Task-driven neural network models predict neural dynamics of proprioception.

Proprioception tells the brain the state of the body based on distributed sensory neurons. Yet, the principles that govern proprioceptive processing are poorly understood. Here, we employ a task-driven modeling approach to investigate the neural code of proprioceptive neurons in cuneate nucleus (CN) and somatosensory cortex area 2 (S1). We simulated muscle spindle signals through musculoskeletal modeling and generated a large-scale movement repertoire to train neural networks based on 16 hypotheses, each representing different computational goals. We found that the emerging, task-optimized internal representations generalize from synthetic data to predict neural dynamics in CN and S1 of primates. Computational tasks that aim to predict the limb position and velocity were the best at predicting the neural activity in both areas. Since task optimization develops representations that better predict neural activity during active than passive movements, we postulate that neural activity in the CN and S1 is top-down modulated during goal-directed movements.

Preserved neural dynamics across animals performing similar behaviour.

Animals of the same species exhibit similar behaviours that are advantageously adapted to their body and environment. These behaviours are shaped at the species level by selection pressures over evolutionary timescales. Yet, it remains unclear how these common behavioural adaptations emerge from the idiosyncratic neural circuitry of each individual. The overall organization of neural circuits is preserved across individuals1 because of their common evolutionarily specified developmental programme2-4. Such organization at the circuit level may constrain neural activity5-8, leading to low-dimensional latent dynamics across the neural population9-11. Accordingly, here we suggested that the shared circuit-level constraints within a species would lead to suitably preserved latent dynamics across individuals. We analysed recordings of neural populations from monkey and mouse motor cortex to demonstrate that neural dynamics in individuals from the same species are surprisingly preserved when they perform similar behaviour. Neural population dynamics were also preserved when animals consciously planned future movements without overt behaviour12 and enabled the decoding of planned and ongoing movement across different individuals. Furthermore, we found that preserved neural dynamics extend beyond cortical regions to the dorsal striatum, an evolutionarily older structure13,14. Finally, we used neural network models to demonstrate that behavioural similarity is necessary but not sufficient for this preservation. We posit that these emergent dynamics result from evolutionary constraints on brain development and thus reflect fundamental properties of the neural basis of behaviour.

Motor cortex latent dynamics encode spatial and temporal arm movement parameters independently.

The fluid movement of an arm requires multiple spatiotemporal parameters to be set independently. Recent studies have argued that arm movements are generated by the collective dynamics of neurons in motor cortex. An untested prediction of this hypothesis is that independent parameters of movement must map to independent components of the neural dynamics. Using a task where three male monkeys made a sequence of reaching movements to randomly placed targets, we show that the spatial and temporal parameters of arm movements are independently encoded in the low-dimensional trajectories of population activity in motor cortex: Each movement's direction corresponds to a fixed neural trajectory through neural state space and its speed to how quickly that trajectory is traversed. Recurrent neural network models show this coding allows independent control over the spatial and temporal parameters of movement by separate network parameters. Our results support a key prediction of the dynamical systems view of motor cortex, but also argue that not all parameters of movement are defined by different trajectories of population activitySignificance Statement From delicate strokes while drawing to ballistic swings while playing tennis, a skilled arm movement requires precise control of both its direction and speed. Motor cortex is thought to play a key role in controlling both, but it is unclear how they are jointly controlled. We show here that the population activity in motor cortex represents both the spatial and temporal properties of arm movements in the same low-dimensional signal. This representation was remarkably simple: the movement's direction is represented by the trajectory that signal takes; the movement's speed by how quickly the signal moves along its trajectory. Our network modelling shows this encoding allows an arm movement's direction and speed to be simultaneously and independently controlled.

A large-scale neural network training framework for generalized estimation of single-trial population dynamics.

Achieving state-of-the-art performance with deep neural population dynamics models requires extensive hyperparameter tuning for each dataset. AutoLFADS is a model-tuning framework that automatically produces high-performing autoencoding models on data from a variety of brain areas and tasks, without behavioral or task information. We demonstrate its broad applicability on several rhesus macaque datasets: from motor cortex during free-paced reaching, somatosensory cortex during reaching with perturbations, and dorsomedial frontal cortex during a cognitive timing task.

Sensory computations in the cuneate nucleus of macaques.

