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Doyle O.M.,King's College London | Ashburner J.,Wellcome Trust Center for Neuroimaging | Zelaya F.O.,King's College London | Williams S.C.R.,King's College London | And 2 more authors.
NeuroImage | Year: 2013

Neuroimaging data are increasingly being used to predict potential outcomes or groupings, such as clinical severity, drug dose response, and transitional illness states. In these examples, the variable (target) we want to predict is ordinal in nature. Conventional classification schemes assume that the targets are nominal and hence ignore their ranked nature, whereas parametric and/or non-parametric regression models enforce a metric notion of distance between classes. Here, we propose a novel, alternative multivariate approach that overcomes these limitations - whole brain probabilistic ordinal regression using a Gaussian process framework. We applied this technique to two data sets of pharmacological neuroimaging data from healthy volunteers. The first study was designed to investigate the effect of ketamine on brain activity and its subsequent modulation with two compounds - lamotrigine and risperidone. The second study investigates the effect of scopolamine on cerebral blood flow and its modulation using donepezil. We compared ordinal regression to multi-class classification schemes and metric regression. Considering the modulation of ketamine with lamotrigine, we found that ordinal regression significantly outperformed multi-class classification and metric regression in terms of accuracy and mean absolute error. However, for risperidone ordinal regression significantly outperformed metric regression but performed similarly to multi-class classification both in terms of accuracy and mean absolute error. For the scopolamine data set, ordinal regression was found to outperform both multi-class and metric regression techniques considering the regional cerebral blood flow in the anterior cingulate cortex. Ordinal regression was thus the only method that performed well in all cases. Our results indicate the potential of an ordinal regression approach for neuroimaging data while providing a fully probabilistic framework with elegant approaches for model selection. © 2013.

Jbabdi S.,University of Oxford | Sotiropoulos S.N.,University of Oxford | Savio A.M.,University of the Basque Country | Grana M.,University of the Basque Country | And 2 more authors.
Magnetic Resonance in Medicine | Year: 2012

In this article, we highlight an issue that arises when using multiple b-values in a model-based analysis of diffusion MR data for tractography. The non-monoexponential decay, commonly observed in experimental data, is shown to induce overfitting in the distribution of fiber orientations when not considered in the model. Extra fiber orientations perpendicular to the main orientation arise to compensate for the slower apparent signal decay at higher b-values. We propose a simple extension to the ball and stick model based on a continuous gamma distribution of diffusivities, which significantly improves the fitting and reduces the overfitting. Using in vivo experimental data, we show that this model outperforms a simpler, noise floor model, especially at the interfaces between brain tissues, suggesting that partial volume effects are a major cause of the observed non-monoexponential decay. This model may be helpful for future data acquisition strategies that may attempt to combine multiple shells to improve estimates of fiber orientations in white matter and near the cortex. Copyright © 2012 Wiley Periodicals, Inc.

Huys Q.J.M.,Wellcome Trust Center for Neuroimaging | Moutoussis M.,University of Manchester | Williams J.,King's College London
Neural Networks | Year: 2011

Mathematically rigorous descriptions of key hypotheses and theories are becoming more common in neuroscience and are beginning to be applied to psychiatry. In this article two fictional characters, Dr. Strong and Mr. Micawber, debate the use of such computational models (CMs) in psychiatry. We present four fundamental challenges to the use of CMs in psychiatry: (a) the applicability of mathematical approaches to core concepts in psychiatry such as subjective experiences, conflict and suffering; (b) whether psychiatry is mature enough to allow informative modelling; (c) whether theoretical techniques are powerful enough to approach psychiatric problems; and (d) the issue of communicating clinical concepts to theoreticians and vice versa. We argue that CMs have yet to influence psychiatric practice, but that they help psychiatric research in two fundamental ways: (a) to build better theories integrating psychiatry with neuroscience; and (b) to enforce explicit, global and efficient testing of hypotheses through more powerful analytical methods. CMs allow the complexity of a hypothesis to be rigorously weighed against the complexity of the data. The paper concludes with a discussion of the path ahead. It points to stumbling blocks, like the poor communication between theoretical and medical communities. But it also identifies areas in which the contributions of CMs will likely be pivotal, like an understanding of social influences in psychiatry, and of the co-morbidity structure of psychiatric diseases. © 2011 Elsevier Ltd.

