A rapid fMRI task battery for mapping of visual, motor, cognitive, and emotional function

Distributed network flows generate localized category selectivity in human visual cortex

A central goal of neuroscience is to understand how function-relevant brain activations are generated. Here we test the hypothesis that function-relevant brain activations are generated primarily by distributed network flows. We focused on visual processing in human cortex, given the long-standing literature supporting the functional relevance of brain activations in visual cortex regions exhibiting visual category selectivity. We began by using fMRI data from N = 352 human participants to identify category-specific responses in visual cortex for images of faces, places, body parts, and tools. We then systematically tested the hypothesis that distributed network flows can generate these localized visual category selective responses. This was accomplished using a recently developed approach for simulating - in a highly empirically constrained manner - the generation of task-evoked brain activations by modeling activity flowing over intrinsic brain connections. We next tested refinements to our hypothesis, focusing on how stimulus-driven network interactions initialized in V1 generate downstream visual category selectivity. We found evidence that network flows directly from V1 were sufficient for generating visual category selectivity, but that additional, globally distributed (whole-cortex) network flows increased category selectivity further. Using null network architectures we also found that each region's unique intrinsic "connectivity fingerprint" was key to the generation of category selectivity. These results generalized across regions associated with all four visual categories tested (bodies, faces, places, and tools), and provide evidence that the human brain's intrinsic network organization plays a prominent role in the generation of functionally relevant, localized responses.

Shared subcortical arousal systems across sensory modalities during transient modulation of attention

Subcortical arousal systems are known to play a key role in controlling sustained changes in attention and conscious awareness. Recent studies indicate that these systems have a major influence on short-term dynamic modulation of visual attention, but their role across sensory modalities is not fully understood. In this study, we investigated shared subcortical arousal systems across sensory modalities during transient changes in attention using block and event-related fMRI paradigms. We analyzed massive publicly available fMRI datasets collected while 1,561 participants performed visual, auditory, tactile, and taste perception tasks. Our analyses revealed a shared circuit of subcortical arousal systems exhibiting early transient increases in activity in midbrain reticular formation and central thalamus across perceptual modalities, as well as less consistent increases in pons, hypothalamus, basal forebrain, and basal ganglia. Identifying these networks is critical for understanding mechanisms of normal attention and consciousness and may help facilitate subcortical targeting for therapeutic neuromodulation.

Contributions of network structure, chemoarchitecture and diagnostic categories to transitions between cognitive topographies

The mechanisms linking the brain's network structure to cognitively relevant activation patterns remain largely unknown. Here, by leveraging principles of network control, we show how the architecture of the human connectome shapes transitions between 123 experimentally defined cognitive activation maps (cognitive topographies) from the NeuroSynth meta-analytic database. Specifically, we systematically integrated large-scale multimodal neuroimaging data from functional magnetic resonance imaging, diffusion tractography, cortical morphometry and positron emission tomography to simulate how anatomically guided transitions between cognitive states can be reshaped by neurotransmitter engagement or by changes in cortical thickness. Our model incorporates neurotransmitter-receptor density maps (18 receptors and transporters) and maps of cortical thickness pertaining to a wide range of mental health, neurodegenerative, psychiatric and neurodevelopmental diagnostic categories (17,000 patients and 22,000 controls). The results provide a comprehensive look-up table charting how brain network organization and chemoarchitecture interact to manifest different cognitive topographies, and establish a principled foundation for the systematic identification of ways to promote selective transitions between cognitive topographies.

Amygdala fMRI-A Critical Appraisal of the Extant Literature

Even before the advent of fMRI, the amygdala occupied a central space in the affective neurosciences. Yet this amygdala-centred view on emotion processing gained even wider acceptance after the inception of fMRI in the early 1990s, a landmark that triggered a goldrush of fMRI studies targeting the amygdala in vivo. Initially, this amygdala fMRI research was mostly confined to task-activation studies measuring the magnitude of the amygdala's response to emotional stimuli. Later, interest began to shift more towards the study of the amygdala's resting-state functional connectivity and task-based psychophysiological interactions. Later still, the test-retest reliability of amygdala fMRI came under closer scrutiny, while at the same time, amygdala-based real-time fMRI neurofeedback gained widespread popularity. Each of these major subdomains of amygdala fMRI research has left its marks on the field of affective neuroscience at large. The purpose of this review is to provide a critical assessment of this literature. By integrating the insights garnered by these research branches, we aim to answer the question: What part (if any) can amygdala fMRI still play within the current landscape of affective neuroscience? Our findings show that serious questions can be raised with regard to both the reliability and validity of amygdala fMRI. These conclusions force us to cast doubt on the continued viability of amygdala fMRI as a core pilar of the affective neurosciences.

From Maps to Models: A Survey on the Reliability of Small Studies of Task-Based fMRI

Task-based functional magnetic resonance imaging is a powerful tool for studying brain function, but neuroimaging research produces ongoing concerns regarding small-sample studies and how to interpret them. Although it is well understood that larger samples are preferable, many situations require researchers to make judgments from small studies, including reviewing the existing literature, analyzing pilot data, or assessing subsamples. Quantitative guidance on how to make these judgments remains scarce. To address this, we leverage the Human Connectome Project's Young Adult dataset to survey various analyses-from regional activation maps to predictive models. We find that, for some classic analyses such as detecting regional activation or cluster peak location, studies with as few as 40 subjects are adequate, although this depends crucially on effect sizes. For predictive modeling, similar sizes can be adequate for detecting whether features are predictable, but at least an order of magnitude more (at least hundreds) may be required for developing consistent predictions. These results offer valuable insights for designing and interpreting fMRI studies, emphasizing the importance of considering effect size, sample size, and analysis approach when assessing the reliability of findings. We hope that this survey serves as a reference for identifying which kinds of research questions can be reliably answered with small-scale studies.

Human reasoning on social interactions in ecological contexts: insights from the theory of mind brain circuits

Introduction

The relationship between neural social cognition patterns and performance on social cognition tasks in daily life is a topic of debate, with key consideration given to the extent to which theory of mind (ToM) brain circuits share properties reflecting everyday social functioning. To test the efficacy of ecological stimuli in eliciting brain activation within the ToM brain circuits, we adapted the Edinburgh Social Cognition test social scenarios, consisting of dynamic ecological contextually embedded social stimuli, to a fMRI paradigm.

Methods

Forty-two adults (21 men, mean age ± SD = 34.19 years ±12.57) were enrolled and underwent an fMRI assessment which consisted of a ToM task using the Edinburgh Social Cognition test scenarios. We used the same stimuli to prompt implicit (movie viewing) and explicit (silent and two-choice answers) reasoning on cognitive and affective mental states. The fMRI analysis was based on the classical random effect analysis. Group inferences were complemented with supplemental analyses using overlap maps to assess inter-subject variability.

Results

We found that explicit mentalizing reasoning yielded wide neural activations when two-choice answers were used. We also observed that the nature of ToM reasoning, that is, affective or cognitive, played a significant role in activating different neural circuits.

Discussion

The ESCoT stimuli were particularly effective in evoking ToM core neural underpinnings and elicited executive frontal loops. Future work may employ the task in a clinical setting to investigate ToM network reorganization and plasticity.