Tactile nerve fibers fall into a few classes that can be readily distinguished based on their spatiotemporal response properties. Because nerve fibers reflect local skin deformations, they individually carry ambiguous signals about object features. In contrast, cortical neurons exhibit heterogeneous response properties that reflect computations applied to convergent input from multiple classes of afferents, which confer to them a selectivity for behaviorally relevant features of objects. The conventional view is that these complex response properties arise within the cortex itself, implying that sensory signals are not processed to any significant extent in the two intervening structures-the cuneate nucleus (CN) and the thalamus. To test this hypothesis, we recorded the responses evoked in the CN to a battery of stimuli that have been extensively used to characterize tactile coding in both the periphery and cortex, including skin indentations, vibrations, random dot patterns, and scanned edges. We found that CN responses are more similar to their cortical counterparts than they are to their inputs: CN neurons receive input from multiple classes of nerve fibers, they have spatially complex receptive fields, and they exhibit selectivity for object features. Contrary to consensus, then, the CN plays a key role in processing tactile information.

Targeted Modulation of Human Brain Interregional Effective Connectivity With Spike-Timing Dependent Plasticity.

OBJECTIVE: The ability to selectively up- or downregulate interregional brain connectivity would be useful for research and clinical purposes. Toward this aim, cortico-cortical paired associative stimulation (ccPAS) protocols have been developed in which two areas are repeatedly stimulated with a millisecond-level asynchrony. However, ccPAS results in humans using bifocal transcranial magnetic stimulation (TMS) have been variable, and the mechanisms remain unproven. In this study, our goal was to test whether ccPAS mechanism is spike-timing-dependent plasticity (STDP). MATERIALS AND METHODS: Eleven healthy participants received ccPAS to the left primary motor cortex (M1) → right M1 with three different asynchronies (5 milliseconds shorter, equal to, or 5 milliseconds longer than the 9-millisecond transcallosal conduction delay) in separate sessions. To observe the neurophysiological effects, single-pulse TMS was delivered to the left M1 before and after ccPAS while cortico-cortical evoked responses were extracted from the contralateral M1 using source-resolved electroencephalography. RESULTS: Consistent with STDP mechanisms, the effects on synaptic strengths flipped depending on the asynchrony. Further implicating STDP, control experiments suggested that the effects were unidirectional and selective to the targeted connection. CONCLUSION: The results support the idea that ccPAS induces STDP and may selectively up- or downregulate effective connectivity between targeted regions in the human brain.

Estimating the dimensionality of the manifold underlying multi-electrode neural recordings.

It is generally accepted that the number of neurons in a given brain area far exceeds the number of neurons needed to carry any specific function controlled by that area. For example, motor areas of the human brain contain tens of millions of neurons that control the activation of tens or at most hundreds of muscles. This massive redundancy implies the covariation of many neurons, which constrains the population activity to a low-dimensional manifold within the space of all possible patterns of neural activity. To gain a conceptual understanding of the complexity of the neural activity within a manifold, it is useful to estimate its dimensionality, which quantifies the number of degrees of freedom required to describe the observed population activity without significant information loss. While there are many algorithms for dimensionality estimation, we do not know which are well suited for analyzing neural activity. The objective of this study was to evaluate the efficacy of several representative algorithms for estimating the dimensionality of linearly and nonlinearly embedded data. We generated synthetic neural recordings with known intrinsic dimensionality and used them to test the algorithms' accuracy and robustness. We emulated some of the important challenges associated with experimental data by adding noise, altering the nature of the embedding of the low-dimensional manifold within the high-dimensional recordings, varying the dimensionality of the manifold, and limiting the amount of available data. We demonstrated that linear algorithms overestimate the dimensionality of nonlinear, noise-free data. In cases of high noise, most algorithms overestimated the dimensionality. We thus developed a denoising algorithm based on deep learning, the "Joint Autoencoder", which significantly improved subsequent dimensionality estimation. Critically, we found that all algorithms failed when the intrinsic dimensionality was high (above 20) or when the amount of data used for estimation was low. Based on the challenges we observed, we formulated a pipeline for estimating the dimensionality of experimental neural data.

Cortical Tracking of Speech: Toward Collaboration between the Fields of Signal and Sentence Processing.

