Identifying prodromal biomarkers for schizophrenia and bipolar disorder using magnetoencephalography

Introduction

Schizophrenia (SZ) and bipolar disorder (BD) are severe mental illnesses with large overlapping heritability. Both disorders are associated with altered neurophysiological responses, as measured with magnetoencephalography (MEG) or electroencephalography (EEG), particularly reduced mismatch negativity (MMN) and 40 Hz auditory steady-state responses (ASSR). These deficits could potentially both serve as early markers of illness and provide insight into the underlying pathophysiology as endophenotypes. First-degree relatives to patients with SZ and BD also show some neurophysiological deficits, however whether these deficits can be detected in adolescent offspring of patients is undetermined.

Objectives

We aim to investigate whether adolescents at familial high risk of schizophrenia and bipolar disorder show aberrant MMN and ASSR compared to population-based controls.

Methods

We will investigate MMN and 40 Hz ASSR in 15 year old adolescents (n ≈ 175) born to parents diagnosed with either SZ (FHR-SZ), BD (FHR-BD), or neither SZ or BD (population-based controls, PBC) using MEG. We will first perform sensor-level analyses and subsequently apply dynamic causal modeling (DCM) to investigate effective connectivity and make inferences about the underlying neurobiological mechanisms. We will investigate whether current psychopathology, cognitive status, and genetic risk for SZ and BD predict neurophysiological responses in the adolescents. Investigations are part of The Danish High Risk and Resilience Study - VIA (VIA15), a population-based longitudinal cohort study integrating social, psychological and biological risk factors and outcomes for SZ and BD.

Results

Final results are expected in 2024

Conclusions

The VIA15 study will allow for unprecedented insight into the neurobiological development of schizophrenia and bipolar disorder.

Disclosure

No significant relationships.

160 Applying the Free Energy Principle to Surgical Robotics

Introduction

In an effort to build intelligent, autonomous systems, robotics has repeatedly used neuroscience as a source of inspiration for perception and control. This corresponds well with advances in neuroscience, particularly the free energy principle; a unified theory of the brain, integrating together notions of action, perception, and learning. Crucially for robotics, it provides a simple recipe to synthesise autonomous agents under the framework of active inference.

Method

We conducted a literature search assessing the practicality of autonomous agency within surgical robotics, and to understand the potential of applying the free energy principle to surgical robotics in a bid to optimise locomotion, spatial awareness, planning, and artificial curiosity.

Results

Numerous real-life examples demonstrate the feasibility of applying bayesian inference to humanoid robotics. Here we demonstrate a similar approach to adapting surgical robotics through the representation of sensory data through a probability density function over a group of unknown variables. In this manner, surgical robotics will be able to utilise sensory signals and can provide different weights to sensory cues, thereby efficiently integrating sensory input over space and time.

Conclusions

The free energy principle may be applied to surgical robotics to provide additional feedback, and control to improve patient prognosis.

Caching mechanisms for habit formation in Active Inference

A popular distinction in the human and animal learning literature is between deliberate (or willed) and habitual (or automatic) modes of control. Extensive evidence indicates that, after sufficient learning, living organisms develop behavioural habits that permit them saving computational resources. Furthermore, humans and other animals are able to transfer control from deliberate to habitual modes (and vice versa), trading off efficiently flexibility and parsimony - an ability that is currently unparalleled by artificial control systems. Here, we discuss a computational implementation of habit formation, and the transfer of control from deliberate to habitual modes (and vice versa) within Active Inference: a computational framework that merges aspects of cybernetic theory and of Bayesian inference. To model habit formation, we endow an Active Inference agent with a mechanism to "cache" (or memorize) policy probabilities from previous trials, and reuse them to skip - in part or in full - the inferential steps of deliberative processing. We exploit the fact that the relative quality of policies, conditioned upon hidden states, is constant over trials; provided that contingencies and prior preferences do not change. This means the only quantity that can change policy selection is the prior distribution over the initial state - where this prior is based upon the posterior beliefs from previous trials. Thus, an agent that caches the quality (or the probability) of policies can safely reuse cached values to save on cognitive and computational resources - unless contingencies change. Our simulations illustrate the computational benefits, but also the limits, of three caching schemes under Active Inference. They suggest that key aspects of habitual behaviour - such as perseveration - can be explained in terms of caching policy probabilities. Furthermore, they suggest that there may be many kinds (or stages) of habitual behaviour, each associated with a different caching scheme; for example, caching associated or not associated with contextual estimation. These schemes are more or less impervious to contextual and contingency changes.

