fMRIPrep: a robust preprocessing pipeline for functional MRI

Improving practices and inferences in developmental cognitive neuroscience

The past decade has seen growing concern about research practices in cognitive neuroscience, and psychology more broadly, that shake our confidence in many inferences in these fields. We consider how these issues affect developmental cognitive neuroscience, with the goal of progressing our field to support strong and defensible inferences from our neurobiological data. This manuscript focuses on the importance of distinguishing between confirmatory versus exploratory data analysis approaches in developmental cognitive neuroscience. Regarding confirmatory research, we discuss problems with analytic flexibility, appropriately instantiating hypotheses, and controlling the error rate given how we threshold data and correct for multiple comparisons. To counterbalance these concerns with confirmatory analyses, we present two complementary strategies. First, we discuss the advantages of working within an exploratory analysis framework, including estimating and reporting effect sizes, using parcellations, and conducting specification curve analyses. Second, we summarize defensible approaches for null hypothesis significance testing in confirmatory analyses, focusing on transparent and reproducible practices in our field. Specific recommendations are given, and templates, scripts, or other resources are hyperlinked, whenever possible.

Topographic organization of the human subcortex unveiled with functional connectivity gradients

Brain atlases are fundamental to understanding the topographic organization of the human brain, yet many contemporary human atlases cover only the cerebral cortex, leaving the subcortex a terra incognita. We use functional MRI (fMRI) to map the complex topographic organization of the human subcortex, revealing large-scale connectivity gradients and new areal boundaries. We unveil four scales of subcortical organization that recapitulate well-known anatomical nuclei at the coarsest scale and delineate 27 new bilateral regions at the finest. Ultrahigh field strength fMRI corroborates and extends this organizational structure, enabling the delineation of finer subdivisions of the hippocampus and the amygdala, while task-evoked fMRI reveals a subtle subcortical reorganization in response to changing cognitive demands. A new subcortical atlas is delineated, personalized to represent individual differences and used to uncover reproducible brain-behavior relationships. Linking cortical networks to subcortical regions recapitulates a task-positive to task-negative axis. This new atlas enables holistic connectome mapping and characterization of cortico-subcortical connectivity.

Anxiety in Parkinson's disease is associated with changes in the brain fear circuit

BACKGROUND

Anxiety is frequent in Parkinson's disease (PD) and has a negative impact on disease symptoms and quality of life. The underlying mechanisms remain largely unknown. The aim of this study was to identify anatomical and functional changes associated to PD-related anxiety by comparing the volume, shape and texture of the amygdala, the cortical thickness as well as the functional connectivity (FC) of the fear circuit in patients with and without clinically relevant anxiety.

METHODS

Non-demented PD patients were recruited, and anxiety was quantified using the Parkinson Anxiety Scale. Structural MRI was used to compare cortical thickness and amygdala structure and resting-state functional MRI to compare FC patterns of the amygdala and resting-state functional networks in both groups.

RESULTS

We included 118 patients: 34 with (A+) and 84 without (A-) clinically relevant anxiety. Clusters of cortical thinning were identified in the bilateral fronto-cingulate and left parietal cortices of the A+ group. The texture and the shape of the left amygdala was different in the A+ group but the overall volume did not differ between groups. FC between the amygdala and the whole brain regions did not differ between groups. The internetwork resting-state FC was higher between the "fear circuit" and salience network in the A+ group.

CONCLUSION

Anxiety in PD induces structural modifications of the left amygdala, atrophy of the bilateral fronto-cingulate and the left parietal cortices, and a higher internetwork resting-state FC between the fear circuit and the salience network.

Choosing to view morbid information involves reward circuitry

People often seek out stories, videos or images that detail death, violence or harm. Considering the ubiquity of this behavior, it is surprising that we know very little about the neural circuits involved in choosing negative information. Using fMRI, the present study shows that choosing intensely negative stimuli engages similar brain regions as those that support extrinsic incentives and “regular” curiosity. Participants made choices to view negative and positive images, based on negative (e.g., a soldier kicks a civilian against his head) and positive (e.g., children throw flower petals at a wedding) verbal cues. We hypothesized that the conflicting, but relatively informative act of choosing to view a negative image, resulted in stronger activation of reward circuitry as opposed to the relatively uncomplicated act of choosing to view a positive stimulus. Indeed, as preregistered, we found that choosing negative cues was associated with activation of the striatum, inferior frontal gyrus, anterior insula, and anterior cingulate cortex, both when contrasting against a passive viewing condition, and when contrasting against positive cues. These findings nuance models of decision-making, valuation and curiosity, and are an important starting point when considering the value of seeking out negative content.

Finding the neural correlates of collaboration using a three-person fMRI hyperscanning paradigm

Humans have an extraordinary ability to interact and cooperate with others. Despite the social and evolutionary significance of collaboration, research on finding its neural correlates has been limited partly due to restrictions on the simultaneous neuroimaging of more than one participant (also known as hyperscanning). Several studies have used dyadic fMRI hyperscanning to examine the interaction between two participants. However, to our knowledge, no study to date has aimed at revealing the neural correlates of social interactions using a three-person (or triadic) fMRI hyperscanning paradigm. Here, we simultaneously measured the blood-oxygenation level-dependent signal from 12 triads (n = 36 participants), while they engaged in a collaborative drawing task based on the social game of Pictionary General linear model analysis revealed increased activation in the brain regions previously linked with the theory of mind during the collaborative phase compared to the independent phase of the task. Furthermore, using intersubject correlation analysis, we revealed increased synchronization of the right temporo-parietal junction (R TPJ) during the collaborative phase. The increased synchrony in the R TPJ was observed to be positively associated with the overall team performance on the task. In sum, our paradigm revealed a vital role of the R TPJ among other theory-of-mind regions during a triadic collaborative drawing task.