In recent years, a growing number of studies have used cortical tracking methods to investigate auditory language processing. Although most studies that employ cortical tracking stem from the field of auditory signal processing, this approach should also be of interest to psycholinguistics-particularly the subfield of sentence processing-given its potential to provide insight into dynamic language comprehension processes. However, there has been limited collaboration between these fields, which we suggest is partly because of differences in theoretical background and methodological constraints, some mutually exclusive. In this paper, we first review the theories and methodological constraints that have historically been prioritized in each field and provide concrete examples of how some of these constraints may be reconciled. We then elaborate on how further collaboration between the two fields could be mutually beneficial. Specifically, we argue that the use of cortical tracking methods may help resolve long-standing debates in the field of sentence processing that commonly used behavioral and neural measures (e.g., ERPs) have failed to adjudicate. Similarly, signal processing researchers who use cortical tracking may be able to reduce noise in the neural data and broaden the impact of their results by controlling for linguistic features of their stimuli and by using simple comprehension tasks. Overall, we argue that a balance between the methodological constraints of the two fields will lead to an overall improved understanding of language processing as well as greater clarity on what mechanisms cortical tracking of speech reflects. Increased collaboration will help resolve debates in both fields and will lead to new and exciting avenues for research.

Encoding of limb state by single neurons in the cuneate nucleus of awake monkeys.

The cuneate nucleus (CN) is among the first sites along the neuraxis where proprioceptive signals can be integrated, transformed, and modulated. The objective of the study was to characterize the proprioceptive representations in CN. To this end, we recorded from single CN neurons in three monkeys during active reaching and passive limb perturbation. We found that many neurons exhibited responses that were tuned approximately sinusoidally to limb movement direction, as has been found for other sensorimotor neurons. The distribution of their preferred directions (PDs) was highly nonuniform and resembled that of muscle spindles within individual muscles, suggesting that CN neurons typically receive inputs from only a single muscle. We also found that the responses of proprioceptive CN neurons tended to be modestly amplified during active reaching movements compared to passive limb perturbations, in contrast to cutaneous CN neurons whose responses were not systematically different in the active and passive conditions. Somatosensory signals thus seem to be subject to a "spotlighting" of relevant sensory information rather than uniform suppression as has been suggested previously.NEW & NOTEWORTHY The cuneate nucleus (CN) is the somatosensory gateway into the brain, and only recently has it been possible to record these signals from an awake animal. We recorded single CN neurons in monkeys. Proprioceptive CN neurons appear to receive input from very few muscles, and their sensitivity to movement changes reliably during reaching relative to passive arm perturbations. Sensitivity is generally increased, but not exclusively so, as though CN "spotlights" critical proprioceptive information during reaching.

Latent Factors and Dynamics in Motor Cortex and Their Application to Brain-Machine Interfaces.

In the 1960s, Evarts first recorded the activity of single neurons in motor cortex of behaving monkeys (Evarts, 1968). In the 50 years since, great effort has been devoted to understanding how single neuron activity relates to movement. Yet these single neurons exist within a vast network, the nature of which has been largely inaccessible. With advances in recording technologies, algorithms, and computational power, the ability to study these networks is increasing exponentially. Recent experimental results suggest that the dynamical properties of these networks are critical to movement planning and execution. Here we discuss this dynamical systems perspective and how it is reshaping our understanding of the motor cortices. Following an overview of key studies in motor cortex, we discuss techniques to uncover the "latent factors" underlying observed neural population activity. Finally, we discuss efforts to use these factors to improve the performance of brain-machine interfaces, promising to make these findings broadly relevant to neuroengineering as well as systems neuroscience.

A muscle-activity-dependent gain between motor cortex and EMG.

Whether one is delicately placing a contact lens on the surface of the eye or lifting a heavy weight from the floor, the motor system must produce a wide range of forces under different dynamical loads. How does the motor cortex, with neurons that have a limited activity range, function effectively under these widely varying conditions? In this study, we explored the interaction of activity in primary motor cortex (M1) and muscles (electromyograms, EMGs) of two male rhesus monkeys for wrist movements made during three tasks requiring different dynamical loads and forces. Despite traditionally providing adequate predictions in single tasks, in our experiments, a single linear model failed to account for the relation between M1 activity and EMG across conditions. However, a model with a gain parameter that increased with the target force remained accurate across forces and dynamical loads. Surprisingly, this model showed that a greater proportion of EMG changes were explained by the nonlinear gain than the linear mapping from M1. In addition to its theoretical implications, the strength of this nonlinearity has important implications for brain-computer interfaces (BCIs). If BCI decoders are to be used to control movement dynamics (including interaction forces) directly, they will need to be nonlinear and include training data from broad data sets to function effectively across tasks. Our study reinforces the need to investigate neural control of movement across a wide range of conditions to understand its basic characteristics as well as translational implications. NEW & NOTEWORTHY We explored the motor cortex-to-electromyogram (EMG) mapping across a wide range of forces and loading conditions, which we found to be highly nonlinear. A greater proportion of EMG was explained by a nonlinear gain than a linear mapping. This nonlinearity allows motor cortex to control the wide range of forces encountered in the real world. These results unify earlier observations and inform the next-generation brain-computer interfaces that will control movement dynamics and interaction forces.