Campbell-Meiklejohn D.K.,Wellcome Trust Center for Neuroimaging | Campbell-Meiklejohn D.K.,University of Aarhus | Bach D.R.,Wellcome Trust Center for Neuroimaging | Roepstorff A.,University of Aarhus | And 3 more authors.
Current Biology | Year: 2010

The opinions of others can easily affect how much we value things. We investigated what happens in our brain when we agree with others about the value of an object and whether or not there is evidence, at the neural level, for social conformity through which we change object valuation. Using functional magnetic resonance imaging we independently modeled (1) learning reviewer opinions about a piece of music, (2) reward value while receiving a token for that music, and (3) their interaction in 28 healthy adults. We show that agreement with two "expert" reviewers on music choice produces activity in a region of ventral striatum that also responds when receiving a valued object. It is known that the magnitude of activity in the ventral striatum reflects the value of reward-predicting stimuli [1-8]. We show that social influence on the value of an object is associated with the magnitude of the ventral striatum response to receiving it. This finding provides clear evidence that social influence mediates very basic value signals in known reinforcement learning circuitry [9-12]. Influence at such a low level could contribute to rapid learning and the swift spread of values throughout a population. © 2010 Elsevier Ltd.

Picard F.,University of Geneva | Friston K.,Wellcome Trust Center for Neuroimaging
Neurology | Year: 2014

In recent years there has been a paradigm shift in theoretical neuroscience in which the brain-as a passive processor of sensory information-is now considered an active organ of inference, generating predictions and hypotheses about the causes of its sensations. In this commentary, we try to convey the basic ideas behind this perspective, describe their neurophysiologic underpinnings, and highlight the potential importance of this formulation for clinical neuroscience. The formalism it provides-and the implementation of active inference in the brain-may have the potential to reveal aspects of functional neuroanatomy that are compromised in conditions ranging from Parkinson disease to schizophrenia. In particular, many neurologic and neuropsychiatric conditions may be understandable in terms of a failure to modulate the postsynaptic gain of neuronal populations reporting prediction errors during action and perception. From the perspective of the predictive brain, this represents a failure to encode the precision of-or confidence in- sensory information. We propose that the predictive or inferential perspective on brain function offers novel insights into brain diseases. © 2014 American Academy of Neurology.

FitzGerald T.H.B.,Wellcome Trust Center for Neuroimaging | Moran R.J.,Virginia Polytechnic Institute and State University | Friston K.J.,Wellcome Trust Center for Neuroimaging | Dolan R.J.,Wellcome Trust Center for Neuroimaging
NeuroImage | Year: 2015

Primate studies show slow ramping activity in posterior parietal cortex (PPC) neurons during perceptual decision-making. These findings have inspired a rich theoretical literature to account for this activity. These accounts are largely unrelated to Bayesian theories of perception and predictive coding, a related formulation of perceptual inference in the cortical hierarchy. Here, we tested a key prediction of such hierarchical inference, namely that the estimated precision (reliability) of information ascending the cortical hierarchy plays a key role in determining both the speed of decision-making and the rate of increase of PPC activity. Using dynamic causal modelling of magnetoencephalographic (MEG) evoked responses, recorded during a simple perceptual decision-making task, we recover ramping-activity from an anatomically and functionally plausible network of regions, including early visual cortex, the middle temporal area (MT) and PPC. Precision, as reflected by the gain on pyramidal cell activity, was strongly correlated with both the speed of decision making and the slope of PPC ramping activity. Our findings indicate that the dynamics of neuronal activity in the human PPC during perceptual decision-making recapitulate those observed in the macaque, and in so doing we link observations from primate electrophysiology and human choice behaviour. Moreover, the synaptic gain control modulating these dynamics is consistent with predictive coding formulations of evidence accumulation. © 2014 The Authors.

