In praise of tedious anatomy

Functional neuroimaging is fundamentally a tool for mapping function to structure, and its success consequently requires neuroanatomical precision and accuracy. Here we review the various means by which functional activation can be localized to neuroanatomy and suggest that the gold standard should be localization to the individual’s or group’s own anatomy through the use of neuroanatomical knowledge and atlases of neuroanatomy. While automated means of localization may be useful, they cannot provide the necessary accuracy, given variability between individuals. We also suggest that the field of functional neuroimaging needs to converge on a common set of methods for reporting functional localization including a common “standard” space and criteria for what constitutes sufficient evidence to report activation in terms of Brodmann’s areas

Reward learning and working memory: Effects of massed versus spaced training and post-learning delay period

Neuroscience research has illuminated the mechanisms supporting learning from reward feedback, demonstrating a critical role for the striatum and midbrain dopamine system. However, in humans, short-term working memory that is dependent on frontal and parietal cortices can also play an important role, particularly in commonly used paradigms in which learning is relatively condensed in time. Given the growing use of reward-based learning tasks in translational studies in computational psychiatry, it is important to understand the extent of the influence of working memory and also how core gradual learning mechanisms can be better isolated. In our experiments, we manipulated the spacing between repetitions along with a post-learning delay preceding a test phase. We found that learning was slower for stimuli repeated after a long delay (spaced-trained) compared to those repeated immediately (massed-trained), likely reflecting the remaining contribution of feedback learning mechanisms when working memory is not available. For massed learning, brief interruptions led to drops in subsequent performance, and individual differences in working memory capacity positively correlated with overall performance. Interestingly, when tested after a delay period but not immediately, relative preferences decayed in the massed condition and increased in the spaced condition. Our results provide additional support for a large role of working memory in reward-based learning in temporally condensed designs. We suggest that spacing training within or between sessions is a promising approach to better isolate and understand mechanisms supporting gradual reward-based learning, with particular importance for understanding potential learning dysfunctions in addiction and psychiatric disorders

Neural fate of seen and unseen faces in visuospatial neglect: A combined event-related functional MRI and event-related potential study

This is a post print version of the article. The official published version can be obtained from the link below.To compare neural activity produced by visual events that escape or reach conscious awareness, we used event-related MRI and evoked potentials in a patient who had neglect and extinction after focal right parietal damage, but intact visual fields. This neurological disorder entails a loss of awareness for stimuli in the field contralateral to a brain lesion when stimuli are simultaneously presented on the ipsilateral side, even though early visual areas may be intact, and single contralateral stimuli may still be perceived. Functional MRI and event-related potential study were performed during a task where faces or shapes appeared in the right, left, or both fields. Unilateral stimuli produced normal responses in V1 and extrastriate areas. In bilateral events, left faces that were not perceived still activated right V1 and inferior temporal cortex and evoked nonsignificantly reduced N1 potentials, with preserved face-specific negative potentials at 170 ms. When left faces were perceived, the same stimuli produced greater activity in a distributed network of areas including right V1 and cuneus, bilateral fusiform gyri, and left parietal cortex. Also, effective connectivity between visual, parietal, and frontal areas increased during perception of faces. These results suggest that activity can occur in V1 and ventral temporal cortex without awareness, whereas coupling with dorsal parietal and frontal areas may be critical for such activity to afford conscious perception. Right parietal damage may cause a loss of awareness for contralateral (left) sensory inputs, such as hemispatial neglect and extinction (1–3). Visual extinction is the failure to perceive a stimulus in the contralesional field when presented together with an ipsilesional stimulus (bilateral simultaneous stimulation, BSS), even though occipital visual areas are intact and unilateral contralesional stimuli can be perceived when presented alone. It reflects a deficit of spatial attention toward the contralesional side, excluding left inputs from awareness in the presence of competing stimuli (2, 3). Spatial attention involves a complex neural network centered on the right parietal lobe (4, 5), but how parietal and related areas interact with sensory processing in distant cortices is largely unknown. Here we combined event-related functional MRI (fMRI) and event-related potentials (ERPs) to study the regional pattern and temporal course of brain activity produced by seen and unseen stimuli in a patient with chronic neglect and extinction caused by parietal damage. In keeping with intact early visual areas in such patients, behavioral studies suggest that some residual processing may still occur for contralesional stimuli without attention, or without awareness, including “preattentive” grouping (e.g., refs. 6 and 7) and semantic priming (e.g., ref. 8). It has been speculated (3, 9) that such effects might relate to separate cortical visual streams, with temporal areas extracting object features for identification, and parietal areas encoding spatial locations and parameters for action (10). Because neglect and extinction follow parietal damage, residual perceptual and semantic processing still might occur in occipital and temporal cortex without awareness, in the absence of normal integration with concomitant processing in parietal regions. Our study tested this hypothesis by using event-related imaging and electrophysiology measures, which are widely used to study mechanisms of normal attention (11, 12). There have been few imaging (e.g., ref. 13) or ERP (e.g., ref. 14) studies in neglect, and most examined activity at rest or during passive unilateral visual stimulation, rather than in relation to awareness or extinction on bilateral stimulation. However, a recent ERP study (15) found signals evoked by perceived, but not extinguished, visual stimuli in a parietal patient. By contrast, functional imaging in another patient (16) showed activation of striate cortex by extinguished stimuli, although severe extinction on all bilateral stimuli precluded any comparison with normal perception. In our patient we used both fMRI and ERPs during a similar extinction task to determine the neural correlates of two critical conditions: (i) when contralesional stimuli are extinguished, and (ii) when the same stimuli are seen. Stimulus presentation was arranged so as to obtain a balanced number of extinguished and seen contralesional events across all bilateral trials. Like Rees et al. (16), we used face stimuli to exploit previous knowledge that face processing activates fusiform areas in temporal cortex (e.g., refs. 17 and 18), and elicits characteristic potentials 170–200 ms after stimulus onset (e.g., refs. 19–21) in addition to other visual components such as P1 and N1 (e.g., ref. 11). We reasoned that such responses might help trace the neural fate of contralesional stimuli (seen or extinguished) at both early and later processing stages in the visual system