A novel EEG paradigm to simultaneously and rapidly assess the functioning of auditory and visual pathways.

Objective assessment of the sensory pathways is crucial for understanding their development across the life span and how they may be affected by neurodevelopmental disorders (e.g., autism spectrum) and neurological pathologies (e.g., stroke, multiple sclerosis, etc.). Quick and passive measurements, for example, using electroencephalography (EEG), are especially important when working with infants and young children and with patient populations having communication deficits (e.g., aphasia). However, many EEG paradigms are limited to measuring activity from one sensory domain at a time, may be time consuming, and target only a subset of possible responses from that particular sensory domain (e.g., only auditory brainstem responses or only auditory P1-N1-P2 evoked potentials). Thus we developed a new multisensory paradigm that enables simultaneous, robust, and rapid (6-12 min) measurements of both auditory and visual EEG activity, including auditory brainstem responses, auditory and visual evoked potentials, as well as auditory and visual steady-state responses. This novel method allows us to examine neural activity at various stations along the auditory and visual hierarchies with an ecologically valid continuous speech stimulus, while an unrelated video is playing. Both the speech stimulus and the video can be customized for any population of interest. Furthermore, by using two simultaneous visual steady-state stimulation rates, we demonstrate the ability of this paradigm to track both parafoveal and peripheral visual processing concurrently. We report results from 25 healthy young adults, which validate this new paradigm.NEW & NOTEWORTHY A novel electroencephalography paradigm enables the rapid, reliable, and noninvasive assessment of neural activity along both auditory and visual pathways concurrently. The paradigm uses an ecologically valid continuous speech stimulus for auditory evaluation and can simultaneously track visual activity to both parafoveal and peripheral visual space. This new methodology may be particularly appealing to researchers and clinicians working with infants and young children and with patient populations with limited communication abilities.

Restoring sensorimotor function through intracortical interfaces: progress and looming challenges.

The loss of a limb or paralysis resulting from spinal cord injury has devastating consequences on quality of life. One approach to restoring lost sensory and motor abilities in amputees and patients with tetraplegia is to supply them with implants that provide a direct interface with the CNS. Such brain-machine interfaces might enable a patient to exert voluntary control over a prosthetic or robotic limb or over the electrically induced contractions of paralysed muscles. A parallel interface could convey sensory information about the consequences of these movements back to the patient. Recent developments in the algorithms that decode motor intention from neuronal activity and in approaches to convey sensory feedback by electrically stimulating neurons, using biomimetic and adaptation-based approaches, have shown the promise of invasive interfaces with sensorimotor cortices, although substantial challenges remain.

Wind speed reductions by large-scale wind turbine deployments lower turbine efficiencies and set low generation limits.

Wind turbines generate electricity by removing kinetic energy from the atmosphere. Large numbers of wind turbines are likely to reduce wind speeds, which lowers estimates of electricity generation from what would be presumed from unaffected conditions. Here, we test how well wind power limits that account for this effect can be estimated without explicitly simulating atmospheric dynamics. We first use simulations with an atmospheric general circulation model (GCM) that explicitly simulates the effects of wind turbines to derive wind power limits (GCM estimate), and compare them to a simple approach derived from the climatological conditions without turbines [vertical kinetic energy (VKE) estimate]. On land, we find strong agreement between the VKE and GCM estimates with respect to electricity generation rates (0.32 and 0.37 We m-2) and wind speed reductions by 42 and 44%. Over ocean, the GCM estimate is about twice the VKE estimate (0.59 and 0.29 We m-2) and yet with comparable wind speed reductions (50 and 42%). We then show that this bias can be corrected by modifying the downward momentum flux to the surface. Thus, large-scale limits to wind power use can be derived from climatological conditions without explicitly simulating atmospheric dynamics. Consistent with the GCM simulations, the approach estimates that only comparatively few land areas are suitable to generate more than 1 We m-2 of electricity and that larger deployment scales are likely to reduce the expected electricity generation rate of each turbine. We conclude that these atmospheric effects are relevant for planning the future expansion of wind power.

