Task-based Experiments Research Articles

Spontaneous cortical activity is altered in persons with HIV and related to domain-specific cognitive function.

Whilst the average lifespan of persons with HIV now approximates that of the general population, these individuals are at a much higher risk of developing cognitive impairment with ∼35-70% experiencing at least subtle cognitive deficits. Previous works suggest that HIV impacts both low-level primary sensory regions and higher-level association cortices. Notably, multiple neuroHIV studies have reported elevated levels of spontaneous cortical activity during the pre-stimulus baseline period of task-based experiments, but only a few have examined such activity during resting-state conditions. In the current study, we examined such spontaneous cortical activity using magnetoencephalography in 79 persons with HIV and 83 demographically matched seronegative controls and related this neural activity to performance on neuropsychological assessments of cognitive function. Consistent with previous works, persons with HIV exhibited stronger spontaneous gamma activity, particularly in inferior parietal, prefrontal and superior temporal cortices. In addition, serostatus moderated the relationship between spontaneous beta activity and attention, motor and processing speed scores, with controls but not persons with HIV showing stronger beta activity with better performance. The current results suggest that HIV predominantly impacts spontaneous activity in association cortices, consistent with alterations in higher-order brain function, and may be attributable to deficient GABAergic signalling, given its known role in the generation of gamma and beta oscillations. Overall, these effects align with previous studies showing aberrant spontaneous activity in persons with HIV and provide a critical new linkage to domain-specific cognitive dysfunction.

The efficacy and methodology of using near-infrared spectroscopy to determine resting-state brain networks.

Functional connectivity is a critical aspect of brain function and is essential for understanding, diagnosing, and treating neurological and psychiatric disorders. It refers to the synchronous activity between different regions of the brain, which gives rise to communication and information processing. Resting-state functional connectivity is a subarea of study that allows researchers to examine brain activity in the absence of a task or stimulus. This can provide insight into the brain's intrinsic functional architecture and help identify neural networks that are active during rest. Thus, determining functional connectivity topography is valuable both clinically and in research. Traditional methods using functional magnetic resonance imaging have proven to be effective, however, they have their limitations. In this review, we investigate the feasibility of using functional near-infrared spectroscopy (fNIRS) as a low-cost, portable alternative for measuring functional connectivity. We first establish fNIRS' ability to detect localized brain activity during task-based experiments. Next, we verify its use in resting-state studies with results showing a high degree of correspondence with resting-state functional magnetic resonance imaging (rs-fMRI). Also discussed are various data-processing methods and the validity of filtering the global signal, which is the current standard for analysis. We consider the possible origins of the global signal, if it contains pertinent neuronal information that could be of importance in better understanding neuronal networks, and what we believe is the best method of approaching signal analysis and regression.

Altered brain activity in unipolar depression unveiled using connectomics

Over 20 years of neuroimaging experiments into aberrant task-based brain activity in unipolar depression have failed to reliably delineate a convergent set of anatomical regions. Here we examined whether study-derived coordinates might delineate a dysfunctional brain network in unipolar depression rather than isolated neuroanatomical foci, utilizing data from 57 studies with 99 individual neuroimaging task-based experiments, testing either emotional or cognitive processing (n = 1,058). We further assessed clinical relevance by computing optimal network-based personalized targets in 26 individuals who previously received transcranial magnetic stimulation for unipolar depression. Although coordinates were neuroanatomically heterogeneous, they localized to highly robust distributed brain networks. Importantly, these networks closely recapitulated clinically meaningful and independently derived models of depression circuitry, quantified by spatial correlation (P < 0.00002). Therapeutic outcome of transcranial magnetic stimulation was dependent on how effectively this circuit was targeted (P = 0.018). These findings indicate that neuroimaging findings in depression, which previously appeared irreconcilable, localize to highly robust and clinically meaningful distributed brain networks. Cash et al. examined whether coordinates derived from neuroimaging studies might delineate a dysfunctional brain network in unipolar depression rather than isolated neuroanatomical foci. The authors found highly robust distributed brain networks that closely recapitulate clinically meaningful models of depression circuitry.