Optimization and validation of multi-echo, multi-contrast SAGE acquisition in fMRI

The purpose of this study was to optimize and validate a multi-contrast, multi-echo fMRI method using a combined spin- and gradient-echo (SAGE) acquisition. It was hypothesized that SAGE-based blood oxygen level-dependent (BOLD) functional MRI (fMRI) will improve sensitivity and spatial specificity while reducing signal dropout. SAGE-fMRI data were acquired with five echoes (2 gradient-echoes, 2 asymmetric spin-echoes, and 1 spin-echo) across 12 protocols with varying acceleration factors, and temporal SNR (tSNR) was assessed. The optimized protocol was then implemented in working memory and vision tasks in 15 healthy subjects. Task-based analysis was performed using individual echoes, quantitative dynamic relaxation times T2 * and T2, and echo time-dependent weighted combinations of dynamic signals. These methods were compared to determine the optimal analysis method for SAGE-fMRI. Implementation of a multiband factor of 2 and sensitivity encoding (SENSE) factor of 2.5 yielded adequate spatiotemporal resolution while minimizing artifacts and loss in tSNR. Higher BOLD contrast-to-noise ratio (CNR) and tSNR were observed for SAGE-fMRI relative to single-echo fMRI, especially in regions with large susceptibility effects and for T2-dominant analyses. Using a working memory task, the extent of activation was highest with T2 *-weighting, while smaller clusters were observed with quantitative T2 * and T2. SAGE-fMRI couples the high BOLD sensitivity from multi-gradient-echo acquisitions with improved spatial localization from spin-echo acquisitions, providing two contrasts for analysis. SAGE-fMRI provides substantial advantages, including improving CNR and tSNR for more accurate analysis.

Improving Predictability, Test-Retest Reliability and Generalisability of Brain-Wide Associations for Cognitive Abilities via Multimodal Stacking

Brain-wide association studies (BWASs) have attempted to relate cognitive abilities with brain phenotypes, but have been challenged by issues such as predictability, test-retest reliability, and cross-cohort generalisability. To tackle these challenges, we proposed "stacking" that combines brain magnetic resonance imaging of different modalities, from task-fMRI contrasts and functional connectivity during tasks and rest to structural measures, into one prediction model. We benchmarked the benefits of stacking, using the Human Connectome Projects: Young Adults and Aging and the Dunedin Multidisciplinary Health and Development Study. For predictability, stacked models led to out-of-sample r ∼.5-.6 when predicting cognitive abilities at the time of scanning and 36 years earlier. For test-retest reliability, stacked models reached an excellent level of reliability (ICC>.75), even when we stacked only task-fMRI contrasts together. For generalisability, a stacked model with non-task MRI built from one dataset significantly predicted cognitive abilities in other datasets. Altogether, stacking is a viable approach to undertake the three challenges of BWAS for cognitive abilities.

The impact of emotional valence and stimulus habituation on fMRI signal reliability during emotion generation

BACKGROUND

The emotional domain is often impaired across many neurological diseases, for this reason it represents a relevant target of rehabilitation interventions. Functional changes in neural activity related to treatment can be assessed with functional MRI (fMRI) using emotion-generation tasks in longitudinal settings. Previous studies demonstrated that within-subject fMRI signal reliability can be affected by several factors such as repetition suppression, type of task and brain anatomy. However, the differential role of repetition suppression and emotional valence of the stimuli on the fMRI signal reliability and reproducibility during an emotion-generation task involving the vision of emotional pictures is yet to be determined.

METHODS

Sixty-two healthy subjects were enrolled and split into two groups: group A (21 subjects, test-retest reliability on same-day and with same-task-form), group B (30 subjects, test-retest reproducibility with 4-month-interval using two equivalent-parallel forms of the task). Test-retest reliability and reproducibility of fMRI responses and patterns were evaluated separately for positive and negative emotional valence conditions in both groups. The analyses were performed voxel-wise, using the general linear model (GLM), and via a region-of-interest (ROI)-based approach, by computing the intra-class correlation coefficient (ICC) on the obtained contrasts.

RESULTS

The voxel-wise GLM test yielded no significant differences for both conditions in reliability and reproducibility analyses. As to the ROI-based approach, across all areas with significant main effects of the stimuli, the reliability, as measured with ICC, was poor (<0.4) for the positive condition and ranged from poor to excellent (0.4-0.75) for the negative condition. The ICC-based reproducibility analysis, related to the comparison of two different parallel forms, yielded similar results.

DISCUSSION

The voxel-wise GLM analysis failed to capture the poor reliability of fMRI signal which was instead highlighted using the ROI-based ICC analysis. The latter showed higher signal reliability for negative valence stimuli with respect to positive ones. The implementation of two parallel forms allowed to exclude neural suppression as the predominant effect causing low signal reliability, which could be instead ascribed to the employment of different neural strategies to cope with emotional stimuli over time. This is an invaluable information for a better assessment of treatment and rehabilitation effects in longitudinal studies of emotional neural processing.

A personalized cortical atlas for functional regions of interest

Advances in functional MRI (fMRI) allow mapping an individual's brain function in vivo. Task fMRI can localize domain-specific regions of cognitive processing or functional regions of interest (fROIs) within an individual. Moreover, data from resting state (no task) fMRI can be used to define an individual's connectome, which can characterize that individual's functional organization via connectivity-based parcellations. However, can connectivity-based parcellations alone predict an individual's fROIs? Here, we describe an approach to compute individualized rs-fROIs (i.e., regions that correspond to given fROI constructed using only resting state data) for motor control, working memory, high-level vision, and language comprehension. The rs-fROIs were computed and validated using a large sample of young adults (n = 1,018) with resting state and task fMRI from the Human Connectome Project. First, resting state parcellations were defined across a sequence of resolutions from broadscale to fine-grained networks in a training group of 500 individuals. Second, 21 rs-fROIs were defined from the training group by identifying the rs network that most closely matched task-defined fROIs across all individuals. Third, the selectivity of rs-fROIs was investigated in a training set of the remaining 518 individuals. All computed rs-fROIs were indeed selective for their preferred category. Critically, the rs-fROIs had higher selectivity than probabilistic atlas parcels for nearly all fROIs. In conclusion, we present a potential approach to define selective fROIs on an individual-level circumventing the need for multiple task-based localizers.NEW & NOTEWORTHY We compute individualized resting state parcels that identify an individual's own functional regions of interest (fROIs) for high-level vision, language comprehension, motor control, and working memory, using only their functional connectome. This approach demonstrates a rapid and powerful alternative for finding a large set of fROIs in an individual, using only their unique connectivity pattern, which does not require the costly acquisition of multiple fMRI localizer tasks.

The arrow of time of brain signals in cognition: Potential intriguing role of parts of the default mode network

A promising idea in human cognitive neuroscience is that the default mode network (DMN) is responsible for coordinating the recruitment and scheduling of networks for computing and solving task-specific cognitive problems. This is supported by evidence showing that the physical and functional distance of DMN regions is maximally removed from sensorimotor regions containing environment-driven neural activity directly linked to perception and action, which would allow the DMN to orchestrate complex cognition from the top of the hierarchy. However, discovering the functional hierarchy of brain dynamics requires finding the best way to measure interactions between brain regions. In contrast to previous methods measuring the hierarchical flow of information using, for example, transfer entropy, here we used a thermodynamics-inspired, deep learning based Temporal Evolution NETwork (TENET) framework to assess the asymmetry in the flow of events, 'arrow of time', in human brain signals. This provides an alternative way of quantifying hierarchy, given that the arrow of time measures the directionality of information flow that leads to a breaking of the balance of the underlying hierarchy. In turn, the arrow of time is a measure of nonreversibility and thus nonequilibrium in brain dynamics. When applied to large-scale Human Connectome Project (HCP) neuroimaging data from close to a thousand participants, the TENET framework suggests that the DMN plays a significant role in orchestrating the hierarchy, that is, levels of nonreversibility, which changes between the resting state and when performing seven different cognitive tasks. Furthermore, this quantification of the hierarchy of the resting state is significantly different in health compared to neuropsychiatric disorders. Overall, the present thermodynamics-based machine-learning framework provides vital new insights into the fundamental tenets of brain dynamics for orchestrating the interactions between cognition and brain in complex environments.

