The Effects of Alcohol Intoxication on Neuronal Activation at Different Levels of Cognitive Load

The aim of this study was to investigate how alcohol intoxication at two blood alcohol concentrations (BAC) affected neuronal activation during increasing levels of cognitive load. For this purpose we used functional magnetic resonance imaging (fMRI) together with a working memory n-back paradigm with three levels of difficulty. Twenty-five healthy male participants were scanned twice on two separate days. Participants in the control group (N=13) were scanned after drinking a soft-drink at both scanning sessions, while participants in the alcohol group (N=12) were scanned once after drinking an alcoholic beverage resulting in a BAC of 0.02%, and once after drinking an alcoholic beverage resulting in a BAC of 0.08%. A decrease in neuronal activation was seen in the dorsal anterior cingulate cortex (dACC) and in the cerebellum in the alcohol group at the BAC of 0.08% when the participants performed the most demanding task. The dACC is important in cognitive control, working memory, response inhibition, decision making and in error monitoring. The results have revealed that the effect of alcohol intoxication on brain activity is dependent on BAC and of cognitive load

An fMRI study of joint action–varying levels of cooperation correlates with activity in control networks

As social agents, humans continually interact with the people around them. Here, motor cooperation was investigated using a paradigm in which pairs of participants, one being scanned with fMRI, jointly controlled a visually presented object with joystick movements. The object oscillated dynamically along two dimensions, color and width of gratings, corresponding to the two cardinal directions of joystick movements. While the overall control of each participant on the object was kept constant, the amount of cooperation along the two dimensions varied along four levels, from no (each participant controlled one dimension exclusively) to full (each participant controlled half of each dimension) cooperation. Increasing cooperation correlated with BOLD signal in the left parietal operculum and anterior cingulate cortex (ACC), while decreasing cooperation correlated with activity in the right inferior frontal and superior temporal gyri, the intraparietal sulci and inferior temporal gyri bilaterally, and the dorsomedial prefrontal cortex. As joint performance improved with the level of cooperation, we assessed the brain responses correlating with behavior, and found that activity in most of the areas associated with levels of cooperation also correlated with the joint performance. The only brain area found exclusively in the negative correlation with cooperation was in the dorso medial frontal cortex, involved in monitoring action outcome. Given the cluster location and condition-related signal change, we propose that this region monitored actions to extract the level of cooperation in order to optimize the joint response. Our results, therefore, indicate that, in the current experimental paradigm involving joint control of a visually presented object with joystick movements, the level of cooperation affected brain networks involved in action control, but not mentalizing

Young Adult Smokers\u27 Neural Response to Graphic Cigarette Warning Labels

Introduction: The study examined young adult smokers\u27 neural response to graphic warning labels (GWLs) on cigarette packs using functional magnetic resonance imaging (fMRI). Methods: Nineteen young adult smokers (M age 22.9, 52.6% male, 68.4% non-white, M 4.3 cigarettes/day) completed pre-scan, self-report measures of demographics, cigarette smoking behavior, and nicotine dependence, and an fMRI scanning session. During the scanning session participants viewed cigarette pack images (total 64 stimuli, viewed 4 s each) that varied based on the warning label (graphic or visually occluded control) and pack branding (branded or plain packaging) in an event-related experimental design. Participants reported motivation to quit (MTQ) in response to each image using a push-button control. Whole-brain blood oxygenation level-dependent (BOLD) functional images were acquired during the task. Results: GWLs produced significantly greater self-reported MTQ than control warnings (p \u3c .001). Imaging data indicate stronger neural activation in response to GWLs than the control warnings at a cluster-corrected threshold p \u3c .001 in medial prefrontal cortex, amygdala, medial temporal lobe, and occipital cortex. There were no significant differences in response to warnings on branded versus plain cigarette packages. Conclusions: In this sample of young adult smokers, GWLs promoted neural activation in brain regions involved in cognitive and affective decision-making and memory formation and the effects of GWLs did not differ on branded or plain cigarette packaging. These findings complement other recent neuroimaging GWL studies conducted with older adult smokers and with adolescents by demonstrating similar patterns of neural activation in response to GWLs among young adult smokers

Sparse temporally dynamic resting-state functional connectivity networks for early MCI identification

In conventional resting-state functional MRI (R-fMRI) analysis, functional connectivity is assumed to be temporally stationary, overlooking neural activities or interactions that may happen within the scan duration. Dynamic changes of neural interactions can be reflected by variations of topology and correlation strength in temporally correlated functional connectivity networks. These connectivity networks may potentially capture subtle yet short neural connectivity disruptions induced by disease pathologies. Accordingly, we are motivated to utilize disrupted temporal network properties for improving control-patient classification performance. Specifically, a sliding window approach is firstly employed to generate a sequence of overlapping R-fMRI sub-series. Based on these sub-series, sliding window correlations, which characterize the neural interactions between brain regions, are then computed to construct a series of temporal networks. Individual estimation of these temporal networks using conventional network construction approaches fails to take into consideration intrinsic temporal smoothness among successive overlapping R-fMRI subseries. To preserve temporal smoothness of R-fMRI sub-series, we suggest to jointly estimate the temporal networks by maximizing a penalized log likelihood using a fused sparse learning algorithm. This sparse learning algorithm encourages temporally correlated networks to have similar network topology and correlation strengths. We design a disease identification framework based on the estimated temporal networks, and group level network property differences and classification results demonstrate the importance of including temporally dynamic R-fMRI scan information to improve diagnosis accuracy of mild cognitive impairment patients