Spontaneous neuronal activity predicts intersubject variations in executive control of attention

Executive control of attention regulates our thoughts, emotion and behavior. Individual differences in executive control are associated with task-related differences in brain activity. But it is unknown whether attentional differences depend on endogenous (resting state) brain activity and to what extent regional fluctuations and functional connectivity contribute to individual variations in executive control processing. Here, we explored the potential contribution of intrinsic brain activity to executive control by using resting-state functional magnetic resonance imaging (fMRI). Using the amplitude of low-frequency fluctuations (ALFF) as an index of spontaneous brain activity, we found that ALFF in the right precuneus (PCUN) and the medial part of left superior frontal gyrus (msFC) was significantly correlated with the efficiency of executive control processing. Crucially, the strengths of functional connectivity between the right PCUN/left msFC and distributed brain regions, including the left fusiform gyrus, right inferior frontal gyrus, left superior frontal gyrus and right precentral gyrus, were correlated with individual differences in executive performance. Together, the ALFF and functional connectivity accounted for 67% of the variability in behavioral performance. Moreover, the strength of functional connectivity between specific regions could predict more individual variability in executive control performance than regionally specific fluctuations. In conclusion, our findings suggest that spontaneous brain activity may reflect or underpin executive control of attention. It will provide new insights into the origins of inter-individual variability in human executive control processing.

Algorithmic procedures for Bayesian MEG/EEG source reconstruction in SPM

The MEG/EEG inverse problem is ill-posed, giving different source reconstructions depending on the initial assumption sets. Parametric Empirical Bayes allows one to implement most popular MEG/EEG inversion schemes (Minimum Norm, LORETA, etc.) within the same generic Bayesian framework. It also provides a cost-function in terms of the variational Free energy-an approximation to the marginal likelihood or evidence of the solution. In this manuscript, we revisit the algorithm for MEG/EEG source reconstruction with a view to providing a didactic and practical guide. The aim is to promote and help standardise the development and consolidation of other schemes within the same framework. We describe the implementation in the Statistical Parametric Mapping (SPM) software package, carefully explaining each of its stages with the help of a simple simulated data example. We focus on the Multiple Sparse Priors (MSP) model, which we compare with the well-known Minimum Norm and LORETA models, using the negative variational Free energy for model comparison. The manuscript is accompanied by Matlab scripts to allow the reader to test and explore the underlying algorithm.

Topological FDR for neuroimaging

In this technical note, we describe and validate a topological false discovery rate (FDR) procedure for statistical parametric mapping. This procedure is designed to deal with signal that is continuous and has, in principle, unbounded spatial support. We therefore infer on topological features of the signal, such as the existence of local maxima or peaks above some threshold. Using results from random field theory, we assign a p-value to each maximum in an SPM and identify an adaptive threshold that controls false discovery rate, using the Benjamini and Hochberg (BH) procedure (1995). This provides a natural complement to conventional family wise error (FWE) control on local maxima. We use simulations to contrast these procedures; both in terms of their relative number of discoveries and their spatial accuracy (via the distribution of the Euclidian distance between true and discovered activations). We also assessed two other procedures: cluster-wise and voxel-wise FDR procedures. Our results suggest that (a) FDR control of maxima or peaks is more sensitive than FWE control of peaks with minimal cost in terms of false-positives, (b) voxel-wise FDR is substantially less accurate than topological FWE or FDR control. Finally, we present an illustrative application using an fMRI study of visual attention.

Population-level inferences for distributed MEG source localization under multiple constraints: Application to face-evoked fields

We address some key issues entailed by population inference about responses evoked in distributed brain systems using magnetoencephalography (MEG). In particular, we look at model selection issues at the within-subject level and feature selection issues at the between-subject level, using responses evoked by intact and scrambled faces around 170 ms (M170). We compared the face validity of subject-specific forward models and their summary statistics in terms of how estimated responses reproduced over subjects. At the within-subject level, we focused on the use of multiple constraints, or priors, for inverting distributed source models. We used restricted maximum likelihood (ReML) estimates of prior covariance components (in both sensor and source space) and show that their relative importance is conserved over subjects. At the between-subject level, we used standard anatomical normalization methods to create posterior probability maps that furnish inference about regionally specific population responses. We used these to compare different summary statistics, namely; (i) whether to test for differences between condition-specific source estimates, or whether to test the source estimate of differences between conditions, and (ii) whether to accommodate differences in source orientation by using signed or unsigned (absolute) estimates of source activity.