Hormetic Effects of Thiophanate-Methyl in Multiple Isolates of Sclerotinia homoeocarpa.

Twenty-eight isolates of Sclerotinia homoeocarpa, causal agent of dollar spot disease in turf, were assessed for fungicide hormesis at sublethal concentrations of thiophanate-methyl (T-methyl). Each isolate was grown in corn meal agar amended with 11 concentrations of T-methyl (30,500 to 0.047 µg/liter), and the area of mycelial growth was determined relative to the control. Three replicates were used per concentration, and the experiment was repeated three to five times for each isolate. Reference isolates (EC50 > 20 µg/liter), with no prior history of T-methyl exposure, were highly sensitive and not stimulated by low doses. Likewise, no stimulation was observed in two highly sensitive isolates (EC50 > 30 µg/liter) that had been preconditioned by exposure to T-methyl, or in four T-methyl-tolerant isolates. Seventeen (81%) preconditioned T-methyl-tolerant isolates (EC50 = 294 to1,550 µg/liter) had statistically significant growth stimulation, in the range of 2.8 to 19.7% relative to the control. These results support that hormesis (low-dose stimulation, high-dose inhibition) is a common dose response in preconditioned S. homoeocarpa, particularly in response to subtoxic doses of T-methyl.

Linear-nonlinear-time-warp-poisson models of neural activity.

Prominent models of spike trains assume only one source of variability - stochastic (Poisson) spiking - when stimuli and behavior are fixed. However, spike trains may also reflect variability due to internal processes such as planning. For example, we can plan a movement at one point in time and execute it at some arbitrary later time. Neurons involved in planning may thus share an underlying time course that is not precisely locked to the actual movement. Here we combine the standard Linear-Nonlinear-Poisson (LNP) model with Dynamic Time Warping (DTW) to account for shared temporal variability. When applied to recordings from macaque premotor cortex, we find that time warping considerably improves predictions of neural activity. We suggest that such temporal variability is a widespread phenomenon in the brain which should be modeled.

Two methods for estimating limits to large-scale wind power generation.

Wind turbines remove kinetic energy from the atmospheric flow, which reduces wind speeds and limits generation rates of large wind farms. These interactions can be approximated using a vertical kinetic energy (VKE) flux method, which predicts that the maximum power generation potential is 26% of the instantaneous downward transport of kinetic energy using the preturbine climatology. We compare the energy flux method to the Weather Research and Forecasting (WRF) regional atmospheric model equipped with a wind turbine parameterization over a 10(5) km2 region in the central United States. The WRF simulations yield a maximum generation of 1.1 We⋅m(-2), whereas the VKE method predicts the time series while underestimating the maximum generation rate by about 50%. Because VKE derives the generation limit from the preturbine climatology, potential changes in the vertical kinetic energy flux from the free atmosphere are not considered. Such changes are important at night when WRF estimates are about twice the VKE value because wind turbines interact with the decoupled nocturnal low-level jet in this region. Daytime estimates agree better to 20% because the wind turbines induce comparatively small changes to the downward kinetic energy flux. This combination of downward transport limits and wind speed reductions explains why large-scale wind power generation in windy regions is limited to about 1 We⋅m(-2), with VKE capturing this combination in a comparatively simple way.

Musculoskeletal geometry accounts for apparent extrinsic representation of paw position in dorsal spinocerebellar tract.