An empirical evaluation of two natural hand interaction systems in augmented reality

Human-computer interaction based on hand gesture tracking is not uncommon in Augmented Reality. In fact, the most recent optical Augmented Reality devices include this type of natural interaction. However, due to hardware and system limitations, these devices, more often than not, settle for semi-natural interaction techniques, which may not always be appropriate for some of the tasks needed in Augmented Reality applications. For this reason, we compare two different optical Augmented Reality setups equipped with hand tracking. The first one is based on a Microsoft HoloLens (released in 2016) and the other one is based on a Magic Leap One (released more than two years later). Both devices offer similar solutions for the visualization and registration problems but differ in the hand tracking approach, since the former uses a metaphoric hand-gesture tracking and the latter relies on an isomorphic approach. We raise seven research questions regarding these two setups, which we answer after performing two task-based experiments using virtual elements, of different sizes, that are moved using natural hand interaction. The questions deal with the accuracy and performance achieved with these setups and also with user preference, recommendation and perceived usefulness. For this purpose, we collect both subjective and objective data about the completion of these tasks. Our initial hypothesis was that there would be differences, in favor of the isomorphic and newer setup, in the use of hand interaction. However, the results surprisingly show that there are very small objective differences between these setups, and the isomorphic approach is not significantly better in terms of accuracy and mistakes, although it allows a faster completion of one of the tasks. In addition, no remarkable statistically significant differences can be found between the two setups in the subjective datasets gathered through a specific questionnaire. We also analyze the opinions of the participants in terms of usefulness, preference and recommendation. The results show that, although the Magic Leap-based system gets more support, the differences are not statistically significant.

Does embodied simulation contain schematic motional imagery when it comes to action concepts?

This study explores the reasons behind the high frequency of homonym words in languages, despite their lack of semantic relationships. Additionally, it survey how it does not contradict language optimality to have a significant amount of homonyms with no semantic relationship between them. The study is based on the Natural Theory of Language and Embodied Simulation Theory. Its goal is to illustrate the gap between linguistics and neurological studies. Moreover, its findings suggest that linguistics can pave the way for deeper neural studies. The study puts forward the hypothesis of Schematic Motional Imageries (SMI). Each SMI works as a framework for structuring different concepts with a single form. Each SMI contains a linguistic form and imagery, these present as generalizations of different conceptual mappings, based on special body movements. Once these SMI emerge in a language they are a frame to create both concrete and abstract concepts with identical or related linguistic forms. SMI provides findings in favor of the Neural Reuse Theory. Accordingly, it suggests that SMIs can interfere with neural activation of the motor cortex areas in task-based experiments. Finally, the presence of different SMIs in languages displays the role of social cognition and historical backgrounds of different societies, suggesting a similarity in distinctive aspects of common actions.

Musical Expertise Shapes Functional and Structural Brain Networks Independent of Absolute Pitch Ability.

Professional musicians are a popular model for investigating experience-dependent plasticity in human large-scale brain networks. A minority of musicians possess absolute pitch, the ability to name a tone without reference. The study of absolute pitch musicians provides insights into how a very specific talent is reflected in brain networks. Previous studies of the effects of musicianship and absolute pitch on large-scale brain networks have yielded highly heterogeneous findings regarding the localization and direction of the effects. This heterogeneity was likely influenced by small samples and vastly different methodological approaches. Here, we conducted a comprehensive multimodal assessment of effects of musicianship and absolute pitch on intrinsic functional and structural connectivity using a variety of commonly used and state-of-the-art multivariate methods in the largest sample to date (n = 153 female and male human participants; 52 absolute pitch musicians, 51 non-absolute pitch musicians, and 50 non-musicians). Our results show robust effects of musicianship in interhemispheric and intrahemispheric connectivity in both structural and functional networks. Crucially, most of the effects were replicable in both musicians with and without absolute pitch compared with non-musicians. However, we did not find evidence for an effect of absolute pitch on intrinsic functional or structural connectivity in our data: The two musician groups showed strikingly similar networks across all analyses. Our results suggest that long-term musical training is associated with robust changes in large-scale brain networks. The effects of absolute pitch on neural networks might be subtle, requiring very large samples or task-based experiments to be detected.SIGNIFICANCE STATEMENT A question that has fascinated neuroscientists, psychologists, and musicologists for a long time is how musicianship and absolute pitch, the rare talent to name a tone without reference, are reflected in large-scale networks of the human brain. Much is still unknown as previous studies have reported widely inconsistent results based on small samples. Here, we investigate the largest sample of musicians and non-musicians to date (n = 153) using a multitude of established and novel analysis methods. Results provide evidence for robust effects of musicianship on functional and structural networks that were replicable in two separate groups of musicians and independent of absolute pitch ability.