Bilinear dynamical systems

In this paper, we propose the use of bilinear dynamical systems (BDS)s for model-based deconvolution of fMRI time-series. The importance of this work lies in being able to deconvolve haemodynamic time-series, in an informed way, to disclose the underlying neuronal activity. Being able to estimate neuronal responses in a particular brain region is fundamental for many models of functional integration and connectivity in the brain. BDSs comprise a stochastic bilinear neurodynamical model specified in discrete time, and a set of linear convolution kernels for the haemodynamics. We derive an expectation-maximization (EM) algorithm for parameter estimation, in which fMRI time-series are deconvolved in an E-step and model parameters are updated in an M-Step. We report preliminary results that focus on the assumed stochastic nature of the neurodynamic model and compare the method to Wiener deconvolution.

Multivariate autoregressive modeling of fMRI time series

We propose the use of multivariate autoregressive (MAR) models of functional magnetic resonance imaging time series to make inferences about functional integration within the human brain. The method is demonstrated with synthetic and real data showing how such models are able to characterize interregional dependence. We extend linear MAR models to accommodate nonlinear interactions to model top-down modulatory processes with bilinear terms. MAR models are time series models and thereby model temporal order within measured brain activity. A further benefit of the MAR approach is that connectivity maps may contain loops, yet exact inference can proceed within a linear framework. Model order selection and parameter estimation are implemented by using Bayesian methods.

Interactions among neuronal systems assessed with functional neuroimaging

Cortical organisation is based on the principles of functional specialisation and functional integration. Those two concepts exist at multiple spatial scales. For example at a macroscopic level, functionally segregated areas V1 and V5 are integrated within the dorsal visual stream. At a microscopic level, it is possible to infer the functional integration of (segregated) ocular dominance columns within area V1. At the macroscopic level functional specialisation and integration can be tested with functional neuroimaging, using fMRI/PET or EEG/MEG. A framework that allows making inferences on functional integration between brain regions using fMRI will be presented. The common feature of all techniques within that framework is that they can incorporate second order terms and therefore explicitly allow for contextual modulations. Two examples demonstrate how attention and paired associates learning can modulate effective connectivity within the visual system. Furthermore these models can incorporate non-linear modulatory (feedback) connections that can account for the changes in effective connectivity observed.

A Global Estimator Unbiased by Local Changes

The global activity is an important confound when analyzing PET data in that its inclusion in the statistical model can substantially reduce error variance and increase sensitivity. However, by defining global activity as the average over all voxels one introduces a bias that is collinear with experimental factors. This leads to an underestimation of true activations and the introduction of artefactual deactivations. We propose a novel estimator for the global activity based on the notion of finding a maximally nonlocal mode in a multivariate characterization of the data, while maximizing the locality of the remaining modes. The approach uses singular value decomposition (SVD) to find a provisional set of modes, which are subsequently rotated such that a metric based on the above heuristic is maximized. This metric is a version of the stochastic sign change (SSC) criterion that has been used previously for normalizing medical images with focal defects. The estimator was evaluated on simulated and real functional imaging (PET) data. The simulations show that the bias of the global mean, introduced by focal activations, is reduced by 80--90% with the new estimator. Comparison with a previous unbiased estimator, using the empirical data, yielded similar results. The advantage of the new estimator is that it is not informed of experimental design and relies only on general assumptions regarding the nature of the signal.

Modeling geometric deformations in EPI time series

Even after realignment there is residual movement-related variance present in fMRI time-series, causing loss of sensitivity and, potentially, also specificity. One cause is the differential deformation of the sampling matrix, by field inhomogeneities, at different object positions, i.e., a movement-by-inhomogeneity interaction. This has been addressed previously by using empirical field measurements. In the present paper we suggest a forward model of how data is affected by an inhomogeneous field at different object positions. From this model we derive a method to solve the inverse problem of estimating the field inhomogeneities and their derivatives with respect to object position, directly from the EPI data and estimated realignment parameters. The field is modeled as a linear combination of cosine basis fields, which facilitates a fast way of implementing the necessary matrix operations. Simulations suggest that the solution is tractable and that the fields are estimable given the deformed images and knowledge of the relative positions at which they have been acquired. An experiment on a subject performing voluntary movements in the scanner yielded plausible estimates of the deformation fields and their application to "unwarp" the time series significantly reduced movement-related variance.

Assessing interactions among neuronal systems using functional neuroimaging

We show that new methods for measuring effective connectivity allow us to characterise the interactions between brain regions that underlie the complex interactions among different processing stages of functional architectures.