Neural Correlates of Theory of Mind Are Preserved in Young Women With Anorexia Nervosa

People with anorexia nervosa (AN) commonly exhibit social difficulties, which may be related to problems with understanding the perspectives of others, commonly known as Theory of Mind (ToM) processing. However, there is a dearth of literature investigating the neural basis of these differences in ToM and at what age they emerge. This study aimed to test for differences in the neural correlates of ToM processes in young women with AN, and young women weight-restored (WR) from AN, as compared to healthy control participants (HC). Based on previous findings in AN, we hypothesized that young women with current or prior AN, as compared to HCs, would exhibit a reduced neural response in the medial prefrontal cortex (mPFC), the inferior frontal gyrus, and the temporo-parietal junction (TPJ) whilst completing a ToM task. We recruited 73 young women with AN, 45 WR young women, and 70 young women without a history of AN to take part in the current study. Whilst undergoing a functional magnetic resonance imaging (fMRI) scan, participants completed the Frith-Happé task, which is a commonly used measure of ToM with demonstrated reliability and validity in adult populations. In this task, participants viewed the movements of triangles, which depicted either action movements, simple interactions, or complex social interactions. Viewing trials with more complex social interactions in the Frith-Happé task was associated with increased brain activation in regions including the right TPJ, the bilateral mPFC, the cerebellum, and the dorsolateral prefrontal cortex. There were no group differences in neural activation in response to the ToM contrast. Overall, these results suggest that the neural basis of spontaneous mentalizing is preserved in most young women with AN.

Potential role of primed microglia during obesity on the mesocorticolimbic circuit in autism spectrum disorder

Autism spectrum disorder (ASD) is a complex neurodevelopmental disease which involves functional and structural defects in selective central nervous system (CNS) regions that harm function and individual ability to process and respond to external stimuli. Individuals with ASD spend less time engaging in social interaction compared to non-affected subjects. Studies employing structural and functional magnetic resonance imaging reported morphological and functional abnormalities in the connectivity of the mesocorticolimbic reward pathway between the nucleus accumbens and the ventral tegmental area (VTA) in response to social stimuli, as well as diminished medial prefrontal cortex in response to visual cues, whereas stronger reward system responses for the non-social realm (e.g., video games) than social rewards (e.g., approval), associated with caudate nucleus responsiveness in ASD children. Defects in the mesocorticolimbic reward pathway have been modulated in transgenic murine models using D2 dopamine receptor heterozygous (D2+/-) or dopamine transporter knockout mice, which exhibit sociability deficits and repetitive behaviors observed in ASD phenotypes. Notably, the mesocorticolimbic reward pathway is modulated by systemic and central inflammation, such as primed microglia, which occurs during obesity or maternal overnutrition. Therefore, we propose that a positive energy balance during obesity/maternal overnutrition coordinates a systemic and central inflammatory crosstalk that modulates the dopaminergic neurotransmission in selective brain areas of the mesocorticolimbic reward pathway. Here, we will describe how obesity/maternal overnutrition may prime microglia, causing abnormalities in dopamine neurotransmission of the mesocorticolimbic reward pathway, postulating a possible immune role in the development of ASD.

3D Deep Learning on Medical Images: A Review

The rapid advancements in machine learning, graphics processing technologies and the availability of medical imaging data have led to a rapid increase in the use of deep learning models in the medical domain. This was exacerbated by the rapid advancements in convolutional neural network (CNN) based architectures, which were adopted by the medical imaging community to assist clinicians in disease diagnosis. Since the grand success of AlexNet in 2012, CNNs have been increasingly used in medical image analysis to improve the efficiency of human clinicians. In recent years, three-dimensional (3D) CNNs have been employed for the analysis of medical images. In this paper, we trace the history of how the 3D CNN was developed from its machine learning roots, we provide a brief mathematical description of 3D CNN and provide the preprocessing steps required for medical images before feeding them to 3D CNNs. We review the significant research in the field of 3D medical imaging analysis using 3D CNNs (and its variants) in different medical areas such as classification, segmentation, detection and localization. We conclude by discussing the challenges associated with the use of 3D CNNs in the medical imaging domain (and the use of deep learning models in general) and possible future trends in the field.

Electroencephalographic and Neuroimaging Asymmetry Correlation in Patients with Attention-Deficit Hyperactivity Disorder

The present study explores the correlation between electroencephalographic and neuroimaging asymmetry index from EEG-MRI functional connectome and EEG power analysis in inattention, motion, and mixed profile subgroups of ADHD. Sixty-two subjects from Healthy Brain Network Biobank of the Child Mind Institute dataset were selected basing on the quotient score. From both MRI and EEG asymmetry index, Pearson's correlation, ANOVA, and partial least square analysis were performed matching left and right brain parcels and channels. The asymmetry index significantly correlated across subjects between fMRI and power-EEG in the inattention group in frontal and temporal areas for theta and alpha bands, an anticorrelation in the same areas for delta band was found. Significant patterns of hemispheric asymmetry index have been reported, involving EEG bands that underlie cognitive impairments in ADHD. Alpha and theta bands were altered in the inattention group of patients, reflecting widespread deficiency of basic attentional processing.

Cognitive function and cerebellar morphometric changes relate to abnormal intra-cerebellar and cerebro-cerebellum functional connectivity in old adults

INTRODUCTION

Numerous structural studies have already reported volumetric reduction in cerebellum with aging. However, there are still limited studies particularly focusing on analysis of the cerebellar resting state FC in old adults. Even so, the least related studies were unable to include some important cerebellar lobules due to limited cerebellum segmentation methods.

OBJECTIVE

The purpose of this study is to explore cognitive function in relation to cerebellar lobular morphometry and cortico-cerebellar connectivity changes in old adults' lifespan by incorporating previously undetected cerebellar lobules.

METHODS

This study includes a sample of 264 old adults subdivided into five cognitively normal age groups (G1 through G5). Cerebellum Segmentation (CERES) software was used to obtain morphometric measures and brain masks of all the 24 cerebellar lobules. We then defined individual lobules as seed regions and mapped the whole-brain to get functional connectivity maps. To analyze age group differences in cortico-cerebellar connectivity and cerebellar lobular volume, we used one way ANOVA and post hoc analysis was performed for multiple comparisons using Bonferroni method.

RESULTS

Our results report cerebellar lobular volumetric reduction, disrupted intra-cerebellar connectivity and significant differences in cortico-cerebellar resting state FC across age groups. In addition, our results show that disrupted FC between left Crus-II and right ACC relates to well emotion regulation and cognitive decline and is associated with poor performance on TMT-B and logical memory tests in older adults.

