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      First steps in using machine learning on fMRI data to predict intrusive memories of traumatic film footage

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          Abstract

          After psychological trauma, why do some only some parts of the traumatic event return as intrusive memories while others do not? Intrusive memories are key to cognitive behavioural treatment for post-traumatic stress disorder, and an aetiological understanding is warranted. We present here analyses using multivariate pattern analysis (MVPA) and a machine learning classifier to investigate whether peri-traumatic brain activation was able to predict later intrusive memories (i.e. before they had happened). To provide a methodological basis for understanding the context of the current results, we first show how functional magnetic resonance imaging (fMRI) during an experimental analogue of trauma (a trauma film) via a prospective event-related design was able to capture an individual's later intrusive memories. Results showed widespread increases in brain activation at encoding when viewing a scene in the scanner that would later return as an intrusive memory in the real world. These fMRI results were replicated in a second study. While traditional mass univariate regression analysis highlighted an association between brain processing and symptomatology, this is not the same as prediction. Using MVPA and a machine learning classifier, it was possible to predict later intrusive memories across participants with 68% accuracy, and within a participant with 97% accuracy; i.e. the classifier could identify out of multiple scenes those that would later return as an intrusive memory. We also report here brain networks key in intrusive memory prediction. MVPA opens the possibility of decoding brain activity to reconstruct idiosyncratic cognitive events with relevance to understanding and predicting mental health symptoms.

          Highlights

          • Why only some moments within a trauma intrude while others do not is unclear.

          • Neuroimaging may provide further clues as to why this is the case.

          • Multivariate pattern analysis, a recent neuroimaging analysis tool, was able to predict intrusive memories.

          • Those brain networks involved in intrusive memory prediction are presented.

          • Multivariate pattern analysis may inform future innovation in mental health.

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          Most cited references58

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          Neurocircuitry models of posttraumatic stress disorder and extinction: human neuroimaging research--past, present, and future.

          The prevailing neurocircuitry models of anxiety disorders have been amygdalocentric in form. The bases for such models have progressed from theoretical considerations, extrapolated from research in animals, to in vivo human imaging data. For example, one current model of posttraumatic stress disorder (PTSD) has been highly influenced by knowledge from rodent fear conditioning research. Given the phenomenological parallels between fear conditioning and the pathogenesis of PTSD, we have proposed that PTSD is characterized by exaggerated amygdala responses (subserving exaggerated acquisition of fear associations and expression of fear responses) and deficient frontal cortical function (mediating deficits in extinction and the capacity to suppress attention/response to trauma-related stimuli), as well as deficient hippocampal function (mediating deficits in appreciation of safe contexts and explicit learning/memory). Neuroimaging studies have yielded convergent findings in support of this model. However, to date, neuroimaging investigations of PTSD have not principally employed conditioning and extinction paradigms per se. The recent development of such imaging probes now sets the stage for directly testing hypotheses regarding the neural substrates of fear conditioning and extinction abnormalities in PTSD.
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            Identifying natural images from human brain activity.

            A challenging goal in neuroscience is to be able to read out, or decode, mental content from brain activity. Recent functional magnetic resonance imaging (fMRI) studies have decoded orientation, position and object category from activity in visual cortex. However, these studies typically used relatively simple stimuli (for example, gratings) or images drawn from fixed categories (for example, faces, houses), and decoding was based on previous measurements of brain activity evoked by those same stimuli or categories. To overcome these limitations, here we develop a decoding method based on quantitative receptive-field models that characterize the relationship between visual stimuli and fMRI activity in early visual areas. These models describe the tuning of individual voxels for space, orientation and spatial frequency, and are estimated directly from responses evoked by natural images. We show that these receptive-field models make it possible to identify, from a large set of completely novel natural images, which specific image was seen by an observer. Identification is not a mere consequence of the retinotopic organization of visual areas; simpler receptive-field models that describe only spatial tuning yield much poorer identification performance. Our results suggest that it may soon be possible to reconstruct a picture of a person's visual experience from measurements of brain activity alone.
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              Predictors of posttraumatic stress disorder and symptoms in adults: a meta-analysis.

              A review of 2,647 studies of posttraumatic stress disorder (PTSD) yielded 476 potential candidates for a meta-analysis of predictors of PTSD or of its symptoms. From these, 68 studies met criteria for inclusion in a meta-analysis of 7 predictors: (a) prior trauma, (b) prior psychological adjustment, (c) family history of psychopathology, (d) perceived life threat during the trauma, (e) posttrauma social support, (f) peritraumatic emotional responses, and (g) peritraumatic dissociation. All yielded significant effect sizes, with family history, prior trauma, and prior adjustment the smallest (weighted r = .17) and peritraumatic dissociation the largest (weighted r = .35). The results suggest that peritraumatic psychological processes, not prior characteristics, are the strongest predictors of PTSD.
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                Author and article information

                Contributors
                Journal
                Behav Res Ther
                Behav Res Ther
                Behaviour Research and Therapy
                Elsevier Science
                0005-7967
                1873-622X
                1 November 2014
                November 2014
                : 62
                : 37-46
                Affiliations
                [a ]University Department of Psychiatry, Warneford Hospital, University of Oxford, United Kingdom
                [b ]Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, United Kingdom
                [c ]FMRIB Centre, Nuffield Department of Clinical Neurosciences, John Radcliffe Hospital, University of Oxford, United Kingdom
                [d ]Medical Research Council Cognition and Brain Sciences Unit, 15 Chaucer Road, Cambridge CB2 7EF, United Kingdom
                [e ]Oxford Centre for Human Brain Activity (OHBA), Department of Psychiatry, Warneford Hospital, University of Oxford, United Kingdom
                [f ]Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
                Author notes
                []Corresponding author. emily.holmes@ 123456mrc-cbu.cam.ac.uk
                [1]

                Present address: Wellcome Trust Centre for Neuroimaging, Institute of Neurology, University College London, 12 Queen Square, London WC1N 3BG, United Kingdom.

                Article
                S0005-7967(14)00113-2
                10.1016/j.brat.2014.07.010
                4222599
                25151915
                8e952d4b-7f81-48ec-a294-c7dcd9932140
                © 2014 The Authors
                Categories
                Article

                Clinical Psychology & Psychiatry
                intrusive memories,trauma,flashback,mvpa,machine learning,functional magnetic resonance imaging,mental imagery

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