Proprioception, the sense of limb position and motion, arises from individual muscle receptors. An important question is how and where in the neuroaxis our high level "extrinsic" sense of limb movement originates. In the 1990s, a series of papers detailed the properties of neurons in the dorsal spinocerebellar tract (DSCT) of the cat. Despite their direct projections from sensory receptors, it appeared that half of these neurons had consistent, high-level tuning to paw position rather than to joint angles (or muscle lengths). These results suggested that many DSCT neurons compute paw position from lower level sensory information. We examined the contribution of musculoskeletal geometry to this apparent extrinsic representation by simulating a three-joint hindlimb with mono- and biarticular muscles, each providing a muscle spindlelike signal, modulated by the muscle length. We simulated neurons driven by randomly weighted combinations of these signals and moved the paw to different positions under two joint-covariance conditions similar to the original experiments. Our results paralleled those experiments in a number of respects: 1) Many neurons were tuned to paw position relative to the hip under both conditions. 2) The distribution of tuning was strongly bimodal, with most neurons driven by whole-leg flexion or extension. 3) The change in tuning between conditions clustered around zero (median absolute change ~20°). These results indicate that, at least for these constraint conditions, extrinsic-like representation can be achieved simply through musculoskeletal geometry and convergent muscle length inputs. Consequently, they suggest a reinterpretation of the earlier results may be required.NEW & NOTEWORTHY A classic experiment concluding that many dorsal spinocerebellar tract neurons encode paw position rather than joint angles has been cited by many studies as evidence for high-level computation occurring within a single synapse of the sensors. However, our study provides evidence that such a computation is not required to explain the results. Using simulation, we replicated many of the original results with purely random connectivity, suggesting that a reinterpretation of the classic experiment is needed.

Methodological considerations for a chronic neural interface with the cuneate nucleus of macaques.

While the response properties of neurons in the somatosensory nerves and anterior parietal cortex have been extensively studied, little is known about the encoding of tactile and proprioceptive information in the cuneate nucleus (CN) or external cuneate nucleus (ECN), the first recipients of upper limb somatosensory afferent signals. The major challenge in characterizing neural coding in CN/ECN has been to record from these tiny, difficult-to-access brain stem structures. Most previous investigations of CN response properties have been carried out in decerebrate or anesthetized animals, thereby eliminating the well-documented top-down signals from cortex, which likely exert a strong influence on CN responses. Seeking to fill this gap in our understanding of somatosensory processing, we describe an approach to chronically implanting arrays of electrodes in the upper limb representation in the brain stem in primates. First, we describe the topography of CN/ECN in rhesus macaques, including its somatotopic organization and the layout of its submodalities (touch and proprioception). Second, we describe the design of electrode arrays and the implantation strategy to obtain stable recordings. Third, we show sample responses of CN/ECN neurons in brain stem obtained from awake, behaving monkeys. With this method, we are in a position to characterize, for the first time, somatosensory representations in CN and ECN of primates.NEW & NOTEWORTHY In primates, the neural basis of touch and of our sense of limb posture and movements has been studied in the peripheral nerves and in somatosensory cortex, but coding in the cuneate and external cuneate nuclei, the first processing stage for these signals in the central nervous system, remains an enigma. We have developed a method to record from these nuclei, thereby paving the way to studying how sensory information from the limb is encoded there.

Fyn kinase regulates translation in mammalian mitochondria.

BACKGROUND: Mitochondrial translation machinery solely exists for the synthesis of 13 mitochondrially-encoded subunits of the oxidative phosphorylation (OXPHOS) complexes in mammals. Therefore, it plays a critical role in mitochondrial energy production. However, regulation of the mitochondrial translation machinery is still poorly understood. In comprehensive proteomics studies with normal and diseased tissues and cell lines, we and others have found the majority of mitochondrial ribosomal proteins (MRPs) to be phosphorylated. Neither the kinases for these phosphorylation events nor their specific roles in mitochondrial translation are known. METHODS: Mitochondrial kinases are responsible for phosphorylation of MRPs enriched from bovine mitoplasts by strong cation-exchange chromatography and identified by mass spectrometry-based proteomics analyses of kinase rich fractions. Phosphorylation of recombinant MRPs and 55S ribosomes was assessed by in vitro phosphorylation assays using the kinase-rich fractions. The effect of identified kinase on OXPHOS and mitochondrial translation was assessed by various cell biological and immunoblotting approaches. RESULTS: Here, we provide the first evidence for the association of Fyn kinase, a Src family kinase, with mitochondrial translation components and its involvement in phosphorylation of 55S ribosomal proteins in vitro. Modulation of Fyn expression in human cell lines has provided a link between mitochondrial translation and energy metabolism, which was evident by the changes in 13 mitochondrially encoded subunits of OXPHOS complexes. CONCLUSIONS AND GENERAL SIGNIFICANCE: Our findings suggest that Fyn kinase is part of a complex mechanism that regulates protein synthesis and OXPHOS possibly by tyrosine phosphorylation of translation components in mammalian mitochondria.