CONCLUSION

Overall, our findings confirm that as humans get older and older, the cerebellar lobular volumes as well as the cortico-cerebellar functional connectivity are affected and hence reduces cognition.

Leveraging shared connectivity to aggregate heterogeneous datasets into a common response space

Connectivity hyperalignment can be used to estimate a single shared response space across disjoint datasets. We develop a connectivity-based shared response model that factorizes aggregated fMRI datasets into a single reduced-dimension shared connectivity space and subject-specific topographic transformations. These transformations resolve idiosyncratic functional topographies and can be used to project response time series into shared space. We evaluate this algorithm on a large collection of heterogeneous, naturalistic fMRI datasets acquired while subjects listened to spoken stories. Projecting subject data into shared space dramatically improves between-subject story time-segment classification and increases the dimensionality of shared information across subjects. This improvement generalizes to subjects and stories excluded when estimating the shared space. We demonstrate that estimating a simple semantic encoding model in shared space improves between-subject forward encoding and inverted encoding model performance. The shared space estimated across all datasets is distinct from the shared space derived from any particular constituent dataset; the algorithm leverages shared connectivity to yield a consensus shared space conjoining diverse story stimuli.

A data resource from concurrent intracranial stimulation and functional MRI of the human brain

Mapping the causal effects of one brain region on another is a challenging problem in neuroscience that we approached through invasive direct manipulation of brain function together with concurrent whole-brain measurement of the effects produced. Here we establish a unique resource and present data from 26 human patients who underwent electrical stimulation during functional magnetic resonance imaging (es-fMRI). The patients had medically refractory epilepsy requiring surgically implanted intracranial electrodes in cortical and subcortical locations. One or multiple contacts on these electrodes were stimulated while simultaneously recording BOLD-fMRI activity in a block design. Multiple runs exist for patients with different stimulation sites. We describe the resource, data collection process, preprocessing using the fMRIPrep analysis pipeline and management of artifacts, and provide end-user analyses to visualize distal brain activation produced by site-specific electrical stimulation. The data are organized according to the brain imaging data structure (BIDS) specification, and are available for analysis or future dataset contributions on openneuro.org including both raw and preprocessed data.

The Resting Brain Sets Support-Giving in Motion: Dorsomedial Prefrontal Cortex Activity During Momentary Rest Primes Supportive Responding

Humans give support, care, and assistance to others on a daily basis. However, the brain mechanisms that set such supportive behavior in motion are unknown. Based on previous findings demonstrating that activity in a portion of the brain’s default network—the dorsomedial prefrontal cortex (DMPFC)—during brief rest primes social thinking and behavior, momentary fluctuations in this brain region at rest may prime supportive responding. To test this hypothesis, 26 participants underwent functional magnetic resonance imaging (fMRI) while they alternated between deciding whether to give support to a close other in financial need, receive support for themselves, and make arbitrary decisions unrelated to support. Decisions were interleaved with brief periods of rest. Results showed that, within participants, spontaneous activity in the DMPFC during momentary periods of rest primed supportive-responding: greater activity in this region at the onset of a brief period of rest predicted, on a trial-by-trial basis, faster decisions to give support to the close other. Thus, activating the DMPFC as soon as our minds are free from external demands to attention may help individuals “default” to support-giving. Implications for understanding the prosocial functions of the resting brain are discussed.

Deep Generative Model of Individual Variability in fMRI Images of Psychiatric Patients

Neuroimaging techniques, such as the resting-state functional magnetic resonance imaging (fMRI), have been investigated to find objective biomarkers of neuro-logical and psychiatric disorders. Objective biomarkers potentially provide a refined diagnosis and quantitative measurements of the effects of treatment. However, fMRI images are sensitive to individual variability, such as functional topography and personal attributes. Suppressing the irrelevant individual variability is crucial for finding objective biomarkers for multiple subjects. Herein, we propose a structured generative model based on deep learning (i.e., a deep generative model) that considers such individual variability. The proposed model builds a joint distribution of (preprocessed) fMRI images, state (with or without a disorder), and individual variability. It can thereby discriminate individual variability from the subject's state. Experimental results demonstrate that the proposed model can diagnose unknown subjects with greater accuracy than conventional approaches. Moreover, the diagnosis is fairer to gender and state, because the proposed model extracts subject attributes (age, gender, and scan site) in an unsupervised manner.

Cognitive control increases honesty in cheaters but cheating in those who are honest

Every day, we are faced with the conflict between the temptation to cheat for financial gains and maintaining a positive image of ourselves as being a "good person." While it has been proposed that cognitive control is needed to mediate this conflict between reward and our moral self-image, the exact role of cognitive control in (dis)honesty remains elusive. Here we identify this role, by investigating the neural mechanism underlying cheating. We developed a task which allows for inconspicuously measuring spontaneous cheating on a trial-by-trial basis in the MRI scanner. We found that activity in the nucleus accumbens promotes cheating, particularly for individuals who cheat a lot, while a network consisting of posterior cingulate cortex, temporoparietal junction, and medial prefrontal cortex promotes honesty, particularly in individuals who are generally honest. Finally, activity in areas associated with cognitive control (anterior cingulate cortex and inferior frontal gyrus) helped dishonest participants to be honest, whereas it enabled cheating for honest participants. Thus, our results suggest that cognitive control is not needed to be honest or dishonest per se but that it depends on an individual's moral default.

What the replication crisis means for intervention science

The provocative paper by Ioannidis (2005) claiming that "most research findings are false" re-ignited longstanding concerns (see Meehl, 1967) that findings in the behavioral sciences are unlikely to be replicated. Then, a landmark paper by Nosek et al. (2015a) substantiated this conjecture, showing that, study reproducibility in psychology hovers at 40%. With the unfortunate failure of clinical trials in brain injury and other neurological disorders, it may be time to reconsider approaches not only in clinical interventions, but also how we establish their efficacy. A scientific community galvanized by a history of failed clinical trials and motivated by this "crisis" may be at critical cross-roads for change engendering a culture of transparent, open science where the primary goal is to test and not support hypotheses about specific interventions. The outcome of this scientific introspection could be a paradigm shift that accelerates our science bringing investigators closer to important advancements in rehabilitation medicine. In this commentary we offer a brief summary of how open science, study pre-registration and reorganization of scientific incentive structure could advance the clinical sciences.

