MRC Cognition and Brain Sciences Unit

One of the most popular experimental paradigms for functional neuroimaging studies of working memory has been the n-back task, in which subjects are asked to monitor the identity or location of a series of verbal or nonverbal stimuli and to indicate when the currently presented stimulus is the same as the one presented n trials previously. We conducted a quantitative meta-analysis of 668 sets of activation coordinates in Talairach space reported in 24 primary studies of n-back task variants manipulating process (location vs. identity monitoring) and content (verbal or nonverbal) of working memory. We found the following cortical regions were activated robustly (voxelwise false discovery rate = 1%): lateral premotor cortex; dorsal cingulate and medial premotor cortex; dorsolateral and ventrolateral prefrontal cortex; frontal poles; and medial and lateral posterior parietal cortex. Subsidiary meta-analyses based on appropriate subsets of the primary data demonstrated broadly similar activation patterns for identity monitoring of verbal stimuli and both location and identity monitoring of nonverbal stimuli. There was also some evidence for distinct frontoparietal activation patterns in response to different task variants. The functional specializations of each of the major cortical components in the generic large-scale frontoparietal system are discussed. We conclude that quantitative meta-analysis can be a powerful tool for combining results of multiple primary studies reported in Talairach space. Here, it provides evidence both for broadly consistent activation of frontal and parietal cortical regions by various versions of the n-back working memory paradigm, and for process- and content-specific frontoparietal activation by working memory.

Traditionally lesion location has been reported using standard templates, text based descriptions or representative raw slices from the patient's CT or MRI scan. Each of these methods has drawbacks for the display of neuroanatomical data. One solution is to display MRI scans in the same stereotaxic space popular with researchers working in functional neuroimaging. Presenting brains in this format is useful as the slices correspond to the standard anatomical atlases used by neuroimagers. In addition, lesion position and volume are directly comparable across patients. This article describes freely available software for presenting stereotaxically aligned patient scans. This article focuses on MRI scans, but many of these tools are also applicable to other modalities (e.g. CT, PET and SPECT). We suggest that this technique of presenting lesions in terms of images normalized to standard stereotaxic space should become the standard for neuropsychological studies.

This study evaluated mindfulness-based cognitive therapy (MBCT), a group intervention designed to train recovered recurrently depressed patients to disengage from dysphoria-activated depressogenic thinking that may mediate relapse/recurrence. Recovered recurrently depressed patients (n = 145) were randomized to continue with treatment as usual or, in addition, to receive MBCT. Relapse/recurrence to major depression was assessed over a 60-week study period. For patients with 3 or more previous episodes of depression (77% of the sample), MBCT significantly reduced risk of relapse/recurrence. For patients with only 2 previous episodes, MBCT did not reduce relapse/recurrence. MBCT offers a promising cost-efficient psychological approach to preventing relapse/recurrence in recovered recurrently depressed patients.

A review of recent research on cognitive processing indicates that biases in attention, memory, and interpretation, as well as repetitive negative thoughts, are common across emotional disorders, although they vary in form according to type of disorder. Current cognitive models emphasize specific forms of biased processing, such as variations in the focus of attention or habitual interpretative styles that contribute to the risk of developing particular disorders. As well as predicting risk of emotional disorders, new studies have provided evidence of a causal relationship between processing bias and vulnerability. Beyond merely demonstrating the existence of biased processing, research is thus beginning to explore the cognitive causes of emotional vulnerability, and their modification.