Lohrenz T.,Virginia Polytechnic Institute and State University | Kishida K.T.,Virginia Polytechnic Institute and State University | Read Montague P.,Virginia Polytechnic Institute and State University | Read Montague P.,Wellcome Trust Center for Neuroimaging
Philosophical Transactions of the Royal Society B: Biological Sciences | Year: 2016

Activity in midbrain dopamine neurons modulates the release of dopamine in terminal structures including the striatum, and controls reward-dependent valuation and choice.This fluctuating release of dopamine is thought to encode reward prediction error (RPE) signals and other value-related information crucial to decision-making, and such models have been used to track prediction error signals in the striatum as encoded by BOLD signals.However, until recently there have been no comparisons of BOLD responses and dopamine responses except for one clear correlation of these two signals in rodents.No such comparisons have been made in humans.Here, we report on the connection between the RPE-related BOLD signal recorded in one group of subjects carrying out an investment task, and the corresponding dopamine signal recorded directly using fast-scan cyclic voltammetry in a separate group of Parkinson’s disease patients undergoing DBS surgery while performing the same task.The data display some correspondence between the signal types; however, there is not a one-to-one relationship.Further work is necessary to quantify the relationship between dopamine release, the BOLD signal and the computational models that have guided our understanding of both at the level of the striatum. © 2016 The Author(s).

Kumar S.,Northumbria University | Kumar S.,Wellcome Trust Center for Neuroimaging | Sedley W.,Northumbria University | Barnes G.R.,Wellcome Trust Center for Neuroimaging | And 4 more authors.
Cortex | Year: 2014

The physiological basis for musical hallucinations (MH) is not understood. One obstacle to understanding has been the lack of a method to manipulate the intensity of hallucination during the course of experiment. Residual inhibition, transient suppression of a phantom percept after the offset of a masking stimulus, has been used in the study of tinnitus. We report here a human subject whose MH were residually inhibited by short periods of music. Magnetoencephalography (MEG) allowed us to examine variation in the underlying oscillatory brain activity in different states. Source-space analysis capable of single-subject inference defined left-lateralised power increases, associated with stronger hallucinations, in the gamma band in left anterior superior temporal gyrus, and in the beta band in motor cortex and posteromedial cortex. The data indicate that these areas form a crucial network in the generation of MH, and are consistent with a model in which MH are generated by persistent reciprocal communication in a predictive coding hierarchy. © 2013 The Authors.

Carhart-Harris R.L.,Imperial College London | Friston K.J.,Wellcome Trust Center for Neuroimaging
Brain | Year: 2010

This article explores the notion that Freudian constructs may have neurobiological substrates. Specifically, we propose that Freud's descriptions of the primary and secondary processes are consistent with self-organized activity in hierarchical cortical systems and that his descriptions of the ego are consistent with the functions of the default-mode and its reciprocal exchanges with subordinate brain systems. This neurobiological account rests on a view of the brain as a hierarchical inference or Helmholtz machine. In this view, large-scale intrinsic networks occupy supraordinate levels of hierarchical brain systems that try to optimize their representation of the sensorium. This optimization has been formulated as minimizing a free-energy; a process that is formally similar to the treatment of energy in Freudian formulations. We substantiate this synthesis by showing that Freud's descriptions of the primary process are consistent with the phenomenology and neurophysiology of rapid eye movement sleep, the early and acute psychotic state, the aura of temporal lobe epilepsy and hallucinogenic drug states.

FitzGerald T.H.B.,Wellcome Trust Center for Neuroimaging | Friston K.J.,Wellcome Trust Center for Neuroimaging | Dolan R.J.,Wellcome Trust Center for Neuroimaging
NeuroImage | Year: 2013

Reward outcome signalling in the sensory cortex is held as important for linking stimuli to their consequences and for modulating perceptual learning in response to incentives. Evidence for reward outcome signalling has been found in sensory regions including the visual, auditory and somatosensory cortices across a range of different paradigms, but it is unknown whether the population of neurons signalling rewarding outcomes are the same as those processing predictive stimuli. We addressed this question using a multivariate analysis of high-resolution functional magnetic resonance imaging (fMRI), in a task where subjects were engaged in instrumental learning with visual predictive cues and auditory signalled reward feedback. We found evidence that outcome signals in sensory regions localise to the same areas involved in stimulus processing. These outcome signals are non-specific and we show that the neuronal populations involved in stimulus representation are not their exclusive target, in keeping with theoretical models of value learning. Thus, our results reveal one likely mechanism through which rewarding outcomes are linked to predictive sensory stimuli, a link that may be key for both reward and perceptual learning. © 2013 The Authors.

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