A direct quantitative relationship between the functional properties of human and macaque V5

The nature of the quantitative relationship between single-neuron recordings in monkeys and functional magnetic resonance imaging (fMRI) measurements in humans is crucial to understanding how experiments in these different species are related, yet it remains undetermined. We measured brain activity in humans attending to moving visual stimuli, using blood oxygenation level-dependent (BOLD) fMRI. Responses in V5 showed a strong and highly linear dependence on increasing strength of motion signal (coherence). These population responses in human V5 had a remarkably simple mathematical relationship to previously observed single-cell responses in macaque V5. We provided an explicit quantitative estimate for the interspecies comparison of single-neuron activity and BOLD population responses. Our data show previously unknown dissociations between the functional properties of human V5 and other human motion-sensitive areas, thus predicting similar dissociations for the properties of single neurons in homologous areas of macaque cortex.

Nonlinear PCA: characterizing interactions between modes of brain activity

This paper presents a nonlinear principal component analysis (PCA) that identifies underlying sources causing the expression of spatial modes or patterns of activity in neuroimaging time-series. The critical aspect of this technique is that, in relation to conventional PCA, the sources can interact to produce (second-order) spatial modes that represent the modulation of one (first-order) spatial mode by another. This nonlinear PCA uses a simple neural network architecture that embodies a specific form for the nonlinear mixing of sources that cause observed data. This form is motivated by a second-order approximation to any general nonlinear mixing and emphasizes interactions among pairs of sources. By introducing these nonlinearities principal components obtain with a unique rotation and scaling that does not depend on the biologically implausible constraints adopted by conventional PCA. The technique is illustrated by application to functional (positron emission tomography and functional magnetic resonance imaging) imaging data where the ensuing first- and second-order modes can be interpreted in terms of distributed brain systems. The interactions among sources render the expression of any one mode context-sensitive, where that context is established by the expression of other modes. The examples considered include interactions between cognitive states and time (i.e. adaptation or plasticity in PET data) and among functionally specialized brain systems (using a fMRI study of colour and motion processing).

Revealing interactions among brain systems with nonlinear PCA

In this work, we present a nonlinear principal component analysis (PCA) that identifies underlying sources causing the expression of spatial modes or patterns of activity in neuroimaging time series where these sources can interact to produce second-order modes. This nonlinear PCA uses a neural network architecture that embodies a specific form for the mixing of sources that is based on a second-order approximation to any general nonlinear mixing. The modes obtained have a unique rotation and scaling that does not depend on the biologically implausible constraints adopted by conventional PCA. Interactions among sources render the expression of any mode or brain system sensitive to the expression of others. The example considers interactions among functionally specialized brain systems (using a fMRI study of colour and motion processing).

The neuroanatomy of autism: a voxel-based whole brain analysis of structural scans

Autism is a biological disorder which affects social cognition, and understanding brain abnormalities of the former will elucidate the brain basis of the latter. We report structural MRI data on 15 high-functioning individuals with autistic disorder. A voxel-based whole brain analysis identified grey matter differences in an amygdala centered system relative to 15 age- and IQ-matched controls. Decreases of grey matter were found in anterior parts of this system (right paracingulate sulcus, left inferior frontal gyrus). Increases were found in posterior parts (amygdala/peri-amygdaloid cortex, middle temporal gyrus, inferior temporal gyrus), and in regions of the cerebellum. These structures are implicated in social cognition by animal, imaging and histopathological studies. This study therefore provides converging evidence of the physiological basis of social cognition.

Learning-related neuronal responses in prefrontal cortex studied with functional neuroimaging

We assessed time-dependent neuronal activity accompanying learning using functional magnetic resonance imaging (fMRI). An artificial grammar learning paradigm enabled us to dissociate activations associated with individual item learning from those involved in learning the underlying grammar system. We show that a localized region of right prefrontal cortex (PFC) is preferentially sensitive to individual item learning during the early stages of the experiment, while the left PFC region is sensitive to grammar learning which occurred across the entire course of the experiment. In addition to dissociating these two types of learning, we were able to characterize the effect of rule acquisition on neuronal responses associated with explicit learning of individual items. This effect was expressed as modulation of the time-dependent right PFC activations such that the early increase in activation associated with item learning was attenuated as the experiment progressed. In a further analysis we used structural equation modelling to explore time-dependent changes in inter-regional connectivity as a function of both item and grammar rule learning. Although there were no significant effects of item learning on the measured path strengths, rule learning was associated with a decrease in right fronto-parietal connectivity and an increase in connectivity between left and right PFC. Further fronto-parietal path strengths were observed to change, with an increase in left fronto-parietal and a decrease in right fronto-parietal connectivity path strength from right PFC to left parietal cortex. We interpret our findings in terms of a left frontal system mediating the semantic analysis of study items and directly influencing a right fronto-parietal system associated with episodic memory retrieval.