UNLABELLED: There is a clear need for brief, but sensitive and specific, cognitive screening instruments as evidenced by the popularity of the Addenbrooke's Cognitive Examination (ACE). OBJECTIVES: We aimed to validate an improved revision (the ACE-R) which incorporates five sub-domain scores (orientation/attention, memory, verbal fluency, language and visuo-spatial). METHODS: Standard tests for evaluating dementia screening tests were applied. A total of 241 subjects participated in this study (Alzheimer's disease=67, frontotemporal dementia=55, dementia of Lewy Bodies=20; mild cognitive impairment-MCI=36; controls=63). RESULTS: Reliability of the ACE-R was very good (alpha coefficient=0.8). Correlation with the Clinical Dementia Scale was significant (r=-0.321, p<0.001). Two cut-offs were defined (88: sensitivity=0.94, specificity=0.89; 82: sensitivity=0.84, specificity=1.0). Likelihood ratios of dementia were generated for scores between 88 and 82: at a cut-off of 82 the likelihood of dementia is 100:1. A comparison of individual age and education matched groups of MCI, AD and controls placed the MCI group performance between controls and AD and revealed MCI patients to be impaired in areas other than memory (attention/orientation, verbal fluency and language). CONCLUSIONS: The ACE-R accomplishes standards of a valid dementia screening test, sensitive to early cognitive dysfunction.

We used functional magnetic resonance imaging to demonstrate preserved conscious awareness in a patient fulfilling the criteria for a diagnosis of vegetative state. When asked to imagine playing tennis or moving around her home, the patient activated predicted cortical areas in a manner indistinguishable from that of healthy volunteers.

Behavioral researchers are increasingly conducting their studies online, to gain access to large and diverse samples that would be difficult to get in a laboratory environment. However, there are technical access barriers to building experiments online, and web browsers can present problems for consistent timing-an important issue with reaction-time-sensitive measures. For example, to ensure accuracy and test-retest reliability in presentation and response recording, experimenters need a working knowledge of programming languages such as JavaScript. We review some of the previous and current tools for online behavioral research, as well as how well they address the issues of usability and timing. We then present the Gorilla Experiment Builder (gorilla.sc), a fully tooled experiment authoring and deployment platform, designed to resolve many timing issues and make reliable online experimentation open and accessible to a wider range of technical abilities. To demonstrate the platform's aptitude for accessible, reliable, and scalable research, we administered a task with a range of participant groups (primary school children and adults), settings (without supervision, at home, and under supervision, in both schools and public engagement events), equipment (participant's own computer, computer supplied by the researcher), and connection types (personal internet connection, mobile phone 3G/4G). We used a simplified flanker task taken from the attentional network task (Rueda, Posner, & Rothbart, 2004). We replicated the "conflict network" effect in all these populations, demonstrating the platform's capability to run reaction-time-sensitive experiments. Unresolved limitations of running experiments online are then discussed, along with potential solutions and some future features of the platform.

The development of magnetic resonance imaging (MRI) techniques has defined modern neuroimaging. Since its inception, tens of thousands of studies using techniques such as functional MRI and diffusion weighted imaging have allowed for the non-invasive study of the brain. Despite the fact that MRI is routinely used to obtain data for neuroscience research, there has been no widely adopted standard for organizing and describing the data collected in an imaging experiment. This renders sharing and reusing data (within or between labs) difficult if not impossible and unnecessarily complicates the application of automatic pipelines and quality assurance protocols. To solve this problem, we have developed the Brain Imaging Data Structure (BIDS), a standard for organizing and describing MRI datasets. The BIDS standard uses file formats compatible with existing software, unifies the majority of practices already common in the field, and captures the metadata necessary for most common data processing operations.

Abstract Over the past few decades, neuroimaging has become a ubiquitous tool in basic research and clinical studies of the human brain. However, no reference standards currently exist to quantify individual differences in neuroimaging metrics over time, in contrast to growth charts for anthropometric traits such as height and weight 1 . Here we assemble an interactive open resource to benchmark brain morphology derived from any current or future sample of MRI data ( http://www.brainchart.io/ ). With the goal of basing these reference charts on the largest and most inclusive dataset available, acknowledging limitations due to known biases of MRI studies relative to the diversity of the global population, we aggregated 123,984 MRI scans, across more than 100 primary studies, from 101,457 human participants between 115 days post-conception to 100 years of age. MRI metrics were quantified by centile scores, relative to non-linear trajectories 2 of brain structural changes, and rates of change, over the lifespan. Brain charts identified previously unreported neurodevelopmental milestones 3 , showed high stability of individuals across longitudinal assessments, and demonstrated robustness to technical and methodological differences between primary studies. Centile scores showed increased heritability compared with non-centiled MRI phenotypes, and provided a standardized measure of atypical brain structure that revealed patterns of neuroanatomical variation across neurological and psychiatric disorders. In summary, brain charts are an essential step towards robust quantification of individual variation benchmarked to normative trajectories in multiple, commonly used neuroimaging phenotypes.

