Multimodal imaging of human brain activity: rational, biophysical aspects and modes of integration

Until relatively recently the vast majority of imaging and electrophysiological studies of human brain activity have relied on single-modality measurements usually correlated with readily observable or experimentally modified behavioural or brain state patterns. Multi-modal imaging is the concept of bringing together observations or measurements from different instruments. We discuss the aims of multi-modal imaging and the ways in which it can be accomplished using representative applications. Given the importance of haemodynamic and electrophysiological signals in current multi-modal imaging applications, we also review some of the basic physiology relevant to understanding their relationship

Vector Associative Maps: Unsupervised Real-time Error-based Learning and Control of Movement Trajectories

This article describes neural network models for adaptive control of arm movement trajectories during visually guided reaching and, more generally, a framework for unsupervised real-time error-based learning. The models clarify how a child, or untrained robot, can learn to reach for objects that it sees. Piaget has provided basic insights with his concept of a circular reaction: As an infant makes internally generated movements of its hand, the eyes automatically follow this motion. A transformation is learned between the visual representation of hand position and the motor representation of hand position. Learning of this transformation eventually enables the child to accurately reach for visually detected targets. Grossberg and Kuperstein have shown how the eye movement system can use visual error signals to correct movement parameters via cerebellar learning. Here it is shown how endogenously generated arm movements lead to adaptive tuning of arm control parameters. These movements also activate the target position representations that are used to learn the visuo-motor transformation that controls visually guided reaching. The AVITE model presented here is an adaptive neural circuit based on the Vector Integration to Endpoint (VITE) model for arm and speech trajectory generation of Bullock and Grossberg. In the VITE model, a Target Position Command (TPC) represents the location of the desired target. The Present Position Command (PPC) encodes the present hand-arm configuration. The Difference Vector (DV) population continuously.computes the difference between the PPC and the TPC. A speed-controlling GO signal multiplies DV output. The PPC integrates the (DV)·(GO) product and generates an outflow command to the arm. Integration at the PPC continues at a rate dependent on GO signal size until the DV reaches zero, at which time the PPC equals the TPC. The AVITE model explains how self-consistent TPC and PPC coordinates are autonomously generated and learned. Learning of AVITE parameters is regulated by activation of a self-regulating Endogenous Random Generator (ERG) of training vectors. Each vector is integrated at the PPC, giving rise to a movement command. The generation of each vector induces a complementary postural phase during which ERG output stops and learning occurs. Then a new vector is generated and the cycle is repeated. This cyclic, biphasic behavior is controlled by a specialized gated dipole circuit. ERG output autonomously stops in such a way that, across trials, a broad sample of workspace target positions is generated. When the ERG shuts off, a modulator gate opens, copying the PPC into the TPC. Learning of a transformation from TPC to PPC occurs using the DV as an error signal that is zeroed due to learning. This learning scheme is called a Vector Associative Map, or VAM. The VAM model is a general-purpose device for autonomous real-time error-based learning and performance of associative maps. The DV stage serves the dual function of reading out new TPCs during performance and reading in new adaptive weights during learning, without a disruption of real-time operation. YAMs thus provide an on-line unsupervised alternative to the off-line properties of supervised error-correction learning algorithms. YAMs and VAM cascades for learning motor-to-motor and spatial-to-motor maps are described. YAM models and Adaptive Resonance Theory (ART) models exhibit complementary matching, learning, and performance properties that together provide a foundation for designing a total sensory-cognitive and cognitive-motor autonomous system.National Science Foundation (IRI-87-16960, IRI-87-6960); Air Force Office of Scientific Research (90-0175); Defense Advanced Research Projects Agency (90-0083

A Neural “Tuning Curve” for Multisensory Experience and Cognitive-Perceptual Schizotypy