Signal-, set- and movement-related activity in the human brain: an event-related fMRI study

Electrophysiological studies on monkeys have been able to distinguish sensory and motor signals close in time by pseudorandomly delaying the cue that instructs the movement from the stimulus that triggers the movement. We have used a similar experimental design in functional magnetic resonance imaging (fMRI), scanning subjects while they performed a visuomotor conditional task with instructed delays. One of four shapes was presented briefly. Two shapes instructed the subjects to flex the index finger; the other two shapes coded the flexion of the middle finger. The subjects were told to perform the movement after a tone. We have exploited a novel use of event-related fMRI. By systematically varying the interval between the visual and acoustic stimuli, it has been possible to estimate the significance of the evoked haemodynamic response (EHR) to each of the stimuli, despite their temporal proximity in relation to the time constant of the EHR. Furthermore, by varying the phase between events and image acquisition, we have been able to achieve high temporal resolution while scanning the whole brain. We dissociated sensory and motor components of the sensorimotor transformations elicited by the task, and assessed sustained activity during the instructed delays. In calcarine and occipitotemporal cortex, the responses were exclusively associated with the visual instruction cues. In temporal auditory cortex and in primary motor cortex, they were exclusively associated with the auditory trigger stimulus. In ventral prefrontal cortex there were movement-related responses preceded by preparatory activity and by signal-related activity. Finally, responses associated with the instruction cue and with sustained activity during the delay period were observed in the dorsal premotor cortex and in the dorsal posterior parietal cortex. Where the association between a visual cue and the appropriate movement is arbitrary, the underlying visuomotor transformations are not achieved exclusively through frontoparietal interactions. Rather, these processes seem to rely on the ventral visual stream, the ventral prefrontal cortex and the anterior part of the dorsal premotor cortex.

Rapid assessment of regional cerebral metabolic abnormalities in single subjects with quantitative and nonquantitative [18F]FDG PET: A clinical validation of statistical parametric mapping

The [18F]fluorodeoxyglucose ([18F]FDG) method for measuring brain metabolism has not the wide clinical application that one might expect, partly because of its high cost and the complexity of the quantification procedure, but also because of reporting techniques based on region of interest (ROI) analysis, which are time-consuming and not fully objective. In this paper we report a clinical validation of statistical parametric mapping (SPM) using rCMRglc (quantitative) and radioactivity distribution (nonquantitative) [18F]FDG PET data. We show that a 10-min noninteractive voxel-based SPM analysis on a standard workstation enables objective assessment, including localization in stereotactic space, of regional glucose consumption abnormalities, whose reliability can be assessed on statistical and clinical grounds. Clinical validity was established using a small series of patients with degenerative or developmental disorders, including probable Alzheimer's disease, progressive aphasia, multiple sclerosis, developmental specific language impairment, and epilepsy. Analysis of quantitative and nonquantitative data showed the same pattern of results, suggesting that, for clinical purposes, quantitation and invasive arterial cannulation can be avoided. This should facilitate a wider application of the technique and the extension of SPM clinical analysis to H215O PET or high resolution SPECT perfusion studies.

Functional magnetic resonance imaging of the human brain: data acquisition and analysis

It is now feasible to create spatial maps of activity in the human brain completely non-invasively using magnetic resonance imaging. Magnetic resonance imaging (MRI) images in which the spin magnetization is refocussed by gradient switching are sensitive to local changes in magnetic susceptibility, which can occur when the oxygenation state of blood changes. Cortical neural activity causes increases in blood flow, which usually result in changes in blood oxygenation. Hence changes of image intensity can be observed, given rise to the so-called Blood Oxygenation Level Dependent (BOLD) contrast technique. Use of echo-planar imaging methods (EPI) allows the monitoring over the entire brain of such changes in real time. A temporal resolution of 1-3 s, and a spatial resolution of 2 mm in-plane, can thus be obtained. Generally in a brain mapping experiment hundred of brain image volumes are acquired at repeat times of 1-6 s, while brain tasks are performed. The data are transformed into statistical maps of image difference, using the technique known as statistical parametric mapping (SPM). This method, based on robust multilinear regression techniques, has become the method of reference for analysis of positron emission tomography (PET) image data. The special characteristics of functional MRI data require some modification of SPM algorithms and strategies, and the MRI data must be gaussianized in time and space to conform to the assumptions of the statistics of Gaussian random fields. The steps of analysis comprise: removal of head movement effects, spatial smoothing, and statistical interference, which includes temporal smoothing and removal by fitting of temporal variations slower than the experimental paradigm. By these means, activation maps can be generated with great flexibility and statistical power, giving probability estimates for activated brain regions based on intensity or spatial extent, or both combined. Recent studies have shown that patterns of activation obtained in human brain for a given stimulus are independent of the order and spatial orientation with which MRI images are acquired, and hence that inflow effects are not important for EPI data with a TR much longer than T1.

