Pain Control by Co-adaptive Learning in a Brain-Machine Interface.

Innovation in the field of brain-machine interfacing offers a new approach to managing human pain. In principle, it should be possible to use brain activity to directly control a therapeutic intervention in an interactive, closed-loop manner. But this raises the question as to whether the brain activity changes as a function of this interaction. Here, we used real-time decoded functional MRI responses from the insula cortex as input into a closed-loop control system aimed at reducing pain and looked for co-adaptive neural and behavioral changes. As subjects engaged in active cognitive strategies orientated toward the control system, such as trying to enhance their brain activity, pain encoding in the insula was paradoxically degraded. From a mechanistic perspective, we found that cognitive engagement was accompanied by activation of the endogenous pain modulation system, manifested by the attentional modulation of pain ratings and enhanced pain responses in pregenual anterior cingulate cortex and periaqueductal gray. Further behavioral evidence of endogenous modulation was confirmed in a second experiment using an EEG-based closed-loop system. Overall, the results show that implementing brain-machine control systems for pain induces a parallel set of co-adaptive changes in the brain, and this can interfere with the brain signals and behavior under control. More generally, this illustrates a fundamental challenge of brain decoding applications-that the brain inherently adapts to being decoded, especially as a result of cognitive processes related to learning and cooperation. Understanding the nature of these co-adaptive processes informs strategies to mitigate or exploit them

Linking brain-wide multivoxel activation patterns to behaviour: Examples from language and math

A key goal of cognitive neuroscience is to find simple and direct connections between brain and behaviour. However, fMRI analysis typically involves choices between many possible options, with each choice potentially biasing any brain-behaviour correlations that emerge. Standard methods of fMRI analysis assess each voxel individually, but then face the problem of selection bias when combining those voxels into a region-of-interest, or ROI. Multivariate pattern-based fMRI analysis methods use classifiers to analyse multiple voxels together, but can also introduce selection bias via data-reduction steps as feature selection of voxels, pre-selecting activated regions, or principal components analysis. We show here that strong brain-behaviour links can be revealed without any voxel selection or data reduction, using just plain linear regression as a classifier applied to the whole brain at once, i.e. treating each entire brain volume as a single multi-voxel pattern. The brain-behaviour correlations emerged despite the fact that the classifier was not provided with any information at all about subjects\u27 behaviour, but instead was given only the neural data and its condition-labels. Surprisingly, more powerful classifiers such as a linear SVM and regularised logistic regression produce very similar results. We discuss some possible reasons why the very simple brain-wide linear regression model is able to find correlations with behaviour that are as strong as those obtained on the one hand from a specific ROI and on the other hand from more complex classifiers. In a manner which is unencumbered by arbitrary choices, our approach offers a method for investigating connections between brain and behaviour which is simple, rigorous and direct. © 2010 Elsevier Inc

Multivariate decoding of brain images using ordinal regression.

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

Resting state correlates of subdimensions of anxious affect

Resting state fMRI may help identify markers of risk for affective disorder. Given the comorbidity of anxiety and depressive disorders and the heterogeneity of these disorders as defined by DSM, an important challenge is to identify alterations in resting state brain connectivity uniquely associated with distinct profiles of negative affect. The current study aimed to address this by identifying differences in brain connectivity specifically linked to cognitive and physiological profiles of anxiety, controlling for depressed affect. We adopted a two-stage multivariate approach. Hierarchical clustering was used to independently identify dimensions of negative affective style and resting state brain networks. Combining the clustering results, we examined individual differences in resting state connectivity uniquely associated with subdimensions of anxious affect, controlling for depressed affect. Physiological and cognitive subdimensions of anxious affect were identified. Physiological anxiety was associated with widespread alterations in insula connectivity, including decreased connectivity between insula subregions and between the insula and other medial frontal and subcortical networks. This is consistent with the insula facilitating communication between medial frontal and subcortical regions to enable control of physiological affective states. Meanwhile, increased connectivity within a frontoparietal-posterior cingulate cortex-precunous network was specifically associated with cognitive anxiety, potentially reflecting increased spontaneous negative cognition (e.g., worry). These findings suggest that physiological and cognitive anxiety comprise subdimensions of anxiety-related affect and reveal associated alterations in brain connectivity