TempoCave: Visualizing Dynamic Connectome Datasets to Support Cognitive Behavioral Therapy

We introduce TempoCave, a novel visualization application for analyzing dynamic brain networks, or connectomes. TempoCave provides a range of functionality to explore metrics related to the activity patterns and modular affiliations of different regions in the brain. These patterns are calculated by processing raw data retrieved functional magnetic resonance imaging (fMRI) scans, which creates a network of weighted edges between each brain region, where the weight indicates how likely these regions are to activate synchronously. In particular, we support the analysis needs of clinical psychologists, who examine these modular affiliations and weighted edges and their temporal dynamics, utilizing them to understand relationships between neurological disorders and brain activity, which could have a significant impact on the way in which patients are diagnosed and treated. We summarize the core functionality of TempoCave, which supports a range of comparative tasks, and runs both in a desktop mode and in an immersive mode. Furthermore, we present a real-world use case that analyzes pre- and post-treatment connectome datasets from 27 subjects in a clinical study investigating the use of cognitive behavior therapy to treat major depression disorder, indicating that TempoCave can provide new insight into the dynamic behavior of the human brain

fMRI scanner noise interaction with affective neural processes

The purpose of the present study was the investigation of interaction effects between functional MRI scanner noise and affective neural processes. Stimuli comprised of psychoacoustically balanced musical pieces, expressing three different emotions (fear, neutral, joy). Participants (N=34, 19 female) were split into two groups, one subjected to continuous scanning and another subjected to sparse temporal scanning that features decreased scanner noise. Tests for interaction effects between scanning group (sparse/quieter vs continuous/noisier) and emotion (fear, neutral, joy) were performed. Results revealed interactions between the affective expression of stimuli and scanning group localized in bilateral auditory cortex, insula and visual cortex (calcarine sulcus). Post-hoc comparisons revealed that during sparse scanning, but not during continuous scanning, BOLD signals were significantly stronger for joy than for fear, as well as stronger for fear than for neutral in bilateral auditory cortex. During continuous scanning, but not during sparse scanning, BOLD signals were significantly stronger for joy than for neutral in the left auditory cortex and for joy than for fear in the calcarine sulcus. To the authors' knowledge, this is the first study to show a statistical interaction effect between scanner noise and affective processes and extends evidence suggesting scanner noise to be an important factor in functional MRI research that can affect and distort affective brain processes

Using fMRI to dissociate sensory encoding from cognitive evaluation of heat pain intensity

Neuroimaging studies of painful stimuli in humans have identified a network of brain regions that is more extensive than identified previously in electrophysiological and anatomical studies of nociceptive pathways. This extensive network has been described as a pain matrix of brain regions that mediate the many interrelated aspects of conscious processing of nociceptive input such as perception, evaluation, affective response, and emotional memory. We used functional magnetic resonance imaging in healthy human subjects to distinguish brain regions required for pain sensory encoding from those required for cognitive evaluation of pain intensity. The results suggest that conscious cognitive evaluation of pain intensity in the absence of any sensory stimulation activates a network that includes bilateral anterior insular cortex/frontal operculum, dorsal lateral prefrontal cortex, bilateral medial prefrontal cortex/anterior cingulate cortex, right superior parietal cortex, inferior parietal lobule, orbital prefrontal cortex, and left occipital cortex. Increased activity common to both encoding and evaluation was observed in bilateral anterior insula/frontal operculum and medial prefrontal cortex/anterior cingulate cortex. We hypothesize that these two regions play a crucial role in bridging the encoding of pain sensation and the cognitive processing of sensory input. Hum Brain Mapp, 2005. © 2005 Wiley-Liss, Inc.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/55800/1/20213_ftp.pd

fMRI of Monkey Visual Cortex

While functional magnetic resonance imaging (fMRI) is now used widely for demonstrating neural activity-related signals associated with perceptual, motor, and cognitive processes in humans, to date this technique has not been developed for use with nonhuman primates. fMRI in monkeys offers a potentially valuable experimental approach for investigating brain function, which will complement and aid existing techniques such as electrophysiology and the behavioral analysis of the effects of brain lesions. There are, however, a number of significant technical challenges involved in using fMRI with monkeys. Here, we describe the procedures by which we have overcome these challenges to carry out successful fMRI experiments in an alert monkey, and we present the first evidence of activity-related fMRI signals from monkey cerebral cortex