A multimodal language region in the ventral visual pathway

Reading words and naming pictures involves the association of visual stimuli with phonological and semantic knowledge. Damage to a region of the brain in the left basal posterior temporal lobe (BA37), which is strategically situated between the visual cortex and the more anterior temporal cortex, leads to reading and naming deficits. Additional evidence implicating this region in linguistic processing comes from functional neuroimaging studies of reading in normal subjects and subjects with developmental dyslexia. Here we test whether the visual component of reading is essential for activation of BA37 by comparing cortical activations elicited by word processing in congenitally blind, late-blind and sighted subjects using functional neuroimaging. Despite the different modalities used (visual and tactile), all groups of subjects showed a common activation of BA37 by words relative to non-word letter-strings. These findings agree with the proposal that BA37 is an association area that integrates converging inputs from many regions. Our study confirms a prediction of theories of brain function that depend on convergence zones; the absence of one input (that is, visual) does not alter the response properties of such a convergence region.

Different activation patterns in the visual cortex of late and congenitally blind subjects

A key issue in developmental neuroscience is the role of activity-dependent mechanisms in the epigenetic induction of functional organization in visual cortex. Ocular blindness and ensuing visual deprivation is one of the rare models available for the investigation of experience-dependent cortical reorganization in man. In a PET study we demonstrate that congenitally blind subjects show task-specific activation of extrastriate visual areas and parietal association areas during Braille reading, compared with auditory word processing. In contrast, blind subjects who lost their sight after puberty show additional activation in the primary visual cortex with the same tasks. Studies in blind-raised monkeys show that crossmodal responses in extrastriate areas can be elicited by somatosensory stimulation. This is consistent with the crossmodal extrastriate activations elicited by tactile processing in our congenitally blind subjects. Since primary visual cortex does not show crossmodal responses in primate studies, the differential activation in late and congenitally blind subjects highlights the possibility of reciprocal activation by visual imagery in subjects with early visual experience.

Incorporating prior knowledge into image registration

The first step in the spatial normalization of brain images is usually to determine the affine transformation that best maps the image to a template image in a standard space. We have developed a rapid and automatic method for performing this registration, which uses a Bayesian scheme to incorporate prior knowledge of the variability in the shape and size of heads. We compared affine registrations with and without incorporating the prior knowledge. We found that the affine transformations derived using the Bayesian scheme are much more robust and that the rate of convergence is greater.

Lateral geniculate activations can be detected using intersubject averaging and fMRI

Applications of fMRI in functional brain imaging are mainly confined to single subject designs, prohibiting the assessment of subject or group by condition interactions (i.e., differential activations) or areas of conjoint activation. In this paper a framework for fMRI group designs, using statistical parametric mapping, is introduced. It is generally believed that intersubject averaging, which requires spatial normalization and smoothing, will decrease the effective spatial resolution of fMRI or its sensitivity. A subcortical activation of the lateral geniculate nucleus (LGN) was therefore chosen to demonstrate the feasibility and power of intersubject averaging in the context of fMRI. Seven volunteers were studied, while looking at a blank screen or radially moving dots. LGN activation was demonstrated in all single subject analyses and in the group analysis.

Multimodal image coregistration and partitioning--a unified framework

This paper presents a method for the coregistration and partitioning (i.e., tissue segmentation) of brain images that have been acquired in different modalities. The basic idea is that instead of matching two images directly, one performs intermediate within-modality registrations to two template images that are already in register. One can use a least-squares minimization to determine the affine transformations that map between the templates and the images. By incorporating suitable constraints, a rigid body transformation which directly maps between the images can be extracted from these more general affine transformations. A further refinement capitalizes on the implicit normalization of both images into a standard space. This facilitates segmentation or partitioning of both original images into homologous tissue classifications. Once partitioned, the partitions can be jointly matched, further increasing the accuracy of the coregistration. In short, these techniques reduce the between-modality problem to a series of simpler within-modality problems. These methods are relatively robust, address a number of problems in image transformations, and require no manual intervention.