Neural correlates of unstructured motor behaviors

Objective. We studied the relationship between uninstructed, unstructured movements and neural activity in three epilepsy patients with intracranial electroencephalographic (iEEG) recordings. Approach. We used a custom system to continuously record high definition video precisely time-aligned to clinical iEEG data. From these video recordings, movement periods were annotated via semi-automatic tracking based on dense optical flow. Main results. We found that neural signal features (8–32 Hz and 76–100 Hz power) previously identified from task-based experiments are also modulated before and during a variety of movement behaviors. These movement behaviors are coarsely labeled by time period and movement side (e.g. ‘Idle’ and ‘Move’, ‘Right’ and ‘Left’); movements within a label can include a wide variety of uninstructed behaviors. A rigorous nested cross-validation framework was used to classify both movement onset and lateralization with statistical significance for all subjects. Significance. We demonstrate an evaluation framework to study neural activity related to natural movements not evoked by a task, annotated over hours of video. This work further establishes the feasibility to study neural correlates of unstructured behavior through continuous recording in the epilepsy monitoring unit. The insights gained from such studies may advance our understanding of how the brain naturally controls movement, which may inform the development of more robust and generalizable brain–computer interfaces.

A Comprehensive Review of Magnetoencephalography (MEG) Studies for Brain Functionality in Healthy Aging and Alzheimer's Disease (AD).

Neural oscillations were established with their association with neurophysiological activities and the altered rhythmic patterns are believed to be linked directly to the progression of cognitive decline. Magnetoencephalography (MEG) is a non-invasive technique to record such neuronal activity due to excellent temporal and fair amount of spatial resolution. Single channel, connectivity as well as brain network analysis using MEG data in resting state and task-based experiments were analyzed from existing literature. Single channel analysis studies reported a less complex, more regular and predictable oscillations in Alzheimer's disease (AD) primarily in the left parietal, temporal and occipital regions. Investigations on both functional connectivity (FC) and effective (EC) connectivity analysis demonstrated a loss of connectivity in AD compared to healthy control (HC) subjects found in higher frequency bands. It has been reported from multiplex network of MEG study in AD in the affected regions of hippocampus, posterior default mode network (DMN) and occipital areas, however, conclusions cannot be drawn due to limited availability of clinical literature. Potential utilization of high spatial resolution in MEG likely to provide information related to in-depth brain functioning and underlying factors responsible for changes in neuronal waves in AD. This review is a comprehensive report to investigate diagnostic biomarkers for AD may be identified by from MEG data. It is also important to note that MEG data can also be utilized for the same pursuit in combination with other imaging modalities.

A window‐less approach for capturing time‐varying connectivity in fMRI data reveals the presence of states with variable rates of change

Functional connectivity during the resting state has been shown to change over time (i.e., has a dynamic connectivity). However, resting-state fluctuations, in contrast to task-based experiments, are not initiated by an external stimulus. Consequently, a more complicated method needs to be designed to measure the dynamic connectivity. Previous approaches have been based on assumptions regarding the nature of the underlying dynamic connectivity to compensate for this knowledge gap. The most common assumption is what we refer to as locality assumption. Under a locality assumption, a single connectivity state can be estimated from data that are close in time. This assumption is so natural that it has been either explicitly or implicitly embedded in many current approaches to capture dynamic connectivity. However, an important drawback of methods using this assumption is they are unable to capture dynamic changes in connectivity beyond the embedded rate while, there has been no evidence that the rate of change in brain connectivity matches the rates enforced by this assumption. In this study, we propose an approach that enables us to capture functional connectivity with arbitrary rates of change, varying from very slow to the theoretically maximum possible rate of change, which is only imposed by the sampling rate of the imaging device. This method allows us to observe unique patterns of connectivity that were not observable with previous approaches. As we explain further, these patterns are also significantly correlated to the age and gender of study subjects, which suggests they are also neurobiologically related.