Reward-Sensitive Basal Ganglia Stabilize the Maintenance of Goal-Relevant Neural Patterns in Adolescents

Maturation of basal ganglia (BG) and frontoparietal circuitry parallels developmental gains in working memory (WM). Neurobiological models posit that adult WM performance is enhanced by communication between reward-sensitive BG and frontoparietal regions, via increased stability in the maintenance of goal-relevant neural patterns. It is not known whether this reward-driven pattern stability mechanism may have a role in WM development. In 34 young adolescents (12.16-14.72 years old) undergoing fMRI, reward-sensitive BG regions were localized using an incentive processing task. WM-sensitive regions were localized using a delayed-response WM task. Functional connectivity analyses were used to examine the stability of goal-relevant functional connectivity patterns during WM delay periods between and within reward-sensitive BG and WM-sensitive frontoparietal regions. Analyses revealed that more stable goal-relevant connectivity patterns between reward-sensitive BG and WM-sensitive frontoparietal regions were associated with both greater adolescent age and WM ability. Computational lesion models also revealed that functional connections to WM-sensitive frontoparietal regions from reward-sensitive BG uniquely increased the stability of goal-relevant functional connectivity patterns within frontoparietal regions. Findings suggested (1) the extent to which goal-relevant communication patterns within reward-frontoparietal circuitry are maintained increases with adolescent development and WM ability and (2) communication from reward-sensitive BG to frontoparietal regions enhances the maintenance of goal-relevant neural patterns in adolescents' WM. The maturation of reward-driven stability of goal-relevant neural patterns may provide a putative mechanism for understanding the developmental enhancement of WM.

Fine-grain atlases of functional modes for fMRI analysis

Population imaging markedly increased the size of functional-imaging datasets, shedding new light on the neural basis of inter-individual differences. Analyzing these large data entails new scalability challenges, computational and statistical. For this reason, brain images are typically summarized in a few signals, for instance reducing voxel-level measures with brain atlases or functional modes. A good choice of the corresponding brain networks is important, as most data analyses start from these reduced signals. We contribute finely-resolved atlases of functional modes, comprising from 64 to 1024 networks. These dictionaries of functional modes (DiFuMo) are trained on millions of fMRI functional brain volumes of total size 2.4 ​TB, spanned over 27 studies and many research groups. We demonstrate the benefits of extracting reduced signals on our fine-grain atlases for many classic functional data analysis pipelines: stimuli decoding from 12,334 brain responses, standard GLM analysis of fMRI across sessions and individuals, extraction of resting-state functional-connectomes biomarkers for 2500 individuals, data compression and meta-analysis over more than 15,000 statistical maps. In each of these analysis scenarii, we compare the performance of our functional atlases with that of other popular references, and to a simple voxel-level analysis. Results highlight the importance of using high-dimensional "soft" functional atlases, to represent and analyze brain activity while capturing its functional gradients. Analyses on high-dimensional modes achieve similar statistical performance as at the voxel level, but with much reduced computational cost and higher interpretability. In addition to making them available, we provide meaningful names for these modes, based on their anatomical location. It will facilitate reporting of results.

Temporal selectivity declines in the aging human auditory cortex

Current models successfully describe the auditory cortical response to natural sounds with a set of spectro-temporal features. However, these models have hardly been linked to the ill-understood neurobiological changes that occur in the aging auditory cortex. Modelling the hemodynamic response to a rich natural sound mixture in N = 64 listeners of varying age, we here show that in older listeners’ auditory cortex, the key feature of temporal rate is represented with a markedly broader tuning. This loss of temporal selectivity is most prominent in primary auditory cortex and planum temporale, with no such changes in adjacent auditory or other brain areas. Amongst older listeners, we observe a direct relationship between chronological age and temporal-rate tuning, unconfounded by auditory acuity or model goodness of fit. In line with senescent neural dedifferentiation more generally, our results highlight decreased selectivity to temporal information as a hallmark of the aging auditory cortex.

It can often be difficult for an older person to understand what someone is saying, particularly in noisy environments. Exactly how and why this age-related change occurs is not clear, but it is thought that older individuals may become less able to tune in to certain features of sound.

Newer tools are making it easier to study age-related changes in hearing in the brain. For example, functional magnetic resonance imaging (fMRI) can allow scientists to ‘see’ and measure how certain parts of the brain react to different features of sound. Using fMRI data, researchers can compare how younger and older people process speech. They can also track how speech processing in the brain changes with age.

Now, Erb et al. show that older individuals have a harder time tuning into the rhythm of speech. In the experiments, 64 people between the ages of 18 to 78 were asked to listen to speech in a noisy setting while they underwent fMRI. The researchers then tested a computer model using the data. In the older individuals, the brain’s tuning to the timing or rhythm of speech was broader, while the younger participants were more able to finely tune into this feature of sound. The older a person was the less able their brain was to distinguish rhythms in speech, likely making it harder to understand what had been said.

This hearing change likely occurs because brain cells become less specialised overtime, which can contribute to many kinds of age-related cognitive decline. This new information about why understanding speech becomes more difficult with age may help scientists develop better hearing aids that are individualised to a person’s specific needs.

Incorporating structured assumptions with probabilistic graphical models in fMRI data analysis

With the wide adoption of functional magnetic resonance imaging (fMRI) by cognitive neuroscience researchers, large volumes of brain imaging data have been accumulated in recent years. Aggregating these data to derive scientific insights often faces the challenge that fMRI data are high-dimensional, heterogeneous across people, and noisy. These challenges demand the development of computational tools that are tailored both for the neuroscience questions and for the properties of the data. We review a few recently developed algorithms in various domains of fMRI research: fMRI in naturalistic tasks, analyzing full-brain functional connectivity, pattern classification, inferring representational similarity and modeling structured residuals. These algorithms all tackle the challenges in fMRI similarly: they start by making clear statements of assumptions about neural data and existing domain knowledge, incorporate those assumptions and domain knowledge into probabilistic graphical models, and use those models to estimate properties of interest or latent structures in the data. Such approaches can avoid erroneous findings, reduce the impact of noise, better utilize known properties of the data, and better aggregate data across groups of subjects. With these successful cases, we advocate wider adoption of explicit model construction in cognitive neuroscience. Although we focus on fMRI, the principle illustrated here is generally applicable to brain data of other modalities.

Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques

Statistical learning, a fundamental skill to extract regularities in the environment, is often considered a core supporting mechanism of the first language development. While many studies of statistical learning are conducted within a single domain or modality, recent evidence suggests that this skill may differ based on the context in which the stimuli are presented. In addition, few studies investigate learning as it unfolds in real-time, rather focusing on the outcome of learning. In this protocol, we describe an approach for identifying the cognitive and neural basis of statistical learning, within an individual, across domains (linguistic vs. non-linguistic) and sensory modalities (visual and auditory). The tasks are designed to cast as little cognitive demand as possible on participants, making it ideal for young school-aged children and special populations. The web-based nature of the behavioral tasks offers a unique opportunity for us to reach more representative populations nationwide, to estimate effect sizes with greater precision, and to contribute to open and reproducible research. The neural measures provided by the functional magnetic resonance imaging (fMRI) task can inform researchers about the neural mechanisms engaged during statistical learning, and how these may differ across individuals on the basis of domain or modality. Finally, both tasks allow for the measurement of real-time learning, as changes in reaction time to a target stimulus is tracked across the exposure period. The main limitation of using this protocol relates to the hour-long duration of the experiment. Children might need to complete all four statistical learning tasks in multiple sittings. Therefore, the web-based platform is designed with this limitation in mind so that tasks may be disseminated individually. This methodology will allow users to investigate how the process of statistical learning unfolds across and within domains and modalities in children from different developmental backgrounds.

Proximal threats promote enhanced acquisition and persistence of reactive fear-learning circuits

Physical proximity to a traumatic event increases the severity of accompanying stress symptoms, an effect that is reminiscent of evolutionarily configured fear responses based on threat imminence. Despite being widely adopted as a model system for stress and anxiety disorders, fear-conditioning research has not yet characterized how threat proximity impacts the mechanisms of fear acquisition and extinction in the human brain. We used three-dimensional (3D) virtual reality technology to manipulate the egocentric distance of conspecific threats while healthy adult participants navigated virtual worlds during functional magnetic resonance imaging (fMRI). Consistent with theoretical predictions, proximal threats enhanced fear acquisition by shifting conditioned learning from cognitive to reactive fear circuits in the brain and reducing amygdala-cortical connectivity during both fear acquisition and extinction. With an analysis of representational pattern similarity between the acquisition and extinction phases, we further demonstrate that proximal threats impaired extinction efficacy via persistent multivariate representations of conditioned learning in the cerebellum, which predicted susceptibility to later fear reinstatement. These results show that conditioned threats encountered in close proximity are more resistant to extinction learning and suggest that the canonical neural circuitry typically associated with fear learning requires additional consideration of a more reactive neural fear system to fully account for this effect.

Back-to-back regression: Disentangling the influence of correlated factors from multivariate observations

Identifying causes solely from observations can be particularly challenging when i) the factors under investigation are difficult to manipulate independently from one-another and ii) observations are high-dimensional. To address this issue, we introduce ''Back-to-Back'' regression (B2B), a linear method designed to efficiently estimate, from a set of correlated factors, those that most plausibly account for multidimensional observations. First, we prove the consistency of B2B, its links to other linear approaches, and show how it can provide a robust, unbiased and interpretable scalar estimate for each factor. Second, we use a variety of simulated data to show that B2B can outperform forward modeling ("encoding"), backward modeling ("decoding") as well as cross-decomposition modeling (i.e. canonical correlation analysis and partial least squares) on causal identification when the factors and the observations are not orthogonal. Finally, we apply B2B to a hundred magneto-encephalography recordings and to a hundred functional Magnetic Resonance Imaging recordings acquired while subjects performed a 1 ​h reading task. B2B successfully disentangles the respective contribution of collinear factors such as word length, word frequency in the early visual and late associative cortical responses respectively. B2B compared favorably to other standard techniques on this disentanglement. We discuss how the speed and the generality of B2B sets promising foundations to help identify the causal contributions of covarying factors from high-dimensional observations.

Religious belief and cognitive conflict sensitivity: A preregistered fMRI study

In the current preregistered fMRI study, we investigated the relationship between religiosity and behavioral and neural mechanisms of conflict processing, as a conceptual replication of the study by Inzlicht et al., (2009). Participants (N=193) performed a gender-Stroop task and afterwards completed standardized measures to assess their religiosity. As expected, the task induced cognitive conflict at the behavioral level and at a neural level this was reflected in increased activity in the anterior cingulate cortex (ACC). However, individual differences in religiosity were not related to performance on the Stroop task as measured in accuracy and interference effects, nor to neural markers of response conflict (correct responses vs. errors) or informational conflict (congruent vs. incongruent stimuli). Overall, we obtained moderate to strong evidence in favor of the null hypotheses that religiosity is unrelated to cognitive conflict sensitivity. We discuss the implications for the neuroscience of religion and emphasize the importance of designing studies that more directly implicate religious concepts and behaviors in an ecologically valid manner.

Behavioral inhibition and reward processing in college binge drinkers with and without marijuana use

AIM

Binge drinking is common during college, and studies have shown that many college students drink in quantities that far exceed the standard binge drinking threshold. Previous research has noted personality differences in individuals who engage in binge drinking, but few studies have examined neurobiological differences in both standard bingers (4/5 drinks in two hours for females/males; sBinge) and extreme binge drinkers (8+/10+ drinks in two hours for females/males; eBinge).

METHOD

The current study of 221 college students used functional magnetic resonance imaging (fMRI) to study neural activation on a stop signal task (SST) to assess behavioral inhibition and a monetary incentive delay (MID) task to assess activation to rewards and losses. Non-bingeing controls, sBinge, and eBinge freshmen and sophomores were recruited. In addition, because binge/extreme binge drinking is often associated with marijuana (MJ) use, MJ + sBinge and MJ + eBinge groups were also included.