The CBIT + TMS trial: study protocol for a two-phase randomized controlled trial testing neuromodulation to augment behavior therapy for youth with chronic tics

BACKGROUND

Comprehensive Behavioral Intervention for Tics (CBIT) is a first-line treatment for tic disorders that aims to improve controllability over tics that an individual finds distressing or impairing. However, it is only effective for approximately half of patients. Supplementary motor area (SMA)-directed neurocircuitry plays a strong role in motor inhibition, and activity in this region is thought to contribute to tic expression. Targeted modulation of SMA using transcranial magnetic stimulation (TMS) may increase CBIT efficacy by improving patients' ability to implement tic controllability behaviors.

METHODS

The CBIT + TMS trial is a two-phase, milestone-driven early-stage randomized controlled trial. The trial will test whether augmenting CBIT with inhibitory, non-invasive stimulation of SMA with TMS modifies activity in SMA-mediated circuits and enhances tic controllability in youth ages 12-21 years with chronic tics. Phase 1 will directly compare two rTMS augmentation strategies (1 Hz rTMS vs. cTBS) vs. sham in N = 60 participants. Quantifiable, a priori "Go/No Go Criteria" guide the decision to proceed to phase 2 and the selection of the optimal TMS regimen. Phase 2 will compare the optimal regimen vs. sham and test the link between neural target engagement and clinical outcomes in a new sample of N = 60 participants.

DISCUSSION

This clinical trial is one of few to date testing TMS augmentation of therapy in a pediatric sample. The results will provide insight into whether TMS is a potentially viable strategy for enhancing CBIT efficacy and reveal potential neural and behavioral mechanisms of change.

TRIAL REGISTRATION

ClinicalTrials.gov NCT04578912 . Registered on October 8, 2020.

Data-Driven Pupil Response Profiles as Transdiagnostic Readouts for the Detection of Neurocognitive Functioning in Affective and Anxiety Disorders

BACKGROUND

Neurocognitive functioning is a relevant transdiagnostic dimension in psychiatry. As pupil size dynamics track cognitive load during a working memory task, we aimed to explore if this parameter allows identification of psychophysiological subtypes in healthy participants and patients with affective and anxiety disorders.

METHODS

Our sample consisted of 226 participants who completed the n-back task during simultaneous functional magnetic resonance imaging and pupillometry measurements. We used latent class growth modeling to identify clusters based on pupil size in response to cognitive load. In a second step, these clusters were compared on affective and anxiety symptom levels, performance in neurocognitive tests, and functional magnetic resonance imaging activity.

RESULTS

The clustering analysis resulted in two distinct pupil response profiles: one with a stepwise increasing pupil size with increasing cognitive load (reactive group) and one with a constant pupil size across conditions (nonreactive group). A larger increase in pupil size was significantly associated with better performance in neurocognitive tests in executive functioning and sustained attention. Statistical maps of parametric modulation of pupil size during the n-back task showed the frontoparietal network in the positive contrast and the default mode network in the negative contrast. The pupil response profile of the reactive group was associated with more thalamic activity, likely reflecting better arousal upregulation and less deactivation of the limbic system.

CONCLUSIONS

Pupil measurements have the potential to serve as a highly sensitive psychophysiological readout for detection of neurocognitive deficits in the core domain of executive functioning, adding to the development of valid transdiagnostic constructs in psychiatry.

The CBIT+TMS Trial: study protocol for a two-phase randomized controlled trial testing neuromodulation to augment behavior therapy for youth with chronic tics

Background

Comprehensive Behavioral Intervention for Tics (CBIT) is a first-line treatment for tic disorders that aims to improve controllability over tics that an individual finds distressing or impairing. However, it is only effective for approximately half of patients. Supplementary motor area (SMA)-directed neurocircuitry plays a strong role in motor inhibition, and activity in this region is thought to contribute to tic expression. Targeted modulation of SMA using transcranial magnetic stimulation (TMS) may increase CBIT efficacy by improving patient ability to implement tic controllability behaviors.

Methods

The CBIT+TMS trial is a two-phase, milestone driven early-stage randomized controlled trial. The trial will test whether augmenting CBIT with inhibitory, noninvasive stimulation of SMA with TMS modifies activity in SMA-mediated circuits and enhances tic controllability in youth ages 12-21 years with chronic tics. Phase 1 will directly compare two rTMS augmentation strategies (1Hz rTMS vs. cTBS) vs. sham in N = 60 participants. Quantifiable, a priori "Go/No Go Criteria" guide the decision to proceed to Phase 2 and selection of the optimal TMS regimen. Phase 2 will compare the optimal regimen vs. sham and test the link between neural target engagement and clinical outcomes in a new sample of N = 60 participants.

Discussion

This clinical trial is one of few to date testing TMS augmentation of therapy in a pediatric sample. Results will provide insight into whether TMS is a potentially viable strategy for enhancing CBIT efficacy and reveal potential neural and behavioral mechanisms of change.

Trial registration

ClinicalTrials.gov Identifier: NCT04578912.

Statistical Inferences for Complex Dependence of Multimodal Imaging Data

Statistical analysis of multimodal imaging data is a challenging task, since the data involves high-dimensionality, strong spatial correlations and complex data structures. In this paper, we propose rigorous statistical testing procedures for making inferences on the complex dependence of multimodal imaging data. Motivated by the analysis of multitask fMRI data in the Human Connectome Project (HCP) study, we particularly address three hypothesis testing problems: (a) testing independence among imaging modalities over brain regions, (b) testing independence between brain regions within imaging modalities, and (c) testing independence between brain regions across different modalities. Considering a general form for all the three tests, we develop a global testing procedure and a multiple testing procedure controlling the false discovery rate. We study theoretical properties of the proposed tests and develop a computationally efficient distributed algorithm. The proposed methods and theory are general and relevant for many statistical problems of testing independence structure among the components of high-dimensional random vectors with arbitrary dependence structures. We also illustrate our proposed methods via extensive simulations and analysis of five task fMRI contrast maps in the HCP study.

Topology of the lateral visual system: The fundus of the superior temporal sulcus and parietal area H connect nonvisual cerebrum to the lateral occipital lobe

BACKGROUND AND PURPOSE

Mapping the topology of the visual system is critical for understanding how complex cognitive processes like reading can occur. We aim to describe the connectivity of the visual system to understand how the cerebrum accesses visual information in the lateral occipital lobe.

METHODS

Using meta-analytic software focused on task-based functional MRI studies, an activation likelihood estimation (ALE) of the visual network was created. Regions of interest corresponding to the cortical parcellation scheme previously published under the Human Connectome Project were co-registered onto the ALE to identify the hub-like regions of the visual network. Diffusion Spectrum Imaging-based fiber tractography was performed to determine the structural connectivity of these regions with extraoccipital cortices.

RESULTS

The fundus of the superior temporal sulcus (FST) and parietal area H (PH) were identified as hub-like regions for the visual network. FST and PH demonstrated several areas of coactivation beyond the occipital lobe and visual network. Furthermore, these parcellations were highly interconnected with other cortical regions throughout extraoccipital cortices related to their nonvisual functional roles. A cortical model demonstrating connections to these hub-like areas was created.

CONCLUSIONS

FST and PH are two hub-like areas that demonstrate extensive functional coactivation and structural connections to nonvisual cerebrum. Their structural interconnectedness with language cortices along with the abnormal activation of areas commonly located in the temporo-occipital region in dyslexic individuals suggests possible important roles of FST and PH in the integration of information related to language and reading. Future studies should refine our model by examining the functional roles of these hub areas and their clinical significance.