Perception and processing of self-motion cues

The capacity of animals to navigate through familiar or novel environments depends crucially on the integration of a disparate set of self motion cues. The study begins with one of the most simple, planar visual motion, and investigates the cortical organisation of motion sensitive areas. It finds evidence of columnar organisation in hMT+ and a large scale map in V1. Chapter 3 extends this by using stimuli designed to emulate visual and auditory forward motion. It finds that participants are able to determine their direction with a precision close to that predicted by Bayesian integration. Predictions were made regarding neural processing through a modified divisive normalisation model, which was also used to fit the behavioural adaptation results. The integration of different modalities requires visual and auditory streams to combine at some stage within the sensory processing hierarchy. Previous research suggests the ventral intraparietal region (VIP) may be the seat of such integration. Chapter 4 tests whether VIP does combine these cues and whether the correlation between VIP and the unimodal regions changes depending on the coherence of unimodal stimuli. The presence of such modulation is predicted by some models, such as the divisive normalisation model. The processing of such egocentric self motion cues leads to the updating of allocentric representations, these are believed to be encoded by head direction cells and place cells. The experiment in chapter 5 uses a virtual reality stimulus during fMRI scanning to give participants the sense of moving and navigating. Their location in the virtual environment was decoded above chance from voxels in the hippocampus. No head direction signal was classified above chance from any of the three cortical regions investigated. We tentatively conclude that head direction is considerably more difficult to classify from the BOLD signal, possibly due to the homogeneous organisation of head direction cells

Auditory conflict and congruence in frontotemporal dementia.

Impaired analysis of signal conflict and congruence may contribute to diverse socio-emotional symptoms in frontotemporal dementias, however the underlying mechanisms have not been defined. Here we addressed this issue in patients with behavioural variant frontotemporal dementia (bvFTD; n = 19) and semantic dementia (SD; n = 10) relative to healthy older individuals (n = 20). We created auditory scenes in which semantic and emotional congruity of constituent sounds were independently probed; associated tasks controlled for auditory perceptual similarity, scene parsing and semantic competence. Neuroanatomical correlates of auditory congruity processing were assessed using voxel-based morphometry. Relative to healthy controls, both the bvFTD and SD groups had impaired semantic and emotional congruity processing (after taking auditory control task performance into account) and reduced affective integration of sounds into scenes. Grey matter correlates of auditory semantic congruity processing were identified in distributed regions encompassing prefrontal, parieto-temporal and insular areas and correlates of auditory emotional congruity in partly overlapping temporal, insular and striatal regions. Our findings suggest that decoding of auditory signal relatedness may probe a generic cognitive mechanism and neural architecture underpinning frontotemporal dementia syndromes

Fractionating the anterior temporal lobe : MVPA reveals differential responses to input and conceptual modality

Words activate cortical regions in accordance with their modality of presentation (i.e., written vs. spoken), yet there is a long-standing debate about whether patterns of activity in any specific brain region capture modality-invariant conceptual information. Deficits in patients with semantic dementia highlight the anterior temporal lobe (ATL) as an amodal store of semantic knowledge but these studies do not permit precise localisation of this function. The current investigation used multiple imaging methods in healthy participants to examine functional dissociations within ATL. Multi-voxel pattern analysis identified spatially segregated regions: a response to input modality in anterior superior temporal gyrus (aSTG) and a response to meaning in more ventral anterior temporal lobe (vATL). This functional dissociation was supported by resting-state connectivity that found greater coupling for aSTG with primary auditory cortex and vATL with the default mode network. A meta-analytic decoding of these connectivity patterns implicated aSTG in processes closely tied to auditory processing (such as phonology and language) and vATL in meaning-based tasks (such as comprehension or social cognition). Thus we provide converging evidence for the segregation of meaning and input modality in the ATL

SPM: a history.

Karl Friston began the SPM project around 1991. The rest is history

Knowing what from where : Hippocampal connectivity with temporoparietal cortex at rest is linked to individual differences in semantic and topographic memory

The hippocampus contributes to episodic, spatial and semantic aspects of memory, yet individual differences within and between these functions are not well-understood. In 136 healthy individuals, we investigated whether these differences reflect variation in the strength of connections between functionally-specialised segments of the hippocampus and diverse cortical regions that participate in different aspects of memory. Better topographical memory was associated with stronger connectivity between lingual gyrus and left anterior, rather than posterior, hippocampus. Better semantic memory was associated with increased connectivity between the cuneus/precuneus and left, rather than right, posterior hippocampus. Notably, we observed a double dissociation between semantic and topographical memory: better semantic memory was associated with stronger connectivity between left temporoparietal cortex and left anterior hippocampus, while better topographic memory was linked to stronger connectivity with right anterior hippocampus. Together these data support a division-of-labour account of hippocampal functioning: at the population level, differences in connectivity across the hippocampus reflect functional specialisation for different facets of memory, while variation in these connectivity patterns across individuals is associated with differences in the capacity to retrieve different types of information. In particular, within-hemisphere connectivity between hippocampus and left temporoparietal cortex supports conceptual processing at the expense of spatial ability