Our coherent perception of external events is enabled by the integration of inputs from different senses occurring within a range of temporal offsets known as the temporal binding window (TBW), which varies from person to person. A relatively wide TBW may increase the likelihood that stimuli originating from different environmental events are erroneously integrated and abnormally large TBW has been found in psychiatric disorders characterized by unusual perceptual experiences. Despite strong evidence of interindividual differences in TBW, both within clinical and nonclinical populations, the neurobiological underpinnings of this variability remain unclear. We adopted an integrated strategy linking TBW to temporal dynamics in functional magnetic resonance imaging (fMRI)-resting-state activity and cortical excitation/inhibition (E/I) balance, indexed by glutamate/Gamma-AminoButyric Acid (GABA) concentrations and common variation in glutamate and GABA genes in a healthy sample. Stronger resting-state longrange temporal correlations, indicated by larger power law exponent (PLE), in the auditory cortex, robustly predicted narrower audio-tactile TBW, which was in turn associated with lower cognitive-perceptual schizotypy. Furthermore, PLE was highest and TBW narrowest for individuals with intermediate levels of E/I balance, with shifts towards either extreme resulting in reduced multisensory temporal precision and increased schizotypy, effectively forming a neural ?tuning curve? for multisensory experience and schizophrenia risk. Our findings shed light on the neurobiological underpinnings of multisensory integration and its potentially clinically relevant inter-individual variability

Multimodal Proprioceptive Integration in Sensorimotor Networks of an Insect Leg

An animal’s nervous system monitors the actions of the body using its sense of proprioception. This information is used for precise motor control and to enable coordinated interaction with the animal’s surroundings. Proprioception is a multimodal sense that includes feedback about limb movement and loading from various peripheral sense organs. The sensory information from distinct sense organs must be integrated by the network to form a coherent representation of the current proprioceptive state and to elicit appropriate motor behavior. By combining intra- and extracellular electrophysiological recording techniques with precise mechanical sensory stimulation paradigms, I studied multimodal proprioceptive integration in the sensorimotor network of the stick insect leg. The findings demonstrate where, when, and how sensory feedback from load-sensing campaniform sensilla (CS) is integrated with movement information from the femoral chordotonal organ (fCO) in the sensorimotor network controlling movement of the femur-tibia (FTi) joint. Proprioceptive information about distinct sensory modalities (load / movement) and from distinct sense organs of the same sensory modality (trochanterofemoral CS (tr/fCS) / tibial CS (tiCS)) was distributed into one network of local premotor nonspiking interneurons (NSIs). The NSIs’ processing of fCO, tr/fCS, and tiCS was antagonistic with respect to a given NSI’s effect on the motor output of extensor tibiae motor neurons (ExtTi MNs). Spatial summation of load and movement feedback occurred in the network of premotor NSIs, whereas temporal summation was shifted between sensory modalities. Load feedback (tr/fCS / tiCS) was consistently delayed relative to movement signals (fCO) throughout the sensorimotor pathways of sensory afferents, premotor NSIs, and ExtTi MNs. The connectivity between these neuron types was inferred using transmission times and followed distinct patterns for individual sense organs. At the motor output level of the system, the temporal shift of simultaneously elicited load and movement feedback caused load responses to be superimposed onto ongoing movement responses. These results raised the hypothesis that load could alter movement signal processing. Load (tiCS) affected movement (fCO) signal gain by presynaptic afferent inhibition. In postsynaptic premotor NSIs, this led to altered movement parameter dependence and nonlinear summation of load and movement signals. Specifically, the amplitude dependence of NSIs opposing ExtTi MN output was increased, and, consistently, the movement response gain of the slow ExtTi MN was decreased. Movement signal processing in the premotor network was altered depending on the proprioceptive context, i.e. the presence or absence of load feedback. Lateral presynaptic interactions between load (tiCS) and movement (fCO) afferents were reciprocal, i.e. existed from fCO to tiCS afferents and vice versa, and also occurred between sensory afferents of the same sense organ. Additionally, a new type of presynaptic interaction was identified. Load signals increased the gain of directional movement information by releasing unidirectionally velocity- or acceleration-sensitive fCO afferents from tonic presynaptic inhibition. Paired double recordings showed lateral connectivity also at the level of the premotor NSI network. NSIs interacted via reciprocal excitatory connections. Additionally, the activity of individual NSIs was correlated in the absence of external stimuli, and specific types of NSIs showed rhythmic 30 Hz oscillations of the resting membrane potential, indicating an underlying mechanism of network synchronization. Taken together, the results of this dissertation provide an understanding of the integration of multimodal proprioceptive feedback in the sensorimotor network by identifying neuronal pathways and mechanism underlying spatial and temporal signal summation. The local network uses multimodal signal integration for context-dependent sensory processing, thereby providing insights into the mechanism by which a local network can adapt sensory processing to the behavioral context. Initial results clearly highlight the necessity to consider lateral connections along sensorimotor pathways to unravel the complex computations underlying proprioceptive processing and motor control. The findings on the integration of proprioceptive signals, obtained in the resting animal, broaden our understanding of sensorimotor processing and motor control not only in the stationary, but also in the walking animal