Activation of the human hippocampal formation during auditory-verbal long-term memory function

Clinical data from brain-damaged patients implicates the human hippocampal formation in memory function. We tested the hypothesis that long-term memory function is associated with activation of the hippocampal formation in humans by measuring regional cerebral blood flow changes whilst subjects performed memory tasks. Bilateral hippocampal regional cerebral blood flow was significantly correlated with a measure of the engagement of long-term auditory-verbal memory. No such association was observed for the degree of engagement of auditory-verbal subspan memory. These data provide, for the first time, direct in vivo evidence for the involvement of the hippocampal formation in long-term memory in the intact human brain.

Thalamic stimulation and suppression of parkinsonian tremor. Evidence of a cerebellar deactivation using positron emission tomography

Parkinsonian tremor can be abolished by chronic high frequency thalamic stimulation of the ventral intermediate nucleus. We have studied six patients with unilateral Parkinson's disease. The patients had an electrode chronically implanted in the ventral intermediate nucleus of the thalamus. We measured changes in cerebral activity by positron emission tomography using an index of regional cerebral blood flow (rCBF). Each patient was scanned in three states: (i) tremor without stimulation (condition A); (ii) tremor with ineffective stimulation (condition B); (iii) tremor abolished by effective stimulation (condition C). The suppression of tremor (C compared with B) was specifically associated with a decrease of rCBF in the cerebellum, whereas the ineffective stimulation (B compared with A) induced a decrease of rCBF in homolateral cerebral cortex. The results give evidence for different contributions from cortex and cerebellum to the generation of parkinsonian tremor and suggest that tremor suppression is mainly associated with a decrease of synaptic activity in the cerebellum.

The cortical localization of the lexicons. Positron emission tomography evidence

Positron emission tomography was used to investigate changes in regional cerebral blood flow (rCBF) in neurologically normal subjects during word reading and word repetition. The blood flow in these conditions was compared with control conditions where subjects were presented with stimuli of comparable auditory and visual complexity to real words and said the same word on presentation of each stimulus. The control condition for word repetition (hearing spoken words presented backwards) resulted in bilateral activation of the superior temporal gyrus. Word repetition caused a significant increase in rCBF over this control condition in the left superior and middle temporal gyri. The control condition for word reading (seeing stimuli written in 'false fonts', i.e. non-existent letter-like forms) resulted in significant changes in rCBF bilaterally in the striate and extrastriate cortex. Word reading caused a significant increase in blood flow relative to this control in the posterior part of the left middle temporal gyrus. The implications of these results are discussed, and it is argued that they are consistent with localization of a lexicon for spoken word recognition in the middle part of the left superior and middle temporal gyri, and a lexicon for written word recognition in the posterior part of the left middle temporal gyrus.

Regional response differences within the human auditory cortex when listening to words

The relationship between activity within the human auditory cortices and the presentation rate of heard words was investigated by measuring changes in regional cerebral blood flow with positron emission tomography. We demonstrate that in the primary auditory cortices and middle regions of the superior temporal gyri there is a linear relationship between the rate of presentation of heard words and blood flow response. In contrast, the blood flow response in an area of the left posterior superior temporal gyrus (Wernicke's area) is primarily dependent on the occurrence of words irrespective of their rate of presentation. The primary auditory cortices are associated with the early processing of complex acoustic signals whereas Wernicke's area is associated with the comprehension of heard words. This study demonstrates for the first time that time dependent sensory signals (heard words) detected in the primary auditory cortices are transformed into a time invariant output which is channelled to a functionally specialised region--Wernicke's area. Wernicke's area is therefore distinguished from other areas of the auditory cortex by direct observation of signal transformation rather than by association with a specific behavioural task.