RESULTS

All five groups showed strong activation in expected key cortical and striatal regions on both the SST and the MID. However, there were no significant differences between groups either at the whole-brain level or in specific regions of interest. Behavioral performance on the fMRI tasks also did not differ between groups.

CONCLUSIONS

These results suggest that our sample of individuals who engage in binge or extreme binge drinking with or without MJ co-use do not differ in brain activity on reward and inhibitory tasks. Neural differences may be present on other cognitive tasks or may emerge later after more sustained use of alcohol, MJ, and other drugs.

Attentional reorientation along the meridians of the visual field: Are there different neural mechanisms at play?

Hemispatial neglect, after unilateral lesions to parietal brain areas, is characterized by an inability to respond to unexpected stimuli in contralesional space. As the visual field's horizontal meridian is most severely affected, the brain networks controlling visuospatial processes might be tuned explicitly to this axis. We investigated such a potential directional tuning in the dorsal and ventral frontoparietal attention networks, with a particular focus on attentional reorientation. We used an orientation‐discrimination task where a spatial precue indicated the target position with 80% validity. Healthy participants (n = 29) performed this task in two runs and were required to (re‐)orient attention either only along the horizontal or the vertical meridian, while fMRI and behavioral measures were recorded. By using a general linear model for behavioral and fMRI data, dynamic causal modeling for effective connectivity, and other predictive approaches, we found strong statistical evidence for a reorientation effect for horizontal and vertical runs. However, neither neural nor behavioral measures differed between vertical and horizontal reorienting. Moreover, models from one run successfully predicted the cueing condition in the respective other run. Our results suggest that activations in the dorsal and ventral attention networks represent higher‐order cognitive processes related to spatial attentional (re‐)orientating that are independent of directional tuning and that unilateral attention deficits after brain damage are based on disrupted interactions between higher‐level attention networks and sensory areas.

Adolescents at clinical high risk for psychosis show qualitatively altered patterns of activation during rule learning

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Novel investigation of novel rule learning in psychosis risk.

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Failed to replicate previous study comparing novel and practiced rule learning.

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No significant group differences, but effect size comparison revealed differences.

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Results suggest that psychosis risk group may rely on different rule retrieval strategies.

Background

The ability to flexibly apply rules to novel situations is a critical aspect of adaptive human behavior. While executive function deficits are known to appear early in the course of psychosis, it is unclear which specific facets are affected. Identifying whether rule learning is impacted at the early stages of psychosis is necessary for truly understanding the etiology of psychosis and may be critical for designing novel treatments. Therefore, we examined rule learning in healthy adolescents and those meeting criteria for clinical high risk (CHR) for psychosis.

Methods

24 control and 22 CHR adolescents underwent rapid, high-resolution fMRI while performing a paradigm which required them to apply novel or practiced task rules.

Results

Previous work has suggested that practiced rules rely on rostrolateral prefrontal cortex (RLPFC) during rule encoding and dorsolateral prefrontal cortex (DLPFC) during task performance, while novel rules show the opposite pattern. We failed to replicate this finding, with greater activity for novel rules during performance. Comparing the HC and CHR group, there were no statistically significant effects, but an effect size analysis found that the CHR group showed less activation during encoding and greater activation during performance. This suggests the CHR group may use less efficient reactive control to retrieve task rules at the time of task performance, rather than proactively during rule encoding.

Conclusions

These findings suggest that flexibility is qualitatively altered in the clinical high risk state, however, more data is needed to determine whether these deficits predict disease progression.

Analysis of task-based functional MRI data preprocessed with fMRIPrep

Functional magnetic resonance imaging (fMRI) is a standard tool to investigate the neural correlates of cognition. fMRI noninvasively measures brain activity, allowing identification of patterns evoked by tasks performed during scanning. Despite the long history of this technique, the idiosyncrasies of each dataset have led to the use of ad-hoc preprocessing protocols customized for nearly every different study. This approach is time consuming, error prone and unsuitable for combining datasets from many sources. Here we showcase fMRIPrep (http://fmriprep.org), a robust tool to prepare human fMRI data for statistical analysis. This software instrument addresses the reproducibility concerns of the established protocols for fMRI preprocessing. By leveraging the Brain Imaging Data Structure to standardize both the input datasets (MRI data as stored by the scanner) and the outputs (data ready for modeling and analysis), fMRIPrep is capable of preprocessing a diversity of datasets without manual intervention. In support of the growing popularity of fMRIPrep, this protocol describes how to integrate the tool in a task-based fMRI investigation workflow.

Brain Network Disruption in Whiplash

BACKGROUND AND PURPOSE

Whiplash-associated disorders frequently develop following motor vehicle collisions and often involve a range of cognitive and affective symptoms, though the neural correlates of the disorder are largely unknown. In this study, a sample of participants with chronic whiplash injuries were scanned by using resting-state fMRI to assess brain network changes associated with long-term outcome metrics.

MATERIALS AND METHODS

Resting-state fMRI was collected for 23 participants and used to calculate network modularity, a quantitative measure of the functional segregation of brain region communities. This was analyzed for associations with whiplash-associated disorder outcome metrics, including scales of neck disability, traumatic distress, depression, and pain. In addition to these clinical scales, cervical muscle fat infiltration was quantified by using Dixon fat-water imaging, which has shown promise as a biomarker for assessing disorder severity and predicting recovery in chronic whiplash.

RESULTS

An association was found between brain network structure and muscle fat infiltration, wherein lower network modularity was associated with larger amounts of cervical muscle fat infiltration after controlling for age, sex, body mass index, and scan motion (t = -4.02, partial R ²  = 0.49, P < .001).

CONCLUSIONS

This work contributes to the existing whiplash literature by examining a sample of participants with whiplash-associated disorder by using resting-state fMRI. Less modular brain networks were found to be associated with greater amounts of cervical muscle fat infiltration suggesting a connection between disorder severity and neurologic changes, and a potential role for neuroimaging in understanding the pathophysiology of chronic whiplash-associated disorders.