The authors review research showing that when recalling autobiographical events, many emotionally disturbed patients summarize categories of events rather than retrieving a single episode. The mechanisms underlying such overgeneral memory are examined, with a focus on M. A. Conway and C. W. Pleydell-Pearce's (2000) hierarchical search model of personal event retrieval. An elaboration of this model is proposed to account for overgeneral memory, focusing on how memory search can be affected by (a) capture and rumination processes, when mnemonic information used in retrieval activates ruminative thinking; (b) functional avoidance, when episodic material threatens to cause affective disturbance; and (c) impairment in executive capacity and control that limits an individual's ability to remain focused on retrieval in the face of distraction.

Diffusion Imaging in Python (Dipy) is a free and open source software project for the analysis of data from diffusion magnetic resonance imaging (dMRI) experiments. dMRI is an application of MRI that can be used to measure structural features of brain white matter. Many methods have been developed to use dMRI data to model the local configuration of white matter nerve fiber bundles and infer the trajectory of bundles connecting different parts of the brain. Dipy gathers implementations of many different methods in dMRI, including: diffusion signal pre-processing; reconstruction of diffusion distributions in individual voxels; fiber tractography and fiber track post-processing, analysis and visualization. Dipy aims to provide transparent implementations for all the different steps of dMRI analysis with a uniform programming interface. We have implemented classical signal reconstruction techniques, such as the diffusion tensor model and deterministic fiber tractography. In addition, cutting edge novel reconstruction techniques are implemented, such as constrained spherical deconvolution and diffusion spectrum imaging (DSI) with deconvolution, as well as methods for probabilistic tracking and original methods for tractography clustering. Many additional utility functions are provided to calculate various statistics, informative visualizations, as well as file-handling routines to assist in the development and use of novel techniques. In contrast to many other scientific software projects, Dipy is not being developed by a single research group. Rather, it is an open project that encourages contributions from any scientist/developer through GitHub and open discussions on the project mailing list. Consequently, Dipy today has an international team of contributors, spanning seven different academic institutions in five countries and three continents, which is still growing.

BACKGROUND: The differential diagnosis of disorders of consciousness is challenging. The rate of misdiagnosis is approximately 40%, and new methods are required to complement bedside testing, particularly if the patient's capacity to show behavioral signs of awareness is diminished. METHODS: At two major referral centers in Cambridge, United Kingdom, and Liege, Belgium, we performed a study involving 54 patients with disorders of consciousness. We used functional magnetic resonance imaging (MRI) to assess each patient's ability to generate willful, neuroanatomically specific, blood-oxygenation-level-dependent responses during two established mental-imagery tasks. A technique was then developed to determine whether such tasks could be used to communicate yes-or-no answers to simple questions. RESULTS: Of the 54 patients enrolled in the study, 5 were able to willfully modulate their brain activity. In three of these patients, additional bedside testing revealed some sign of awareness, but in the other two patients, no voluntary behavior could be detected by means of clinical assessment. One patient was able to use our technique to answer yes or no to questions during functional MRI; however, it remained impossible to establish any form of communication at the bedside. CONCLUSIONS: These results show that a small proportion of patients in a vegetative or minimally conscious state have brain activation reflecting some awareness and cognition. Careful clinical examination will result in reclassification of the state of consciousness in some of these patients. This technique may be useful in establishing basic communication with patients who appear to be unresponsive.

Recovered recurrently depressed patients were randomized to treatment as usual (TAU) or TAU plus mindfulness-based cognitive therapy (MBCT). Replicating previous findings, MBCT reduced relapse from 78% to 36% in 55 patients with 3 or more previous episodes; but in 18 patients with only 2 (recent) episodes corresponding figures were 20% and 50%. MBCT was most effective in preventing relapses not preceded by life events. Relapses were more often associated with significant life events in the 2-episode group. This group also reported less childhood adversity and later first depression onset than the 3-or-more-episode group, suggesting that these groups represented distinct populations. MBCT is an effective and efficient way to prevent relapse/recurrence in recovered depressed patients with 3 or more previous episodes.