Lower resting brain entropy is associated with stronger task activation and deactivation

Brain entropy (BEN) calculated from resting state fMRI has been the subject of increasing research interest in recent years. Previous studies have shown the correlations between rest BEN and neurocognition and task performance, but how this relates to task-evoked brain activations and deactivations remains unknown. The purpose of this study is to address this open question using large data (n = 862). Voxel wise correlations were calculated between rest BEN and task activations/deactivations of five different tasks. For most of the assessed tasks, lower rest BEN was found to be associated with stronger activations (negative correlations) and stronger deactivations (positive correlations) only in brain regions activated or deactivated by the tasks. Higher workload evoked spatially more extended negative correlations between rest BEN and task activations. These results not only confirm that resting brain activity can predict brain activity during task performance but also for the first time show that resting brain activity may facilitate both task activations and deactivations. In addition, the results provide a clue to understanding the individual differences of task performance and brain activations.

Younger and Late Middle-Aged Adults Exhibit Different Patterns of Cognitive-Motor Interference During Locomotor Adaptation, With No Disruption of Savings

It has been proposed that motor adaptation and subsequent savings (or faster relearning) of an adapted movement pattern are mediated by cognitive processes. Here, we evaluated the pattern of cognitive-motor interference that emerges when young and late middle-aged adults perform an executive working memory task during locomotor adaptation. We also asked if this interferes with savings of a newly learned walking pattern, as has been suggested by a study of reaching adaptation. We studied split-belt treadmill adaptation and savings in young (21 ± 2 y/o) and late middle-aged (56 ± 6 y/o) adults with or without a secondary 2-back task during adaptation. We found that young adults showed similar performance on the 2-back task during baseline and adaptation, suggesting no effect of the dual-task on cognitive performance; however, dual-tasking interfered with adaptation over the first few steps. Conversely, dual-tasking caused a decrement in cognitive performance in late middle-aged adults with no effect on adaptation. To determine if this effect was specific to adaptation, we also evaluated dual-task interference in late middle-aged adults that dual-tasked while walking in a complex environment that did not induce motor adaptation. This group exhibited less cognitive-motor interference than late middle-aged adults who dual-tasked during adaptation. Savings was unaffected by dual-tasking in both young and late middle-aged adults, which may indicate different underlying mechanisms for savings of reaching and walking. Collectively, our findings reveal an age-dependent effect of cognitive-motor interference during dual-task locomotor adaptation and no effect of dual-tasking on savings, regardless of age. Young adults maintain cognitive performance and show a mild decrement in locomotor adaptation, while late middle-aged adults adapt locomotion at the expense of cognitive performance.

Complexity Analysis of Resting-State and Task fMRI Using Multiscale Sample Entropy

Functional magnetic resonance imaging (fMRI) is a powerful tool that allows for analysis of neural activity via the measurement of blood-oxygenation-level-dependent (BOLD) signal. The BOLD fluctuations can exhibit different levels of complexity, depending upon the conditions under which they are measured. We examined the complexity of both resting-state and task-based fMRI using sample entropy (SampEn) as a surrogate for signal predictability. We found that within most tasks, regions of the brain that were deemed task-relevant displayed significantly low levels of SampEn, and there was a strong negative correlation between parcel entropy and amplitude.

Pupillometry tracks cognitive load and salience network activity in a working memory functional magnetic resonance imaging task

The diameter of the human pupil tracks working memory processing and is associated with activity in the frontoparietal network. At the same time, recent neuroimaging research has linked human pupil fluctuations to activity in the salience network. In this combined functional magnetic resonance imaging (fMRI)/pupillometry study, we recorded the pupil size of healthy human participants while they performed a blockwise organized working memory task (N‐back) inside an MRI scanner in order to monitor the pupil fluctuations associated neural activity during working memory processing. We first confirmed that mean pupil size closely followed working memory load. Combining this with fMRI data, we focused on blood oxygen level dependent (BOLD) correlates of mean pupil size modeled onto the task blocks as a parametric modulation. Interrogating this modulated task regressor, we were able to retrieve the frontoparietal network. Next, to fully exploit the within‐block dynamics, we divided the blocks into 1 s time bins and filled these with corresponding pupil change values (first‐order derivative of pupil size). We found that pupil change within N‐back blocks was positively correlated with BOLD amplitudes in the areas of the salience network (namely bilateral insula, and anterior cingulate cortex). Taken together, fMRI with simultaneous measurement of pupil parameters constitutes a valuable tool to dissect working memory subprocesses related to both working memory load and salience of the presented stimuli.

Long-Known Music Exposure Effects on Brain Imaging and Cognition in Early-Stage Cognitive Decline: A Pilot Study

BACKGROUND

Repeated exposure to long-known music has been shown to have a beneficial effect on cognitive performance in patients with AD. However, the brain mechanisms underlying improvement in cognitive performance are not yet clear.

OBJECTIVE

In this pilot study we propose to examine the effect of repeated long-known music exposure on imaging indices and corresponding changes in cognitive function in patients with early-stage cognitive decline.

METHODS

Participants with early-stage cognitive decline were assigned to three weeks of daily long-known music listening, lasting one hour in duration. A cognitive battery was administered, and brain activity was measured before and after intervention. Paired-measures tests evaluated the longitudinal changes in brain structure, function, and cognition associated with the intervention.

RESULTS

Fourteen participants completed the music-based intervention, including 6 musicians and 8 non-musicians. Post-baseline there was a reduction in brain activity in key nodes of a music-related network, including the bilateral basal ganglia and right inferior frontal gyrus, and declines in fronto-temporal functional connectivity and radial diffusivity of dorsal white matter. Musician status also significantly modified longitudinal changes in functional and structural brain measures. There was also a significant improvement in the memory subdomain of the Montreal Cognitive Assessment.

CONCLUSION

These preliminary results suggest that neuroplastic mechanisms may mediate improvements in cognitive functioning associated with exposure to long-known music listening and that these mechanisms may be different in musicians compared to non-musicians.

Spontaneous and deliberate modes of creativity: Multitask eigen-connectivity analysis captures latent cognitive modes during creative thinking

Despite substantial progress in the quest of demystifying the brain basis of creativity, several questions remain open. One such issue concerns the relationship between two latent cognitive modes during creative thinking, i.e., deliberate goal-directed cognition and spontaneous thought generation. Although an interplay between deliberate and spontaneous thinking is often implicated in the creativity literature (e.g., dual-process models), a bottom-up data-driven validation of the cognitive processes associated with creative thinking is still lacking. Here, we attempted to capture the latent modes of creative thinking by utilizing a data-driven approach on a novel continuous multitask paradigm (CMP) that widely sampled a hypothetical two-dimensional cognitive plane of deliberate and spontaneous thinking in a single fMRI session. The CMP consisted of eight task blocks ranging from undirected mind wandering to goal-directed working memory task, while also included two widely-used creativity tasks, i.e., alternate uses task (AUT) and remote association task (RAT). Using eigen-connectivity (EC) analysis on the multitask whole-brain functional connectivity (FC) patterns, we embedded the multitask FCs into a low-dimensional latent space. The first two latent components, as revealed by the EC analysis, broadly mapped onto the two cognitive modes of deliberate and spontaneous thinking, respectively. Further, in this low-dimensional space, both creativity tasks were located in the upper right corner of high deliberate and spontaneous thinking (creative cognitive space). Neuroanatomically, the creative cognitive space was represented by not only increased intra-network connectivity within executive control and default mode network, but also by higher coupling between the two canonical brain networks. Further, individual differences reflected in the low-dimensional connectivity embeddings were related to differences in deliberate and spontaneous thinking abilities. Altogether, using a continuous multitask paradigm and a data-driven approach, we provide initial empirical evidence for the contribution of both deliberate and spontaneous modes of cognition during creative thinking.