Variability in the analysis of a single neuroimaging dataset by many teams

Data analysis workflows in many scientific domains have become increasingly complex and flexible. Here we assess the effect of this flexibility on the results of functional magnetic resonance imaging by asking 70 independent teams to analyse the same dataset, testing the same 9 ex-ante hypotheses1. The flexibility of analytical approaches is exemplified by the fact that no two teams chose identical workflows to analyse the data. This flexibility resulted in sizeable variation in the results of hypothesis tests, even for teams whose statistical maps were highly correlated at intermediate stages of the analysis pipeline. Variation in reported results was related to several aspects of analysis methodology. Notably, a meta-analytical approach that aggregated information across teams yielded a significant consensus in activated regions. Furthermore, prediction markets of researchers in the field revealed an overestimation of the likelihood of significant findings, even by researchers with direct knowledge of the dataset2-5. Our findings show that analytical flexibility can have substantial effects on scientific conclusions, and identify factors that may be related to variability in the analysis of functional magnetic resonance imaging. The results emphasize the importance of validating and sharing complex analysis workflows, and demonstrate the need for performing and reporting multiple analyses of the same data. Potential approaches that could be used to mitigate issues related to analytical variability are discussed.

Emotional Instability Relates to Ventral Striatum Activity During Reward Anticipation in Females

Both non-emotional symptoms, such as inattention, and symptoms of emotional instability (EI) are partially co-varying and normally distributed in the general population. Attention Deficit Hyperactivity Disorder (ADHD), which is associated with both inattention and emotional instability, has been related to lower reward anticipation activation in the ventral striatum. However, it is not known whether non-emotional dysregulation, such as inattention, or EI—or both—are associated with this effect. We hypothesized that altered reward processing relates specifically to EI. To test this, 29 healthy participants were recruited to this functional MRI study (n = 15 females). Reward processing was studied using a modified version of the Monetary Incentive Delay (MID) task. Brown Attention-Deficit Disorder Scales questionnaire was used to assess EI and inattention symptoms on a trait level. We observed less ventral striatal activation during reward anticipation related to the EI trait in females, also when controlling for the inattention trait, but not in the whole sample or males only. Our study suggests the existence of sex differences in the relationship between reward processing and EI/inattention traits.

Other people's gaze encoded as implied motion in the human brain

Keeping track of other people's gaze is an essential task in social cognition and key for successfully reading other people's intentions and beliefs (theory of mind). Recent behavioral evidence suggests that we construct an implicit model of other people's gaze, which may incorporate physically incoherent attributes such as a construct of force-carrying beams that emanate from the eyes. Here, we used functional magnetic resonance imaging and multivoxel pattern analysis to test the prediction that the brain encodes gaze as implied motion streaming from an agent toward a gazed-upon object. We found that a classifier, trained to discriminate the direction of visual motion, significantly decoded the gaze direction in static images depicting a sighted face, but not a blindfolded one, from brain activity patterns in the human motion-sensitive middle temporal complex (MT+) and temporo-parietal junction (TPJ). Our results demonstrate a link between the visual motion system and social brain mechanisms, in which the TPJ, a key node in theory of mind, works in concert with MT+ to encode gaze as implied motion. This model may be a fundamental aspect of social cognition that allows us to efficiently connect agents with the objects of their attention. It is as if the brain draws a quick visual sketch with moving arrows to help keep track of who is attending to what. This implicit, fluid-flow model of other people's gaze may help explain culturally universal myths about the mind as an energy-like, flowing essence.

Network organization during probabilistic learning via taste outcomes

Reinforcement learning guides food decisions, yet how the brain learns from taste in humans is not fully understood. Existing research examines reinforcement learning from taste using passive condition paradigms, but response-dependent instrumental conditioning better reflects natural eating behavior. Here, we examined brain response during a taste-motivated reinforcement learning task and how measures of task-based network structure were related to behavioral outcomes. During a functional MRI scan, 85 participants completed a probabilistic selection task with feedback via sweet taste or bitter taste. Whole brain response and functional network topology measures, including identification of communities and community segregation, were examined during choice, sweet taste, and bitter taste conditions. Relative to the bitter taste, sweet taste was associated with increased whole brain response in the hippocampus, oral somatosensory cortex, and orbitofrontal cortex. Sweet taste was also related to differential community assignment of the ventromedial prefrontal cortex and ventrolateral prefrontal cortex compared to bitter taste. During choice, increasing segregation of a community containing the amygdala, hippocampus, and right fusiform gyrus was associated with increased sensitivity to punishment on the task's posttest. Further, normal BMI was associated with differential community structure compared to overweight and obese BMI, where high BMI reflected increased connectivity of visual regions. Together, results demonstrate that network topology of learning and memory regions during choice is related to avoiding a bitter taste, and that BMI is associated with increased connectivity of area involved in processing external stimuli. Network organization and topology provide unique insight into individual differences in brain response to instrumental conditioning via taste reinforcers.

Dynamic functional connectivity between nucleus accumbens and the central executive network relates to chronic cannabis use

The neural mechanisms of drug cue‐reactivity regarding the temporal fluctuations of functional connectivity, namely the dynamic connectivity, are sparsely studied. Quantifying the task‐modulated variability in dynamic functional connectivity at cue exposure can aid the understanding. We analyzed changes in dynamic connectivity in 54 adult cannabis users and 90 controls during a cannabis cue exposure task. The variability was measured as standard deviation in the (a) connectivity weights of the default mode, the central executive, and the salience networks and two reward loci (amygdalae and nuclei accumbens); and (b) topological indexes of the whole brain (global efficiency, modularity and network resilience). These were compared for the main effects of task conditions and the group (users vs. controls), and correlated with pre‐ and during‐scan subjective craving. The variability of connectivity weights between the central executive network and nuclei accumbens was increased in users throughout the cue exposure task, and, was positively correlated with during‐scan craving for cannabis. The variability of modularity was not different by groups, but positively correlated with prescan craving. The variability of dynamic connectivity during cannabis cue exposure task between the central executive network and the nuclei accumbens, and, the level of modularity, seem to relate to the neural underpinning of cannabis use and the subjective craving.