Sharing brain mapping statistical results with the neuroimaging data model

Only a tiny fraction of the data and metadata produced by an fMRI study is finally conveyed to the community. This lack of transparency not only hinders the reproducibility of neuroimaging results but also impairs future meta-analyses. In this work we introduce NIDM-Results, a format specification providing a machine-readable description of neuroimaging statistical results along with key image data summarising the experiment. NIDM-Results provides a unified representation of mass univariate analyses including a level of detail consistent with available best practices. This standardized representation allows authors to relay methods and results in a platform-independent regularized format that is not tied to a particular neuroimaging software package. Tools are available to export NIDM-Result graphs and associated files from the widely used SPM and FSL software packages, and the NeuroVault repository can import NIDM-Results archives. The specification is publically available at: http://nidm.nidash.org/specs/nidm-results.html

An event based topic learning pipeline for neuroimaging literature mining

Neuroimaging text mining extracts knowledge from neuroimaging texts and has received widespread attention. Topic learning is an important research focus of neuroimaging text mining. However, current neuroimaging topic learning researches mainly used traditional probability topic models to extract topics from literature and cannot obtain high-quality neuroimaging topics. The existing topic learning methods also cannot meet the requirements of topic learning oriented to full-text neuroimaging literature. In this paper, three types of neuroimaging research topic events are defined to describe the process and result of neuroimaging researches. An event based topic learning pipeline, called neuroimaging Event-BTM, is proposed to realize topic learning from full-text neuroimaging literature. The experimental results on the PLoS One data set show that the accuracy and completeness of the proposed method are significantly better than the existing main topic learning methods

Human Computer Interaction Meets Psychophysiology: A Critical Perspective

Human computer interaction (HCI) groups are more and more often exploring the utility of new, lower cost electroencephalography (EEG) interfaces for assessing user engagement and experience as well as for directly controlling computers. While the potential benefits of using EEG are considerable, we argue that research is easily driven by what we term naïve neurorealism. That is, data obtained with psychophysiological devices have poor reliability and uncertain validity, making inferences on mental states difficult. This means that unless sufficient care is taken to address the inherent shortcomings, the contributions of psychophysiological human computer interaction are limited to their novelty value rather than bringing scientific advance. Here, we outline the nature and severity of the reliability and validity problems and give practical suggestions for HCI researchers and reviewers on the way forward, and which obstacles to avoid. We hope that this critical perspective helps to promote good practice in the emerging field of psychophysiology in HCI

Category label and response location shifts in category learning

The category shift literature suggests that rule-based classification, an important form of explicit learning, is mediated by two separate learned associations: a stimulus-to-label association that associates stimuli and category labels, and a label-to-response association that associates category labels and responses. Three experiments investigate whether information–integration classification, an important form of implicit learning, is also mediated by two separate learned associations. Participants were trained on a rule-based or an information–integration categorization task and then the association between stimulus and category label, or between category label and response location was altered. For rule-based categories, and in line with previous research, breaking the association between stimulus and category label caused more interference than breaking the association between category label and response location. However, no differences in recovery rate emerged. For information–integration categories, breaking the association between stimulus and category label caused more interference and led to greater recovery than breaking the association between category label and response location. These results provide evidence that information–integration category learning is mediated by separate stimulus-to-label and label-to-response associations. Implications for the neurobiological basis of these two learned associations are discussed

Recognition without identification, erroneous familiarity, and déjà vu

Déjà vu is characterized by the recognition of a situation concurrent with the awareness that this recognition is inappropriate. Although forms of déjà vu resolve in favor of the inappropriate recognition and therefore have behavioral consequences, typical déjà vu experiences resolve in favor of the awareness that the sensation of recognition is inappropriate. The resultant lack of behavioral modification associated with typical déjà vu means that clinicians and experimenters rely heavily on self-report when observing the experience. In this review, we focus on recent déjà vu research. We consider issues facing neuropsychological, neuroscientific, and cognitive experimental frameworks attempting to explore and experimentally generate the experience. In doing this, we suggest the need for more experimentation and amore cautious interpretation of research findings, particularly as many techniques being used to explore déjà vu are in the early stages of development.PostprintPeer reviewe

Scanning the horizon: towards transparent and reproducible neuroimaging research

Functional neuroimaging techniques have transformed our ability to probe the neurobiological basis of behaviour and are increasingly being applied by the wider neuroscience community. However, concerns have recently been raised that the conclusions that are drawn from some human neuroimaging studies are either spurious or not generalizable. Problems such as low statistical power, flexibility in data analysis, software errors and a lack of direct replication apply to many fields, but perhaps particularly to functional MRI. Here, we discuss these problems, outline current and suggested best practices, and describe how we think the field should evolve to produce the most meaningful and reliable answers to neuroscientific questions