Heterogeneous fractionation profiles of meta-analytic coactivation networks

Computational cognitive neuroimaging approaches can be leveraged to characterize the hierarchical organization of distributed, functionally specialized networks in the human brain. To this end, we performed large-scale mining across the BrainMap database of coordinate-based activation locations from over 10,000 task-based experiments. Meta-analytic coactivation networks were identified by jointly applying independent component analysis (ICA) and meta-analytic connectivity modeling (MACM) across a wide range of model orders (i.e., d=20–300). We then iteratively computed pairwise correlation coefficients for consecutive model orders to compare spatial network topologies, ultimately yielding fractionation profiles delineating how “parent” functional brain systems decompose into constituent “child” sub-networks. Fractionation profiles differed dramatically across canonical networks: some exhibited complex and extensive fractionation into a large number of sub-networks across the full range of model orders, whereas others exhibited little to no decomposition as model order increased. Hierarchical clustering was applied to evaluate this heterogeneity, yielding three distinct groups of network fractionation profiles: high, moderate, and low fractionation. BrainMap-based functional decoding of resultant coactivation networks revealed a multi-domain association regardless of fractionation complexity. Rather than emphasize a cognitive-motor-perceptual gradient, these outcomes suggest the importance of inter-lobar connectivity in functional brain organization. We conclude that high fractionation networks are complex and comprised of many constituent sub-networks reflecting long-range, inter-lobar connectivity, particularly in fronto-parietal regions. In contrast, low fractionation networks may reflect persistent and stable networks that are more internally coherent and exhibit reduced inter-lobar communication.

Temporal Non-Local Means Filtering Reveals Real-Time Whole-Brain Cortical Interactions in Resting fMRI.

Intensity variations over time in resting BOLD fMRI exhibit spatial correlation patterns consistent with a set of large scale cortical networks. However, visualizations of this data on the brain surface, even after extensive preprocessing, are dominated by local intensity fluctuations that obscure larger scale behavior. Our novel adaptation of non-local means (NLM) filtering, which we refer to as temporal NLM or tNLM, reduces these local fluctuations without the spatial blurring that occurs when using standard linear filtering methods. We show examples of tNLM filtering that allow direct visualization of spatio-temporal behavior on the cortical surface. These results reveal patterns of activity consistent with known networks as well as more complex dynamic changes within and between these networks. This ability to directly visualize brain activity may facilitate new insights into spontaneous brain dynamics. Further, temporal NLM can also be used as a preprocessor for resting fMRI for exploration of dynamic brain networks. We demonstrate its utility through application to graph-based functional cortical parcellation. Simulations with known ground truth functional regions demonstrate that tNLM filtering prior to parcellation avoids the formation of false parcels that can arise when using linear filtering. Application to resting fMRI data from the Human Connectome Project shows significant improvement, in comparison to linear filtering, in quantitative agreement with functional regions identified independently using task-based experiments as well as in test-retest reliability.

Motor Readiness Increases Brain Connectivity Between Default-Mode Network and Motor Cortex: Impact on Sampling Resting Periods from fMRI Event-Related Studies.

The default-mode network (DMN) has been implicated in many conditions. One particular function relates to its role in motor preparation. However, the possibly complex relationship between DMN activity and motor preparation has not been fully explored. Dynamic interactions between default mode and motor networks may compromise the ability to evaluate intrinsic connectivity using resting period data extracted from task-based experiments. In this study, we investigated alterations in connectivity between the DMN and the motor network that are associated with motor readiness during the intervals between motor task trials. fMRI data from 20 normal subjects were acquired under three conditions: pure resting state; resting state interleaved with brief, cued right-hand movements at constant intervals (lower readiness); and resting state interleaved with the same movements at unpredictable intervals (higher readiness). The functional connectivity between regions of motor and DMNs was assessed separately for movement periods and intertask intervals. We found a negative relationship between the DMN and the left sensorimotor cortex during the task periods for both motor conditions. Furthermore, during the intertask intervals of the unpredictable condition, the DMN showed a positive relationship with right sensorimotor cortex and a negative relation with the left sensorimotor cortex. These findings indicate a specific modulation on motor processing according to the state of motor readiness. Therefore, connectivity studies using task-based fMRI to probe DMN should consider the influence of motor system modulation when interpreting the results.