Inferior temporal (IT) cortex in human and nonhuman primates serves visual object recognition. Computational object-vision models, although continually improving, do not yet reach human performance. It is unclear to what extent the internal representations of computational models can explain the IT representation. Here we investigate a wide range of computational model representations (37 in total), testing their categorization performance and their ability to account for the IT representational geometry. The models include well-known neuroscientific object-recognition models (e.g. HMAX, VisNet) along with several models from computer vision (e.g. SIFT, GIST, self-similarity features, and a deep convolutional neural network). We compared the representational dissimilarity matrices (RDMs) of the model representations with the RDMs obtained from human IT (measured with fMRI) and monkey IT (measured with cell recording) for the same set of stimuli (not used in training the models). Better performing models were more similar to IT in that they showed greater clustering of representational patterns by category. In addition, better performing models also more strongly resembled IT in terms of their within-category representational dissimilarities. Representational geometries were significantly correlated between IT and many of the models. However, the categorical clustering observed in IT was largely unexplained by the unsupervised models. The deep convolutional network, which was trained by supervision with over a million category-labeled images, reached the highest categorization performance and also best explained IT, although it did not fully explain the IT data. Combining the features of this model with appropriate weights and adding linear combinations that maximize the margin between animate and inanimate objects and between faces and other objects yielded a representation that fully explained our IT data. Overall, our results suggest that explaining IT requires computational features trained through supervised learning to emphasize the behaviorally important categorical divisions prominently reflected in IT.

There is growing interest regarding the role of the right inferior frontal gyrus (RIFG) during a particular form of executive control referred to as response inhibition. However, tasks used to examine neural activity at the point of response inhibition have rarely controlled for the potentially confounding effects of attentional demand. In particular, it is unclear whether the RIFG is specifically involved in inhibitory control, or is involved more generally in the detection of salient or task relevant cues. The current fMRI study sought to clarify the role of the RIFG in executive control by holding the stimulus conditions of one of the most popular response inhibition tasks-the Stop Signal Task-constant, whilst varying the response that was required on reception of the stop signal cue. Our results reveal that the RIFG is recruited when important cues are detected, regardless of whether that detection is followed by the inhibition of a motor response, the generation of a motor response, or no external response at all.

The British Association for Psychopharmacology guidelines specify the scope and targets of treatment for bipolar disorder. The third version is based explicitly on the available evidence and presented, like previous Clinical Practice Guidelines, as recommendations to aid clinical decision making for practitioners: it may also serve as a source of information for patients and carers, and assist audit. The recommendations are presented together with a more detailed review of the corresponding evidence. A consensus meeting, involving experts in bipolar disorder and its treatment, reviewed key areas and considered the strength of evidence and clinical implications. The guidelines were drawn up after extensive feedback from these participants. The best evidence from randomized controlled trials and, where available, observational studies employing quasi-experimental designs was used to evaluate treatment options. The strength of recommendations has been described using the GRADE approach. The guidelines cover the diagnosis of bipolar disorder, clinical management, and strategies for the use of medicines in short-term treatment of episodes, relapse prevention and stopping treatment. The use of medication is integrated with a coherent approach to psychoeducation and behaviour change.