Evolutional Neural Architecture Search for Optimization of Spatiotemporal Brain Network Decomposition

Using deep neural networks (DNNs) to explore spatial patterns and temporal dynamics of human brain activities has been an important yet challenging problem because the artificial neural networks are hard to be designed manually. There have been several promising deep learning methods, e.g., deep belief network (DBN), convolutional neural network (CNN), and deep sparse recurrent auto-encoder (DSRAE), that can decompose neuroscientific and meaningful spatiotemporal patterns from 4D functional Magnetic Resonance Imaging (fMRI) data. However, those previous studies still depend on hand-crafted neural network architectures and hyperparameters, which are not optimal in various senses. In this paper, we employ the evolutionary algorithms (EA) to optimize the deep neural architecture of DSRAE by minimizing the expected loss of initialized models, named eNAS-DSRAE (evolutionary Neural Architecture Search on Deep Sparse Recurrent Auto-Encoder). Also, validation experiments are designed and performed on the publicly available human connectome project (HCP) 900 datasets, and the results achieved by the optimized eNAS-DSRAE suggested that our framework can successfully identify the spatiotemporal features and perform better than the hand-crafted neural network models. To our best knowledge, the proposed eNAS-DSRAE is not only among the earliest NAS models that can extract connectome-scale meaningful spatiotemporal brain networks from 4D fMRI data, but also is an effective framework to optimize the RNN-based models.

Reduced Segregation Between Cognitive and Emotional Processes in Cannabis Dependence

Addiction is characterized by an erosion of cognitive control toward drug taking that is accentuated by negative emotional states. Here we tested the hypothesis that enhanced interference on cognitive control reflects a loss of segregation between cognition and emotion in addiction. We analyzed Human Connectome Project data from 1206 young adults, including 89 with cannabis dependence (CD). Two composite factors, one for cognition and one for emotion, were derived using principal component (PC) analyses. Component scores for these PCs were significantly associated in the CD group, such that negative emotionality correlated with poor cognition. However, the corresponding component scores were uncorrelated in matched controls and nondependent recreational cannabis users (n = 87). In CD, but not controls or recreational users, functional magnetic resonance imaging activations to emotional stimuli (angry/fearful faces > shapes) correlated with activations to cognitive demand (working memory; 2-back > 0-back). Canonical correlation analyses linked individual differences in cognitive and emotional component scores with brain activations. In CD, there was substantial overlap between cognitive and emotional brain-behavior associations, but in controls, associations were more restricted to the cognitive domain. These findings support our hypothesis of impaired segregation between cognitive and emotional processes in CD that might contribute to poor cognitive control under conditions of increased emotional demand.

The neurocognitive correlates of brain entropy estimated by resting state fMRI

Resting state brain activity consumes most of brain energy, likely creating and maintaining a reserve of general brain functionality. The latent reserve if it exists may be reflected by the profound long-range fluctuations of resting brain activity. The long-range temporal coherence (LRTC) can be characterized by resting state fMRI (rsfMRI)-based brain entropy (BEN) mapping. While BEN mapping results have shown sensitivity to neuromodulations or disease conditions, the underlying neuromechanisms especially the associations of BEN or LRTC to neurocognition still remain unclear. To address this standing question and to test a novel hypothesis that resting BEN reflects a latent functional reserve through the link to general functionality, we mapped resting BEN of 862 young adults and comprehensively examined its associations to neurocognitions using data from the Human Connectome Project (HCP). Our results unanimously highlighted two brain circuits: the default mode network (DMN) and executive control network (ECN) through their negative associations of BEN to general functionality, which is independent of age and sex. While BEN in DMN/ECN increases with age, it decreases with education years. These results demonstrated the neurocognitive correlates of resting BEN in DMN/ECN and suggest resting BEN in DMN/ECN as a potential proxy of the latent functional reserve that facilitates general brain functionality and may be enhanced by education.

MEG current source reconstruction using a meta-analysis fMRI prior

Magnetoencephalography (MEG) offers a unique way to noninvasively investigate millisecond-order cortical activities by mapping sensor signals (magnetic fields outside the head) to cortical current sources using current source reconstruction methods. Current source reconstruction is defined as an ill-posed inverse problem, since the number of sensors is less than the number of current sources. One powerful approach to solving this problem is to use functional MRI (fMRI) data as a spatial constraint, although it boosts the cost of measurement and the burden on subjects. Here, we show how to use the meta-analysis fMRI data synthesized from thousands of papers instead of the individually recorded fMRI data. To mitigate the differences between the meta-analysis and individual data, the former are imported as prior information of the hierarchical Bayesian estimation. Using realistic simulations, we found out the performance of current source reconstruction using meta-analysis fMRI data to be better than that using low-quality individual fMRI data and conventional methods. By applying experimental data of a face recognition task, we qualitatively confirmed that group analysis results using the meta-analysis fMRI data showed a tendency similar to the results using the individual fMRI data. Our results indicate that the use of meta-analysis fMRI data improves current source reconstruction without additional measurement costs. We assume the proposed method would have greater effect for modalities with lower measurement costs, such as optically pumped magnetometers.

Simultaneous spatial-temporal decomposition for connectome-scale brain networks by deep sparse recurrent auto-encoder

Exploring the spatial patterns and temporal dynamics of human brain activity has been of great interest, in the quest to better understand connectome-scale brain networks. Though modeling spatial and temporal patterns of functional brain networks have been researched for a long time, the development of a unified and simultaneous spatial-temporal model has yet to be realized. For instance, although some deep learning methods have been proposed recently in order to model functional brain networks, most of them can only represent either spatial or temporal perspective of functional Magnetic Resonance Imaging (fMRI) data and rarely model both domains simultaneously. Due to the recent success in applying sequential auto-encoders for brain decoding, in this paper, we propose a deep sparse recurrent auto-encoder (DSRAE) to be applied unsupervised to learn spatial patterns and temporal fluctuations of brain networks at the same time. The proposed DSRAE was evaluated and validated based on three tasks of the publicly available Human Connectome Project (HCP) fMRI dataset, resulting with promising evidence. To the best of our knowledge, the proposed DSRAE is among the early efforts in developing unified models that can extract connectome-scale spatial-temporal networks from 4D fMRI data simultaneously.

Dump the "dimorphism": Comprehensive synthesis of human brain studies reveals few male-female differences beyond size

With the explosion of neuroimaging, differences between male and female brains have been exhaustively analyzed. Here we synthesize three decades of human MRI and postmortem data, emphasizing meta-analyses and other large studies, which collectively reveal few reliable sex/gender differences and a history of unreplicated claims. Males' brains are larger than females' from birth, stabilizing around 11 % in adults. This size difference accounts for other reproducible findings: higher white/gray matter ratio, intra- versus interhemispheric connectivity, and regional cortical and subcortical volumes in males. But when structural and lateralization differences are present independent of size, sex/gender explains only about 1% of total variance. Connectome differences and multivariate sex/gender prediction are largely based on brain size, and perform poorly across diverse populations. Task-based fMRI has especially failed to find reproducible activation differences between men and women in verbal, spatial or emotion processing due to high rates of false discovery. Overall, male/female brain differences appear trivial and population-specific. The human brain is not "sexually dimorphic."

