Shy Shoham

Intramembrane cavitation as a unifying mechanism for ultrasound-induced bioeffects

The purpose of this study was to develop a unified model capable of explaining the mechanisms of interaction of ultrasound and biological tissue at both the diagnostic nonthermal, noncavitational (<100 mW·cm−2) and therapeutic, potentially cavitational (>100 mW·cm−2) spatial peak temporal average intensity levels. The cellular-level model (termed “bilayer sonophore”) combines the physics of bubble dynamics with cell biomechanics to determine the dynamic behavior of the two lipid bilayer membrane leaflets. The existence of such a unified model could potentially pave the way to a number of controlled ultrasound-assisted applications, including CNS modulation and blood–brain barrier permeabilization. The model predicts that the cellular membrane is intrinsically capable of absorbing mechanical energy from the ultrasound field and transforming it into expansions and contractions of the intramembrane space. It further predicts that the maximum area strain is proportional to the acoustic pressure amplitude and inversely proportional to the square root of the frequency () and is intensified by proximity to free surfaces, the presence of nearby microbubbles in free medium, and the flexibility of the surrounding tissue. Model predictions were experimentally supported using transmission electron microscopy (TEM) of multilayered live-cell goldfish epidermis exposed in vivo to continuous wave (CW) ultrasound at cavitational (1 MHz) and noncavitational (3 MHz) conditions. Our results support the hypothesis that ultrasonically induced bilayer membrane motion, which does not require preexistence of air voids in the tissue, may account for a variety of bioeffects and could elucidate mechanisms of ultrasound interaction with biological tissue that are currently not fully understood.

Neural substrates of tactile object recognition ; An fMRI study

A functional magnetic resonance imaging (fMRI) study was conducted during which seven subjects carried out naturalistic tactile object recognition (TOR) of real objects. Activation maps, conjunctions across subjects, were compared between tasks involving TOR of common real objects, palpation of “nonsense” objects, and rest. The tactile tasks involved similar motor and sensory stimulation, allowing higher tactile recognition processes to be isolated. Compared to nonsense object palpation, the most prominent activation evoked by TOR was in secondary somatosensory areas in the parietal operculum (SII) and insula, confirming a modality‐specific path for TOR. Prominent activation was also present in medial and lateral secondary motor cortices, but not in primary motor areas, supporting the high level of sensory and motor integration characteristic of object recognition in the tactile modality. Activation in a lateral occipitotemporal area associated previously with visual object recognition may support cross‐modal collateral activation. Finally, activation in medial temporal and prefrontal areas may reflect a common final pathway of modality‐independent object recognition. This study suggests that TOR involves a complex network including parietal and insular somatosensory association cortices, as well as occipitotemporal visual areas, prefrontal, and medial temporal supramodal areas, and medial and lateral secondary motor cortices. It confirms the involvement of somatosensory association areas in the recognition component of TOR, and the existence of a ventrolateral somatosensory pathway for TOR in intact subjects. It challenges the results of previous studies that emphasize the role of visual cortex rather than somatosensory association cortices in higher‐level somatosensory cognition. Hum. Brain Mapping 21:236–246, 2004.

How silent is the brain: is there a “dark matter” problem in neuroscience?

Evidence from a variety of recording methods suggests that many areas of the brain are far more sparsely active than commonly thought. Here, we review experimental findings pointing to the existence of neurons which fire action potentials rarely or only to very specific stimuli. Because such neurons would be difficult to detect with the most common method of monitoring neural activity in vivo—extracellular electrode recording—they could be referred to as “dark neurons,” in analogy to the astrophysical observation that much of the matter in the universe is undetectable, or dark. In addition to discussing the evidence for largely silent neurons, we review technical advances that will ultimately answer the question: how silent is the brain?

Superlinear population encoding of dynamic hand trajectory in primary motor cortex.

Neural activity in primary motor cortex (MI) is known to correlate with hand position and velocity. Previous descriptions of this tuning have (1) been linear in position or velocity, (2) depended only instantaneously on these signals, and/or (3) not incorporated the effects of interneuronal dependencies on firing rate. We show here that many MI cells encode a superlinear function of the full time-varying hand trajectory. Approximately 20% of MI cells carry information in the hand trajectory beyond just the position, velocity, and acceleration at a single time lag. Moreover, approximately one-third of MI cells encode the trajectory in a significantly superlinear manner; as one consequence, even small position changes can dramatically modulate the gain of the velocity tuning of MI cells, in agreement with recent psychophysical evidence. We introduce a compact nonlinear “preferred trajectory” model that predicts the complex structure of the spatiotemporal tuning functions described in previous work. Finally, observing the activity of neighboring cells in the MI network significantly increases the predictability of the firing rate of a single MI cell; however, we find interneuronal dependencies in MI to be much more locked to external kinematic parameters than those described recently in the hippocampus. Nevertheless, this neighbor activity is approximately as informative as the hand velocity, supporting the view that neural encoding in MI is best understood at a population level.