In 2014, two groups of scientists published open letters on the efficacy of brain-training interventions, or "brain games," for improving cognition. The first letter, a consensus statement from an international group of more than 70 scientists, claimed that brain games do not provide a scientifically grounded way to improve cognitive functioning or to stave off cognitive decline. Several months later, an international group of 133 scientists and practitioners countered that the literature is replete with demonstrations of the benefits of brain training for a wide variety of cognitive and everyday activities. How could two teams of scientists examine the same literature and come to conflicting "consensus" views about the effectiveness of brain training?In part, the disagreement might result from different standards used when evaluating the evidence. To date, the field has lacked a comprehensive review of the brain-training literature, one that examines both the quantity and the quality of the evidence according to a well-defined set of best practices. This article provides such a review, focusing exclusively on the use of cognitive tasks or games as a means to enhance performance on other tasks. We specify and justify a set of best practices for such brain-training interventions and then use those standards to evaluate all of the published peer-reviewed intervention studies cited on the websites of leading brain-training companies listed on Cognitive Training Data (www.cognitivetrainingdata.org), the site hosting the open letter from brain-training proponents. These citations presumably represent the evidence that best supports the claims of effectiveness.Based on this examination, we find extensive evidence that brain-training interventions improve performance on the trained tasks, less evidence that such interventions improve performance on closely related tasks, and little evidence that training enhances performance on distantly related tasks or that training improves everyday cognitive performance. We also find that many of the published intervention studies had major shortcomings in design or analysis that preclude definitive conclusions about the efficacy of training, and that none of the cited studies conformed to all of the best practices we identify as essential to drawing clear conclusions about the benefits of brain training for everyday activities. We conclude with detailed recommendations for scientists, funding agencies, and policymakers that, if adopted, would lead to better evidence regarding the efficacy of brain-training interventions.

OBJECTIVE: To estimate the prevalence of frontotemporal dementia (FTD) and other degenerative early-onset dementias in a geographically defined population. BACKGROUND: Early-onset dementia (at age <65 years) results in high psychiatric morbidity and caregiver burden. Prevalence figures are available for early-onset AD but not for FTD, a dementia that is almost invariably of early onset. METHODS: Case ascertainment was by review of case records of three specialist clinic databases and inpatient admissions at a university hospital in Cambridge, United Kingdom, for patients with dementia who were <65 years of age, living in Cambridge City or East or South Cambridgeshire (population 326,019) on May 30, 2000. All the relevant health services in the area were also contacted for potential cases. Diagnosis of various dementias was based on published criteria. All patients with potential FTD were examined by the study investigators and underwent structural neuroimaging. The 1998 population estimates for the area were used to calculate age and sex prevalence with confidence intervals for AD, FTD, and other causes of dementia. RESULTS: A total of 108 patients (66 men and 42 women) with dementia with onset before they were 65 years of age were identified, of whom 60 were <65 years on the census date, giving an overall prevalence of 81 (95% CI, 62.8 to 104.5) per 100,000 in the 45- to 64-year age group. The prevalences of early-onset FTD and AD were the same: 15 per 100,000 (8.4 to 27.0) in the 45- to 64-year-old population. The mean age at onset of FTD was 52.8 years and there was a striking male preponderance (14:3). It is possible case ascertainment methods resulted in a relative underrepresentation of some forms of dementia. CONCLUSIONS: Frontotemporal dementia is a more common cause of early-onset dementia than previously recognized and appears to be more common in men.

Recent advances in neural network modeling have enabled major strides in computer vision and other artificial intelligence applications. Human-level visual recognition abilities are coming within reach of artificial systems. Artificial neural networks are inspired by the brain, and their computations could be implemented in biological neurons. Convolutional feedforward networks, which now dominate computer vision, take further inspiration from the architecture of the primate visual hierarchy. However, the current models are designed with engineering goals, not to model brain computations. Nevertheless, initial studies comparing internal representations between these models and primate brains find surprisingly similar representational spaces. With human-level performance no longer out of reach, we are entering an exciting new era, in which we will be able to build biologically faithful feedforward and recurrent computational models of how biological brains perform high-level feats of intelligence, including vision.

Mental imagery research has weathered both disbelief of the phenomenon and inherent methodological limitations. Here we review recent behavioral, brain imaging, and clinical research that has reshaped our understanding of mental imagery. Research supports the claim that visual mental imagery is a depictive internal representation that functions like a weak form of perception. Brain imaging work has demonstrated that neural representations of mental and perceptual images resemble one another as early as the primary visual cortex (V1). Activity patterns in V1 encode mental images and perceptual images via a common set of low-level depictive visual features. Recent translational and clinical research reveals the pivotal role that imagery plays in many mental disorders and suggests how clinicians can utilize imagery in treatment.