Differentiable neural architecture search for optimal spatial/temporal brain function network decomposition

It has been a key topic to decompose the brain's spatial/temporal function networks from 4D functional magnetic resonance imaging (fMRI) data. With the advantages of robust and meaningful brain pattern extraction, deep neural networks have been shown to be more powerful and flexible in fMRI data modeling than other traditional methods. However, the challenge of designing neural network architecture for high-dimensional and complex fMRI data has also been realized recently. In this paper, we propose a new spatial/temporal differentiable neural architecture search algorithm (ST-DARTS) for optimal brain network decomposition. The core idea of ST-DARTS is to optimize the inner cell structure of the vanilla recurrent neural network (RNN) in order to effectively decompose spatial/temporal brain function networks from fMRI data. Based on the evaluations on all seven fMRI tasks in human connectome project (HCP) dataset, the ST-DARTS model is shown to perform promisingly, both spatially (i.e., it can recognize the most stimuli-correlated spatial brain network activation that is very similar to the benchmark) and temporally (i.e., its temporal activity is highly positively correlated with the task-design). To further improve the efficiency of ST-DARTS model, we introduce a flexible early-stopping mechanism, named as ST-DARTS+, which further improves experimental results significantly. To our best knowledge, the proposed ST-DARTS and ST-DARTS+ models are among the early efforts in optimally decomposing spatial/temporal brain function networks from fMRI data with neural architecture search strategy and they demonstrate great promise.

Genetic and Neuroimaging Approaches to Understanding Post-Traumatic Stress Disorder

Post-traumatic stress disorder (PTSD) is a highly disabling condition, increasingly recognized as both a disorder of mental health and social burden, but also as an anxiety disorder characterized by fear, stress, and negative alterations in mood. PTSD is associated with structural, metabolic, and molecular changes in several brain regions and the neural circuitry. Brain areas implicated in the traumatic stress response include the amygdala, hippocampus, and prefrontal cortex, which play an essential role in memory function. Abnormalities in these brain areas are hypothesized to underlie symptoms of PTSD and other stress-related psychiatric disorders. Conventional methods of studying PTSD have proven to be insufficient for diagnosis, measurement of treatment efficacy, and monitoring disease progression, and currently, there is no diagnostic biomarker available for PTSD. A deep understanding of cutting-edge neuroimaging genetic approaches is necessary for the development of novel therapeutics and biomarkers to better diagnose and treat the disorder. A current goal is to understand the gene pathways that are associated with PTSD, and how those genes act on the fear/stress circuitry to mediate risk vs. resilience for PTSD. This review article explains the rationale and practical utility of neuroimaging genetics in PTSD and how the resulting information can aid the diagnosis and clinical management of patients with PTSD.

Comparison of feature selection methods based on discrimination and reliability for fMRI decoding analysis

BACKGROUND

Feature selection is a crucial step in the machine learning methods that are currently used to assist with decoding brain states from fMRI data. This step can be based on either feature discrimination or feature reliability, but there is no clear evidence indicating which method is more suitable for fMRI data.

METHODS

We used ANOVA and Kendall's concordance coefficient as proxies for the two kinds of feature selection criteria. The performances of both methods were compared using different subject and feature numbers. The study included 987 subjects from the Human Connectome Project (HCP).

RESULTS

Classification performance suggested that features based on discrimination were more capable of distinguishing between various brain states for any number of subjects or extracted features. In addition, reliability-based features were always more stable than other features, and these properties (discernment and stability) of features, to some degree, related to the number of subjects and features. Furthermore, when the number of extracted features increased, the feature distributions also gradually extended from occipital lobe to more association regions of the brain.

CONCLUSION

The results from this study provide empirical guides for feature selection for the prediction of individual brain states.

Genetic influence is linked to cortical morphology in category-selective areas of visual cortex

Human visual cortex contains discrete areas that respond selectively to specific object categories such as faces, bodies, and places. A long-standing question is whether these areas are shaped by genetic or environmental factors. To address this question, here we analyzed functional MRI data from an unprecedented number (n = 424) of monozygotic (MZ) and dizygotic (DZ) twins. Category-selective maps were more identical in MZ than DZ twins. Within each category-selective area, distinct subregions showed significant genetic influence. Structural MRI analysis revealed that the ‘genetic voxels’ were predominantly located in regions with higher cortical curvature (gyral crowns in face areas and sulcal fundi in place areas). Moreover, we found that cortex was thicker and more myelinated in genetic voxels of face areas, while it was thinner and less myelinated in genetic voxels of place areas. This double dissociation suggests a differential development of face and place areas in cerebral cortex.

It remains unclear whether the functional organization of the visual cortex is shaped by genetic or environmental factors. Using fMRI in twins (n = 424), these authors show that activation patterns in category-selective areas are heritable, and that the genetic effects in these areas are linked to structural properties of cortical tissue.

Identifying Brain Networks at Multiple Time Scales via Deep Recurrent Neural Network

For decades, task functional magnetic resonance imaging has been a powerful noninvasive tool to explore the organizational architecture of human brain function. Researchers have developed a variety of brain network analysis methods for task fMRI data, including the general linear model, independent component analysis, and sparse representation methods. However, these shallow models are limited in faithful reconstruction and modeling of the hierarchical and temporal structures of brain networks, as demonstrated in more and more studies. Recently, recurrent neural networks (RNNs) exhibit great ability of modeling hierarchical and temporal dependence features in the machine learning field, which might be suitable for task fMRI data modeling. To explore such possible advantages of RNNs for task fMRI data, we propose a novel framework of a deep recurrent neural network (DRNN) to model the functional brain networks from task fMRI data. Experimental results on the motor task fMRI data of Human Connectome Project 900 subjects release demonstrated that the proposed DRNN can not only faithfully reconstruct functional brain networks, but also identify more meaningful brain networks with multiple time scales which are overlooked by traditional shallow models. In general, this work provides an effective and powerful approach to identifying functional brain networks at multiple time scales from task fMRI data.

Task-evoked Negative BOLD Response and Functional Connectivity in the Default Mode Network are Representative of Two Overlapping but Separate Neurophysiological Processes

The topography of the default mode network (DMN) can be obtained with one of two different functional magnetic resonance imaging (fMRI) methods: either from the spontaneous but organized synchrony of the low-frequency fluctuations in resting-state fMRI (rs-fMRI), known as "functional connectivity", or from the consistent and robust deactivations in task-based fMRI (tb-fMRI), here referred to as the "negative BOLD response" (NBR). These two methods are fundamentally different, but their results are often used interchangeably to describe the brain's resting-state, baseline, or intrinsic activity. While the DMN was initially defined by consistent task-based decreases in blood flow in a set of specific brain regions using PET imaging, recently nearly all studies on the DMN employ functional connectivity in rs-fMRI. In this study, we first show the high level of spatial overlap between NBR and functional connectivity of the DMN extracted from the same tb-fMRI scan; then, we demonstrate that the NBR in putative DMN regions can be significantly altered without causing any change in their overlapping functional connectivity. Furthermore, we present evidence that in the DMN, the NBR is more closely related to task performance than the functional connectivity. We conclude that the NBR and functional connectivity of the DMN reflect two separate but overlapping neurophysiological processes, and thus should be differentiated in studies investigating brain-behavior relationships in both healthy and diseased populations. Our findings further raise the possibility that the macro-scale networks of the human brain might internally exhibit a hierarchical functional architecture.

Resting-State Functional Magnetic Resonance Imaging and Probabilistic Diffusion Tensor Imaging Demonstrate That the Greatest Functional and Structural Connectivity in the Hand Motor Homunculus Occurs in the Area of the Thumb

The primary hand motor region is classically believed to be in the "hand knob" area in the precentral gyrus (PCG). However, hand motor task-based activation is often localized outside this area. The purpose of this study is to investigate the structural and functional connectivity driven by different seed locations corresponding to the little, index, and thumb in the PCG using probabilistic diffusion tractography (PDT) and resting-state functional magnetic resonance imaging (rfMRI). Twelve healthy subjects had three regions of interest (ROIs) placed in the left PCG: lateral to the hand knob (thumb area), within the hand knob (index finger area), and medial to the hand knob (little finger area). Connectivity maps were generated using PDT and rfMRI. Individual and group level analyses were performed. Results show that the greatest hand motor connectivity between both hemispheres was obtained using the ROI positioned just lateral to the hand knob in the PCG (the thumb area). The number of connected voxels in the PCG between the two hemispheres was greatest in the lateral-most ROI (the thumb area): 279 compared with 13 for the medial-most ROI and 9 for the central hand knob ROI. Similarly, the highest white matter connectivity between the two hemispheres resulted from the ROI placed in the lateral portion of PCG (p < 0.003). The maximal functional and structural connectivity of the hand motor area between hemispheres occurs in the thumb area, located laterally at the "hand knob." Thus, this location appears maximal for rfMRI and PDT seeding of the motor area.