Prospective motion correction of 3D echo-planar imaging data for functional MRI using optical tracking

We evaluated the performance of an optical camera based prospective motion correction (PMC) system in improving the quality of 3D echo-planar imaging functional MRI data. An optical camera and external marker were used to dynamically track the head movement of subjects during fMRI scanning. PMC was performed by using the motion information to dynamically update the sequence's RF excitation and gradient waveforms such that the field-of-view was realigned to match the subject's head movement. Task-free fMRI experiments on five healthy volunteers followed a 2×2×3 factorial design with the following factors: PMC on or off; 3.0mm or 1.5mm isotropic resolution; and no, slow, or fast head movements. Visual and motor fMRI experiments were additionally performed on one of the volunteers at 1.5mm resolution comparing PMC on vs PMC off for no and slow head movements. Metrics were developed to quantify the amount of motion as it occurred relative to k-space data acquisition. The motion quantification metric collapsed the very rich camera tracking data into one scalar value for each image volume that was strongly predictive of motion-induced artifacts. The PMC system did not introduce extraneous artifacts for the no motion conditions and improved the time series temporal signal-to-noise by 30% to 40% for all combinations of low/high resolution and slow/fast head movement relative to the standard acquisition with no prospective correction. The numbers of activated voxels (p<0.001, uncorrected) in both task-based experiments were comparable for the no motion cases and increased by 78% and 330%, respectively, for PMC on versus PMC off in the slow motion cases. The PMC system is a robust solution to decrease the motion sensitivity of multi-shot 3D EPI sequences and thereby overcome one of the main roadblocks to their widespread use in fMRI studies.

Multi-viewpoint Smartphone AR-based Learning System for Solar Movement Observations

Understanding solar movement (e.g., solar diurnal motion) is difficult for those are beginning to learn about astronomy. Previous research has revealed that observation-based learning can help make astronomical phenomena clearer to understand for such learners. In this research, Smartphone Augmented Reality (AR) technology and 3D content were used to develop a multi-viewpoint Smartphone AR-based learning system (M-VSARLS) for solar movement observations that can be used in the real-world environment. The goal of this research is to assess the usefulness of the system, usability of the AR function and 3D content, and the overall effect of the system on the learner’s motivation through task-based experiments with follow-up questionnaires. The results show that the M-VSARL system is effective in improving the observational skills and learning ability of learners, and in enhancing their motivation to learn about solar movement.

Guest Editorial: Multimedia, Communications, Services and Security

“Multimedia, Communications, Services and Security” has been an important research topic in recent years and a key part of innovative ICT projects. In particular, numerous intelligent systems benefit from the integration of tools and solutions proposed by scientists, such as image, video and audio processing, development of intelligent sensors, interlinking heterogeneous subsystems with intraand internet services, and providing reliable and secure communications channels. This special issue consists of eleven papers, which are briefly discussed as follows. In “Detection and Localization of Selected Acoustic Events in Acoustic Fields for Smart Surveillance Applications,” the authors provide a method for determining the position of selected sound events in 3D space. When an event is detected (e.g. speech or impulse sound), the sound source is located so that it can be used in surveillance systems. The following two papers are related to Quality of Experience issues. “Quality Assessment of Visual and Automatic Licence Plate Recognition” discusses the Quality of Experience in Automatic Licence Plate Recognition tasks based on surveillance videos streamed under constrained networking conditions. Based on the presented assumptions, the authors implement automatic recognition algorithms and compare their accuracy against user perception in order to obtain quality models and critical quality thresholds. “A Journey from Subjective Psychophysical Experiments to Objective Quality Optimisation” presents two task-based video experiments in surveillance and medical applications. The problem of quality assessment, from subjective psychophysical experiments to objective quality models, is discussed. Example test results and models are provided. Multimed Tools Appl DOI 10.1007/s11042-013-1498-5

Volitional Control of Cortical Oscillations and Synchrony

Oscillatory activity in motor cortex has been observed in many experimental contexts, leading to various hypotheses about its possible behavioral function. In this issue of Neuron, Engelhard etal. (2013) report that oscillations can be volitionally controlled, opening new directions to explore their function and underlying mechanisms.

Graphical presentation of data for health policy decisions: An exploratory online decision task experiment to measure effectiveness

This paper presents a method for using online, task-based experiments to test information presentations. It was designed to provide important information as a basis for further research which might be conducted in the context of healthcare policy making. A tabulated, numerical presentation of information for a simplified example of a cost-effectiveness decision task was compared to a presentation which also included graphical Isotype-style elements and vertical bars. The online study was accessed 244 times over a 3 6 day period. The data collected showed that the two presentation methods were largely similar in terms of average decision accuracy and time taken. However, performance was linked to the participants'preference for the presentations. The study recommends that online, task-based experiments can be used to compare information presentations, where a large number of people can be invited to take part.