findings suggest a spatial gradient in SC integration, with a division of labor between simple relay circuits and those integrating complex dynamics.

Gate-based quantum neurons in hybrid neural networks

Abstract Quantum computing is conceived as a promising and powerful next-generation platform for information processing and it has been shown that it could bring significant accelerations to certain tasks, compared to its classical counterparts. With recent advances in noisy intermediate-scale quantum (NISQ) devices, we can process classical data from real-world problems using hybrid quantum systems. In this work, we investigate the critical problem of designing a gate-based hybrid quantum neuron under NISQ constraints to enable the construction of scalable hybrid quantum deep neural networks (HQDNNs). We explore and characterize diverse quantum circuits for hybrid quantum neurons and discuss related critical components of HQDNNs. We also utilize a new schema to infer multiple predictions from a single hybrid neuron. We further compose a highly customizable platform for simulating HQDNNs via Qiskit and test them on diverse classification problems including the iris and the wheat seed datasets. The results show that even HQDNNs with the simplest neurons could lead to superior performance on these tasks. Finally, we show that the HQDNNs are robust to certain levels of noise, making them preferred on NISQ devices. Our work provides a comprehensive investigation of building scalable near-term gate-based HQDNNs and paves the way for future studies of quantum deep learning via both simulations on classical computers and experiments on accessible NISQ devices.

Decision-making shapes dynamic inter-areal communication within macaque ventral frontal cortex

Macaque ventral frontal cortex is composed of a set of anatomically heterogeneous and highly interconnected areas. Collectively, these areas have been implicated in many higher-level affective and cognitive processes, most notably the adaptive control of decision-making. Despite this appreciation, little is known about how subdivisions of ventral frontal cortex dynamically interact with each other during decision-making. Here, we assessed functional interactions between areas by analyzing the activity of thousands of single neurons recorded from eight anatomically defined subdivisions of ventral frontal cortex in macaques performing a visually guided two-choice probabilistic task for different fruit juices. We found that the onset of stimuli and reward delivery globally increased communication between all parts of ventral frontal cortex. Inter-areal communication was, however, temporally specific, occurred through unique activity subspaces between areas, and depended on the encoding of decision variables. In particular, areas 12l and 12o showed the highest connectivity with other areas while being more likely to receive information from other parts of ventral frontal cortex than to send it. This pattern of functional connectivity suggests a role for these two areas in integrating diverse sources of information during decision processes. Taken together, our work reveals the specific patterns of inter-areal communication between anatomically connected subdivisions of ventral frontal cortex that are dynamically engaged during decision-making.

Perpetual step-like restructuring of hippocampal circuit dynamics

Representation of the environment by hippocampal populations is known to drift even within a familiar environment, which could reflect gradual changes in single-cell activity or result from averaging across discrete switches of single neurons. Disambiguating these possibilities is crucial, as they each imply distinct mechanisms. Leveraging change point detection and model comparison, we find that CA1 population vectors decorrelate gradually within a session. In contrast, individual neurons exhibit predominantly step-like emergence and disappearance of place fields or sustained changes in within-field firing. The changes are not restricted to particular parts of the maze or trials and do not require apparent behavioral changes. The same place fields emerge, disappear, and reappear across days, suggesting that the hippocampus reuses pre-existing assemblies, rather than forming new fields de novo. Our results suggest an internally driven perpetual step-like reorganization of the neuronal assemblies.

A new single multiplicative neuron model artificial neural network based on black hole optimization algorithm: forecasting the amounts of clean water given to metropolis

A new single multiplicative neuron model artificial neural network based on black hole optimization algorithm: forecasting the amounts of clean water given to metropolis

What can neuroimaging of neuromodulation reveal about the basis of circuit therapies for psychiatry?

Neuromodulation is increasingly becoming a therapeutic option for treatment resistant psychiatric disorders. These non-invasive and invasive therapies are still being refined but are clinically effective and, in some cases, provide sustained symptom reduction. Neuromodulation relies on changing activity within a specific brain region or circuit, but the precise mechanisms of action of these therapies, is unclear. Here we review work in both humans and animals that has provided insight into how therapies such as deep brain and transcranial magnetic stimulation alter neural activity across the brain. We focus on studies that have combined neuromodulation with neuroimaging such as PET and MRI as these measures provide detailed information about the distributed networks that are modulated and thus insight into both the mechanisms of action of neuromodulation but also potentially the basis of psychiatric disorders. Further we highlight work in nonhuman primates that has revealed how neuromodulation changes neural activity at different scales from single neuron activity to functional connectivity, providing key insight into how neuromodulation influences the brain. Ultimately, these studies highlight the value of combining neuromodulation with neuroimaging to reveal the mechanisms through which these treatments influence the brain, knowledge vital for refining targeted neuromodulation therapies for psychiatric disorders.