Assessing motor, visual and language function using a single 5-minute fMRI paradigm: three birds with one stone

Clinical functional Magnetic Resonance Imaging (fMRI) requires inferences on localization of major brain functions at the individual subject level. We hypothesized that a single "triple use" task would satisfy sensitivity and reliability requirements for successfully assessing the motor, visual and language domain in this context. This was tested here by the application in a group of healthy adults, assessing sensitivity and reliability at the individual subject level, separately for each domain.Our "triple use" task consisted of 2 conditions (condition 1, assessing motor and visual domain, and condition 2, assessing the language domain), serving mutually as active/control. We included 20 healthy adult subjects. Random effect analyses showed activation in primary motor, visual and language regions, as expected. Less expected regions were activated both for the motor and visual domains. Further, reliability of primary activation patterns was very high across individual subjects, with activation seen in 70-100% of subjects in primary motor, visual, and left-lateralized language regions.These findings suggest the "triple use" task to be reliable at the individual subject's level to assess motor, visual and language domains in the clinical fMRI context. Benefits of such an approach include shortening of acquisition time, simplicity of the task for each domain, and using a visual stimulus. Following establishment of reliability in adults, the task may also be a valuable addition in the pediatric clinical fMRI context, where each of these factors is of high relevance.

Recognizing Brain States Using Deep Sparse Recurrent Neural Network

Brain activity is a dynamic combination of different sensory responses and thus brain activity/state is continuously changing over time. However, the brain's dynamical functional states recognition at fast time-scales in task fMRI data have been rarely explored. In this paper, we propose a novel 5-layer deep sparse recurrent neural network (DSRNN) model to accurately recognize the brain states across the whole scan session. Specifically, the DSRNN model includes an input layer, one fully-connected layer, two recurrent layers, and a softmax output layer. The proposed framework has been tested on seven task fMRI data sets of Human Connectome Project. Extensive experiment results demonstrate that the proposed DSRNN model can accurately identify the brain's state in different task fMRI data sets and significantly outperforms other auto-correlation methods or non-temporal approaches in the dynamic brain state recognition accuracy. In general, the proposed DSRNN offers a new methodology for basic neuroscience and clinical research.

Activation-based association profiles differentiate network roles across cognitive loads

Working memory (WM) is a complex and pivotal cognitive system underlying the performance of many cognitive behaviors. Although individual differences in WM performance have previously been linked to the blood oxygenation level-dependent (BOLD) response across several large-scale brain networks, the unique and shared contributions of each large-scale brain network to efficient WM processes across different cognitive loads remain elusive. Using a WM paradigm and functional magnetic resonance imaging (fMRI) from the Human Connectome Project, we proposed a framework to assess the association and shared-association strength between imaging biomarkers and behavioral scales. Association strength is the capability of individual brain regions to modulate WM performance and shared-association strength measures how different regions share the capability of modulating performance. Under higher cognitive load (2-back), the frontoparietal executive control network (FPN), dorsal attention network (DAN), and salience network showed significant positive activation and positive associations, whereas the default mode network (DMN) showed the opposite pattern, namely, significant deactivation and negative associations. Comparing the different cognitive loads, the DMN and FPN showed predominant associations and globally shared-associations. When investigating the differences in association from lower to higher cognitive loads, the DAN demonstrated enhanced association strength and globally shared-associations, which were significantly greater than those of the other networks. This study characterized how brain regions individually and collaboratively support different cognitive loads.

Trait and state anxiety is marked by increased working memory-related parietal BOLD signal

Anxiety is associated with compromised cognitive control functions, such as working memory. State and trait anxiety within the non-clinical population can be utilised to investigate potential neural markers for anxiety, which may help to elucidate potential prevention and intervention methods. Thirty-two healthy adults (20 female, 12 male), aged between 30 and 65 years, performed a 2-back task whilst fMRI BOLD signal was acquired using a 3T scanner. Mean BOLD signal was obtained in cognitive control network regions of interest of: left and right dorsolateral prefrontal cortex (DLPFC) and posterior parietal lobe (PPL), and medial prefrontal cortex (MPFC). State and trait anxiety levels were recorded. Higher overall anxiety was moderately associated with more left and right PPL BOLD signal; there was a weak relationship between anxiety and left DLPFC BOLD signal. MPFC BOLD signal and trait anxiety were moderately associated with overall 2-back task performance. These findings suggest that non-clinical anxiety affects the recruitment of cortical resources during working memory, but that anxiety does not impair performance during a 2-back task.

Impaired sensory processing measured by functional MRI in Bipolar disorder manic and depressed mood states

Bipolar disorder is characterized by recurring episodes of depression and mania. Defining differences in brain function during these states is an important goal of bipolar disorder research. However, few imaging studies have directly compared brain activity between bipolar mood states. Herein, we compare functional magnetic resonance imaging (fMRI) responses during a flashing checkerboard stimulus between bipolar participants across mood states (euthymia, depression, and mania) in order to identify functional differences between these states. 40 participants with bipolar I disorder and 33 healthy controls underwent fMRI during the presentation of the stimulus. A total of 23 euthymic-state, 16 manic-state, 15 depressed-state, and 32 healthy control imaging sessions were analyzed in order to compare functional activation during the stimulus between mood states and with healthy controls. A reduced response was identified in the visual cortex in both the depressed and manic groups compared to euthymic and healthy participants. Functional differences between bipolar mood states were also observed in the cerebellum, thalamus, striatum, and hippocampus. Functional differences between mood states occurred in several brain regions involved in visual and other sensory processing. These differences suggest that altered visual processing may be a feature of mood states in bipolar disorder. The key limitations of this study are modest mood-state group size and the limited temporal resolution of fMRI which prevents the segregation of primary visual activity from regulatory feedback mechanisms.

Thresholding functional connectomes by means of mixture modeling

Functional connectivity has been shown to be a very promising tool for studying the large-scale functional architecture of the human brain. In network research in fMRI, functional connectivity is considered as a set of pair-wise interactions between the nodes of the network. These interactions are typically operationalized through the full or partial correlation between all pairs of regional time series. Estimating the structure of the latent underlying functional connectome from the set of pair-wise partial correlations remains an open research problem though. Typically, this thresholding problem is approached by proportional thresholding, or by means of parametric or non-parametric permutation testing across a cohort of subjects at each possible connection. As an alternative, we propose a data-driven thresholding approach for network matrices on the basis of mixture modeling. This approach allows for creating subject-specific sparse connectomes by modeling the full set of partial correlations as a mixture of low correlation values associated with weak or unreliable edges in the connectome and a sparse set of reliable connections. Consequently, we propose to use alternative thresholding strategy based on the model fit using pseudo-False Discovery Rates derived on the basis of the empirical null estimated as part of the mixture distribution. We evaluate the method on synthetic benchmark fMRI datasets where the underlying network structure is known, and demonstrate that it gives improved performance with respect to the alternative methods for thresholding connectomes, given the canonical thresholding levels. We also demonstrate that mixture modeling gives highly reproducible results when applied to the functional connectomes of the visual system derived from the n-back Working Memory task in the Human Connectome Project. The sparse connectomes obtained from mixture modeling are further discussed in the light of the previous knowledge of the functional architecture of the visual system in humans. We also demonstrate that with use of our method, we are able to extract similar information on the group level as can be achieved with permutation testing even though these two methods are not equivalent. We demonstrate that with both of these methods, we obtain functional decoupling between the two hemispheres in the higher order areas of the visual cortex during visual stimulation as compared to the resting state, which is in line with previous studies suggesting lateralization in the visual processing. However, as opposed to permutation testing, our approach does not require inference at the cohort level and can be used for creating sparse connectomes at the level of a single subject.