Distribution of cortical neural networks involved in word comprehension and word retrieval

Six normal volunteers were studied with positron emission tomography to identify the cortical neural networks that participate in the processing of single words. Activity-related changes in regional cerebral blood flow were measured consecutively on 6 occasions in each subject, 2 while the subject was at rest and 4 while single word language tasks were being performed. The data from each subject were standardized for brain shape and size, reconstructed parallel to the intercommissural line, normalized for global flow differences, and then averaged for each activation condition across the 6 subjects. Significant areas of change in rCBF (P less than 0.05, with appropriate Bonferroni corrections) between task and rest conditions were displayed with reference to the coordinates of a standard neuroanatomical atlas. We have demonstrated that categorical judgements on heard pairs of real words activate neural networks along both superior temporal gyri, but with an anatomical distribution no different from that seen when the subjects listened to nonwords: the tasks would appear to be very different in cognitive demands but not in terms of the distribution of activation. However, during a verb generation task that involved thinking of verbs appropriate to heard nouns presented at a slow rate, the only temporal region activated was the left posterior superior temporal association cortex (Wernicke's area). Further analysis showed that whereas activation in other superior temporal regions, both left and right, correlated with rates of word presentation during the 4 tasks, there was no such correlation in Wernicke's area; evidence that this site is responsible for more than early acoustic processing. During verb generation there was also activation of left premotor and prefrontal cortex (including Broca's area and the supplementary motor area). The supplementary motor area is thought to be involved in the motor planning of speech. The subjects did not vocalize during the task, and therefore it would appear that the act of retrieving words from semantic memory activates networks concerned with the production of speech sounds. We conclude that single word comprehension and retrieval activate very different distributed regions of cerebral cortex, with Wernicke's area the only region engaged by both processes and with participation during silent word generation of networks involved in vocalization.

Distribution of cortical neural networks involved in word comprehension and word retrieval

Six normal volunteers were studied with positron emission tomography to identify the cortical neural networks that participate in the processing of single words. Activity-related changes in regional cerebral blood flow were measured consecutively on 6 occasions in each subject, 2 while the subject was at rest and 4 while single word language tasks were being performed. The data from each subject were standardized for brain shape and size, reconstructed parallel to the intercommissural line, normalized for global flow differences, and then averaged for each activation condition across the 6 subjects. Significant areas of change in rCBF (P less than 0.05, with appropriate Bonferroni corrections) between task and rest conditions were displayed with reference to the coordinates of a standard neuroanatomical atlas. We have demonstrated that categorical judgements on heard pairs of real words activate neural networks along both superior temporal gyri, but with an anatomical distribution no different from that seen when the subjects listened to nonwords: the tasks would appear to be very different in cognitive demands but not in terms of the distribution of activation. However, during a verb generation task that involved thinking of verbs appropriate to heard nouns presented at a slow rate, the only temporal region activated was the left posterior superior temporal association cortex (Wernicke's area). Further analysis showed that whereas activation in other superior temporal regions, both left and right, correlated with rates of word presentation during the 4 tasks, there was no such correlation in Wernicke's area; evidence that this site is responsible for more than early acoustic processing. During verb generation there was also activation of left premotor and prefrontal cortex (including Broca's area and the supplementary motor area). The supplementary motor area is thought to be involved in the motor planning of speech. The subjects did not vocalize during the task, and therefore it would appear that the act of retrieving words from semantic memory activates networks concerned with the production of speech sounds. We conclude that single word comprehension and retrieval activate very different distributed regions of cerebral cortex, with Wernicke's area the only region engaged by both processes and with participation during silent word generation of networks involved in vocalization.

Willed action and the prefrontal cortex in man: a study with PET

We used positron emission tomography to contrast changes in cerebral blood flow associated with willed and routine acts. In the six tasks used, volunteers had to make a series of responses to a sequence of stimuli. For the routine acts, each response was completely specified by the stimulus. For the willed acts, the response was open-ended and therefore volunteers had to make a deliberate choice. Willed acts in the two response modalities studied (speaking a word, or lifting a finger) were associated with increased blood flow in the dorsolateral prefrontal cortex (Brodmann area 46). Willed acts were also associated with decreases in blood flow, but the location of these decreases was modality dependent.

Positron emission tomography as a research tool in the investigation of psychiatric and psychological disorders

The principles of positron emission tomography (PET) are described, and illustrations of how these can be applied to clinical psychiatric questions relating to schizophrenia and depression are delineated. The metabolic changes in the frontal lobes which have been described in both depression and schizophrenia and depression are reviewed and discussed. More recent PET techniques allow several serial measurements of changes in regional blood flow in response to either a pharmacological challenge or a specific psychological task. This method provides a promising new approach to the study of the dopaminergic system in schizophrenia. New tracer methods of quantitating changes in in vivo concentrations of opioid receptors allow direct pharmacological access to the endogenous opioid system in the brain. Observations of regional cortical differences in opioid receptor concentration in relation to the medial and lateral pain systems are described. In addition, changes in receptor occupancy during sleep using [11C]diprenorphine and changes in the mu-specific tracer [11C]carfentanil in temporal lobe epilepsy are discussed.