Gamma rhythms and beta rhythms have different synchronization properties

Experimental and modeling efforts suggest that rhythms in the CA1 region of the hippocampus that are in the beta range (12-29 Hz) have a different dynamical structure than that of gamma (30-70 Hz). We use a simplified model to show that the different rhythms employ different dynamical mechanisms to synchronize, based on different ionic currents. The beta frequency is able to synchronize over long conduction delays (corresponding to signals traveling a significant distance in the brain) that apparently cannot be tolerated by gamma rhythms. The synchronization properties are consistent with data suggesting that gamma rhythms are used for relatively local computations whereas beta rhythms are used for higher level interactions involving more distant structures

Cross-modal extinction in a boy with severely autistic behaviour and high verbal intelligence

Anecdotal reports from individuals with autism suggest a loss of awareness to stimuli from one modality in the presence of stimuli from another. Here we document such a case in a detailed study of T.M., a 13-year-old boy with autism in whom significant autistic behaviors are combined with an uneven IQ profile of superior verbal and low performance abilities. Although T.M.'s speech is often unintelligible and his behavior is dominated by motor stereotypies and impulsivity, he can communicate by typing or pointing independently within a letter board. A series of experiments using simple and highly salient visual, auditory, and tactile stimuli demonstrated a hierarchy of cross-modal extinction, in which auditory information extinguished other modalities at various levels of processing. T.M. also showed deficits in shifting and sustaining attention. These results provide evidence for mono-channel perception in autism and suggest a general pattern of winner-takes-all processing in which a stronger stimulus-d riven representation dominates behavior, extinguishing weaker representations

Alice in wonderland syndrome. a clinical and pathophysiological review

Alice in Wonderland Syndrome (AIWS) is a perceptual disorder, principally involving visual and somesthetic integration, firstly reported by Todd, on the literary suggestion of the strange experiences described by Lewis Carroll in Alice in Wonderland books. Symptoms may comprise among others aschematia and dysmetropsia. This syndrome has many different etiologies; however EBV infection is the most common cause in children, while migraine affects more commonly adults. Many data support a strict relationship between migraine and AIWS, which could be considered in many patients as an aura or a migraine equivalent, particularly in children. Nevertheless, AIWS seems to have anatomical correlates. According to neuroimaging, temporoparietal- occipital carrefour (TPO-C) is a key region for developing many of AIWS symptoms. The final part of this review aims to find the relationship between AIWS symptoms, presenting a pathophysiological model. In brief, AIWS symptoms depend on an alteration of TPO-C where visual-spatial and somatosensory information are integrated. Alterations in these brain regions may cause the cooccurrence of dysmetropsia and disorders of body schema. In our opinion, the association of other symptoms reported in literature could vary depending on different etiologies and the lack of clear diagnostic criteria

Neurosystems: brain rhythms and cognitive processing

Neuronal rhythms are ubiquitous features of brain dynamics, and are highly correlated with cognitive processing. However, the relationship between the physiological mechanisms producing these rhythms and the functions associated with the rhythms remains mysterious. This article investigates the contributions of rhythms to basic cognitive computations (such as filtering signals by coherence and/or frequency) and to major cognitive functions (such as attention and multi-modal coordination). We offer support to the premise that the physiology underlying brain rhythms plays an essential role in how these rhythms facilitate some cognitive operations.098352 - Wellcome Trust; 5R01NS067199 - NINDS NIH HH

Prepontine non-giant neurons drive flexible escape behavior in zebrafish

Many species execute ballistic escape reactions to avoid imminent danger. Despite fast reaction times, responses are often highly regulated, reflecting a trade-off between costly motor actions and perceived threat level. However, how sensory cues are integrated within premotor escape circuits remains poorly understood. Here, we show that in zebrafish, less precipitous threats elicit a delayed escape, characterized by flexible trajectories, which are driven by a cluster of 38 prepontine neurons that are completely separate from the fast escape pathway. Whereas neurons that initiate rapid escapes receive direct auditory input and drive motor neurons, input and output pathways for delayed escapes are indirect, facilitating integration of cross-modal sensory information. These results show that rapid decision-making in the escape system is enabled by parallel pathways for ballistic responses and flexible delayed actions and defines a neuronal substrate for hierarchical choice in the vertebrate nervous system