The Adolescent Brain Cognitive Development (ABCD) study: Imaging acquisition across 21 sites

The ABCD study is recruiting and following the brain development and health of over 10,000 9–10 year olds through adolescence. The imaging component of the study was developed by the ABCD Data Analysis and Informatics Center (DAIC) and the ABCD Imaging Acquisition Workgroup. Imaging methods and assessments were selected, optimized and harmonized across all 21 sites to measure brain structure and function relevant to adolescent development and addiction. This article provides an overview of the imaging procedures of the ABCD study, the basis for their selection and preliminary quality assurance and results that provide evidence for the feasibility and age-appropriateness of procedures and generalizability of findings to the existent literature.

Rested-Baseline Responsivity of the Ventral Striatum Is Associated With Caloric and Macronutrient Intake During One Night of Sleep Deprivation

Background: Sleep loss contributes to obesity through a variety of mechanisms, including neuroendocrine functioning, increased hunger, and increased food intake. Additionally, sleep loss alters functional activation within brain regions associated with reward and behavioral control. However, it remains unknown whether individual differences in baseline neural functioning can predict eating behaviors during total sleep deprivation (TSD). We used functional magnetic resonance imaging (fMRI) to test the hypothesis that individuals with increased baseline responsiveness within reward regions are more vulnerable to TSD-induced overeating. Methods: N = 45 subjects completed several fMRI scans during a single pre-TSD session that included performance on the Multi-Source Interference Task (MSIT) and the n-back task. Subjects returned to the laboratory for an overnight TSD session, during which they were given ad libitum access to 10,900 kcal of food. Leftover food and packaging were collected every 6 h (00:00, 06:00, and 12:00) to measure total food consumption. Subjects reported sleepiness every hour and performed a food rating task every 3 h. Results: Functional activation within the ventral striatum during the MSIT and n-back positively correlated with total caloric and carbohydrate intake during the final 6 h (06:00-12:00) of TSD. Activation within the middle and superior temporal gyri during the MSIT also correlated with total carbohydrates consumed. Food consumption did not correlate with subjective sleepiness, hunger, or food desire. Conclusions: Individual differences in neural activity of reward processing areas (i.e., nucleus accumbens) prior to sleep deprivation are associated with an individual's propensity to overeat during subsequent sleep deprivation. This suggests that individual differences within reward processing pathways are potential key factors in sleep loss related overeating. Sleep loss and obesity are tightly linked. Both phenomena have been associated with increased neural activation in regions associated with reward, inhibitory control, and disrupted dopamine signaling. Elevated baseline reward sensitivity in the ventral striatum appears to be further compounded by sleep deprivation induced dysfunction in the reward neurocircuitry, increasing the likelihood of overeating. Our findings suggest that large individual differences in baseline responsiveness of hedonic reward pathways may modulate the association between sleep loss and obesity.

Physiological substrates of executive functioning: a systematic review of the literature

Executive function (EF) is a multifaceted construct that has been defined as a set of higher-order cognitive processes that allow for flexibility, self-regulation, strategic planning, and goal-directed behaviors. EFs have been studied in numerous clinical disorders using a variety of neuropsychological tasks and, more recently, neuroimaging techniques. The underlying physiological substrates of EF were historically attributed to the frontal lobes; however, recent studies suggest more widespread involvement of additional brain regions. The purpose of the present study was to conduct a systematic review (using PRISMA 2009 guidelines) of neuroimaging studies employing functional magnetic resonance imaging and diffusion tensor imaging methods investigating the physiological substrates of EFs in attention-deficit/hyperactivity disorder compared to other clinical groups and non-clinical participants. Research articles were retrieved using PsycINFO, PsycARTICLES, MEDLINE, and ScienceDirect, beginning February 2015 through May 2016. A total of 42 studies met eligibility. Of those 42 studies, 22 studies included clinical participants and 20 studies included non-clinical participants. Results revealed increased activation of the frontal brain region in the majority of non-clinical studies and approximately 50% of the clinical studies, albeit with some inconsistencies across subregions, tasks, and age groups. Implications, methodological limitations, and suggestions for future research are discussed.

Glutamine and Glutamate Complex, as Measured by Functional Magnetic Resonance Spectroscopy, Alters During Face-Name Association Task in Patients with Mild Cognitive Impairment and Alzheimer's Disease

BACKGROUND

The metabolite response during a memory task in Alzheimer's disease (AD) patients is unknown.

OBJECTIVE

To investigate the metabolite changes in subjects with AD, amnestic mild cognitive impairment (aMCI), and cognitively normal (CN) elderly during a memory task using functional magnetic resonance spectroscopy (fMRS).

METHODS

This study involved 23 young normal controls (YC), 24 CN elderly, 24 aMCI, and 24 mild and probable AD individuals. fMRS data were acquired at the precuneus and posterior cingulate brain regions during a face-name association task. Statistical analyses of quantified metabolites were performed to evaluate differences of the metabolite values between the stimulation conditions and among the four subject groups. Receiver operating curve analysis was performed to evaluate whether the metabolic changes after functional activations can differentiate the subject groups.

RESULT

Glutamine and glutamate complex (Glx) was statistically significantly different between the fixation and repeat conditions in aMCI (p = 0.0492) as well as between the fixation and the novel conditions in the AD (p = 0.0412) group. The total N-acetylaspartate (tNAA) was statistically significantly different among the four subject groups in the fixation condition (DF = 3, F = 7.673, p <  0.001), the novel condition (DF = 3, F = 6.945, p <  0.001), and the repeat condition (DF = 3, F = 7.127, p <  0.001). tNAA, tCr, and mIns could be used to differentiate CN from aMCI. Furthermore, tNAA, tCr, Glx, and Glu could also differentiate CN from AD, and aMCI from AD.

CONCLUSION

Glx was altered during a stimulation that may be used to evaluate neuronal dysfunction in a demented patient. tNAA and tCr were reduced in patients with AD.

Gender Differences in Verbal and Visuospatial Working Memory Performance and Networks

BACKGROUND

Working memory (WM) has been a matter of intensive basic and clinical research for some decades now. The investigation of WM function and dysfunction may facilitate the understanding of both physiological and pathological processes in the human brain. Though WM paradigms are widely used in neuroscientific and psychiatric research, conclusive knowledge about potential moderating variables such as gender is still missing.

METHODS

We used functional magnetic resonance imaging to investigate the effects of gender on verbal and visuospatial WM maintenance tasks in a large and homogeneous sample of young healthy subjects.

RESULTS

We found significant gender effects on both the behavioral and neurofunctional level. Females exhibited disadvantages with a small effect size in both WM domains accompanied by stronger activations in a set of brain regions (including bilateral substantia nigra/ventral tegmental area and right Broca's area) independent of WM modality. As load and task difficulty effects have been shown for some of these regions, the stronger activations may reflect a slightly lower capacity of both WM domains in females. Males showed stronger bilateral intraparietal activations next to the precuneus which were specific for the visuospatial WM task. Activity in this specific region may be associated with visuospatial short-term memory capacity.

CONCLUSION

These findings provide evidence for a slightly lower capacity in both WM modalities in females.