Identification of network-level coding units for real-time representation of episodic experiences in the hippocampus

To examine the network-level organizing principles by which the brain achieves its real-time encoding of episodic information, we have developed a 96-channel array to simultaneously record the activity patterns of as many as 260 individual neurons in the mouse hippocampus during various startling episodes. We find that the mnemonic startling episodes triggered firing changes in a set of CA1 neurons in both startle-type and environment-dependent manners. Pattern classification methods reveal that these firing changes form distinct ensemble representations in a low-dimensional encoding subspace. Application of a sliding window technique further enabled us to reliably capture not only the temporal dynamics of real-time network encoding but also postevent processing of newly formed ensemble traces. Our analyses revealed that the network-encoding power is derived from a set of functional coding units, termed neural cliques, in the CA1 network. The individual neurons within neural cliques exhibit “collective cospiking” dynamics that allow the neural clique to overcome the response variability of its members and to achieve real-time encoding robustness. Conversion of activation patterns of these coding unit assemblies into a set of real-time digital codes permits concise and universal representation and categorization of discrete behavioral episodes across different individual brains.

Robust clustering by deterministic agglomeration EM of mixtures of multivariate t-distributions

Abstract This paper presents new robust clustering algorithms, which significantly improve upon the noise and initialization sensitivity of traditional mixture decomposition algorithms, and simplify the determination of the optimal number of clusters in the data set. The algorithms implement maximum likelihood mixture decomposition of multivariate t -distributions, a robust parametric extension of gaussian mixture decomposition. We achieve improved convergence capability relative to the expectation–maximization (EM) approach by deriving deterministic annealing EM (DAEM) algorithms for this mixture model and turning them into agglomerative algorithms (going through a monotonically decreasing number of components), an approach we term deterministic agglomeration EM (DAGEM). Two versions are derived, based on two variants of DAEM for mixture models. Simulation studies demonstrate the algorithms’ performance for mixtures with isotropic and non-isotropic covariances in two and 10 dimensions with known or unknown levels of outlier contamination.

Motor-cortical activity in tetraplegics

Paralysed patients may benefit from the development of an implantable brain–computer interface device that can bypass damaged motor pathways. But it is unclear whether chronically de-efferented areas will still be sufficiently excitable to respond to motor attempts if the motor cortex has been extensively reorganized, and, if they are, whether this excitability is somatotopically organized. Here we use functional magnetic resonance imaging to study brain activity in subjects with spinal-cord injuries while they are executing, or attempting to execute, movements of different limbs. We show that their motor-cortical activation closely follows normal somatotopic organization in the primary and non-primary sensorimotor areas. Our results indicate that any reorganization of the motor system that does occur in these patients does not affect attempt-related activation, and that it should be possible to access voluntary control signals by using a cortical neuroprosthetic.

Laser Photoablation of Guidance Microchannels into Hydrogels Directs Cell Growth in Three Dimensions

Recent years have seen rapid progress in the engineering and application of biomaterials with controlled biological, physical, and chemical properties, and the development of associated methods for micropatterning of three-dimensional tissue-engineering scaffolds. A remaining challenge is the development of robust, flexible methods that can be used to create physical guidance structures in cell-seeded scaffolds independently of environmental constraints. Here we demonstrate that focal photoablation caused by pulsed lasers can generate guidance structures in transparent hydrogels, with feature control down to the micron scale. These photopatterned microchannels guide the directional growth of neurites from dorsal root ganglia. We characterize the effect of laser properties and biomaterial properties on microchannel formation in PEGylated fibrinogen hydrogels, and the effect of photoablation on neural outgrowth. This strategy could lead to the development of a new generation of guidance channels for treating nerve injuries, and the engineering of structured three-dimensional neuronal or nonneuronal networks.

Patterned Optical Activation of Retinal Ganglion Cells

Neuroprosthetic retinal interfaces depend upon the ability to bypass the damaged photoreceptor layer and directly activate populations of retinal ganglion cells (RGCs). To date, the preferred approach to this task largely relies on electrode array implants. We are currently pursuing two alternative methods for light-based direct activation of the RGCs. The first method is based on applying caged glutamate over the retina and uncaging it locally to obtain RGC excitation. The second method is to artificially cause RGCs to express Channelrhodopsin II (ChR2), a light-gated cation channel. In addition to being non-contact, optical techniques lend themselves relatively easily to a variety of technologies for achieving patterned stimulation with high temporal and spatial resolution. Using the Texas Instruments Digital Light Processing (DLP - DMD) technology, we have developed an optical stimulation system capable of controlled, large-scale, flexible stimulation of the retinal tissue with high temporal accuracy. In preliminary studies, we are performing patterned photo-stimulation experiments using samples of caged fluorescent probes and in rat retinas that were virally transfected with ChR2.

Coding of position by simultaneously recorded sensory neurones in the cat dorsal root ganglion

Muscle, cutaneous and joint afferents continuously signal information about the position and movement of individual joints. How does the nervous system extract more global information, for example about the position of the foot in space? To study this question we used microelectrode arrays to record impulses simultaneously from up to 100 discriminable nerve cells in the L6 and L7 dorsal root ganglia (DRG) of the anaesthetized cat. When the hindlimb was displaced passively with a random trajectory, the firing rate of the neurones could be predicted from a linear sum of positions and velocities in Cartesian (x, y), polar or joint angular coordinates. The process could also be reversed to predict the kinematics of the limb from the firing rates of the neurones with an accuracy of 1–2 cm. Predictions of position and velocity could be combined to give an improved fit to limb position. Decoders trained using random movements successfully predicted cyclic movements and movements in which the limb was displaced from a central point to various positions in the periphery. A small number of highly informative neurones (6–8) could account for over 80% of the variance in position and a similar result was obtained in a realistic limb model. In conclusion, this work illustrates how populations of sensory receptors may encode a sense of limb position and how the firing of even a small number of neurones can be used to decode the position of the limb in space.