Neural correlates of self-evaluation in relation to age and pubertal development in early adolescent girls

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Self-evaluation of social traits by adolescent girls elicited activation in midline brain regions.

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Age and pubertal development were not related to neural activation during self-evaluation.

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Higher vmPFC and pgACC activation were related to a higher probability of endorsing negative traits.

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Higher activation in those regions was also related to a lower probability of endorsing positive traits.

Early adolescence is marked by puberty, and is also a time of flux in self-perception. However, there is limited research on the neural correlates of self-evaluation in relation to pubertal development. The current study examined relationships between neural activation during self-evaluation of social traits and maturation (age and pubertal development) in a community sample of female adolescents. Participants (N = 143; age M = 11.65, range = 10.0–13.0) completed a functional MRI task in which they judged the self-descriptiveness of adjectives for prosocial, antisocial and social status-related traits. Pubertal development was based on self-report, and was also examined using morning salivary testosterone, dehydroepiandrosterone, and estradiol. Contrary to preregistered hypotheses, neither age nor pubertal development were related to neural activation during self-evaluation. We further examined whether activation in two regions-of-interest, the ventromedial prefrontal cortex (vmPFC) and perigenual anterior cingulate (pgACC), was associated with trial-level self-evaluative behavior. In line with preregistered hypotheses, higher vmPFC and pgACC activation during self-evaluation were both associated with a higher probability of endorsing negative adjectives, and a lower probability of endorsing positive adjectives. Future studies should examine neural trajectories of self-evaluation longitudinally, and investigate the predictive value of the neural correlates of self-evaluation for adolescent mental health.

Fast detection and reduction of local transient artifacts in resting-state fMRI

Image quality control (QC) is a critical and computationally intensive component of functional magnetic resonance imaging (fMRI). Artifacts caused by physiologic signals or hardware malfunctions are usually identified and removed during data processing offline, well after scanning sessions are complete. A system with the computational efficiency to identify and remove artifacts during image acquisition would permit rapid adjustment of protocols as issues arise during experiments. To improve the speed and accuracy of QC and functional image correction, we developed Fast Anatomy-Based Image Correction (Fast ANATICOR) with newly implemented nuisance models and an improved pipeline. We validated its performance on a dataset consisting of normal scans and scans containing known hardware-driven artifacts. Fast ANATICOR's increased processing speed may make real-time QC and image correction feasible as compared with the existing offline method.

Dynamic reconfiguration of functional brain networks during working memory training

The functional network of the brain continually adapts to changing environmental demands. The consequence of behavioral automation for task-related functional network architecture remains far from understood. We investigated the neural reflections of behavioral automation as participants mastered a dual n-back task. In four fMRI scans equally spanning a 6-week training period, we assessed brain network modularity, a substrate for adaptation in biological systems. We found that whole-brain modularity steadily increased during training for both conditions of the dual n-back task. In a dynamic analysis,we found that the autonomy of the default mode system and integration among task-positive systems were modulated by training. The automation of the n-back task through training resulted in non-linear changes in integration between the fronto-parietal and default mode systems, and integration with the subcortical system. Our findings suggest that the automation of a cognitively demanding task may result in more segregated network organization.

Working memory training reshapes the brain functional network reorganization. Here, the authors demonstrate an increase of the whole-brain network segregation during the n-back task, accompanied by alterations in dynamic communication between the default mode system and task-positive systems.

Consciousness and the Dimensionality of DOC Patients via the Generalized Ising Model

The data from patients with severe brain injuries show complex brain functions. Due to the difficulties associated with these complex data, computational modeling is an especially useful tool to examine the structure-function relationship in these populations. By using computational modeling for patients with a disorder of consciousness (DoC), not only we can understand the changes of information transfer, but we also can test changes to different states of consciousness by hypothetically changing the anatomical structure. The generalized Ising model (GIM), which specializes in using structural connectivity to simulate functional connectivity, has been proven to effectively capture the relationship between anatomical structures and the spontaneous fluctuations of healthy controls (HCs). In the present study we implemented the GIM in 25 HCs as well as in 13 DoC patients diagnosed at three different states of consciousness. Simulated data were analyzed and the criticality and dimensionality were calculated for both groups; together, those values capture the level of information transfer in the brain. Ratifying previous studies, criticality was observed in simulations of HCs. We were also able to observe criticality for DoC patients, concluding that the GIM is generalizable for DoC patients. Furthermore, dimensionality increased for the DoC group as compared to healthy controls, and could distinguish different diagnostic groups of DoC patients.

Getting to know me better: An fMRI study of intimate and superficial self-disclosure to friends during adolescence

Adolescence is often defined as a period of social reorientation, characterized by increased engagement with, and reliance on, same-aged peers. Consistent with these shifting motivations, we hypothesized that communicating information about oneself to friends would be intrinsically valued during adolescence. We specifically examined behavioral and neural differences when sharing information of varying depth in intimacy. These questions were investigated in a sample of early adolescent girls (N = 125, ages 10.0-13.0 years) who completed a self-disclosure monetary choice task while undergoing fMRI. Behaviorally, adolescents gave up more money to share superficial self-referential information than intimate self-referential information with a (real-life) close friend. Neural analyses identified extensive engagement of regions that support social cognition and emotion regulation when engaging in intimate self-disclosure. Behavioral and neural valuation of sharing superficial information were related to individual differences in self-worth and friendship quality. Comparatively, across all levels of analyses, adolescents were less likely to share intimate information. Findings highlight both the value and costs associated with self-disclosure during this time of increased peer sensitivity. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

Prefrontal cortical activation during working memory task anticipation contributes to discrimination between bipolar and unipolar depression

Distinguishing bipolar disorder (BD) from major depressive disorder (MDD) is clinically challenging, especially during depressive episodes. While both groups are characterized by aberrant working memory and anticipatory processing, the role of these processes in discriminating BD from MDD remains unexplored. In this study, we examine how brain activation corresponding to anticipation of and performance on easy vs. difficult working memory tasks with emotional stimuli contributes to discrimination among BD, MDD, and healthy controls (HC). Depressed individuals with BD (n = 18), MDD (n = 23), and HC (n = 23) were scanned while performing a working memory task in which they had to first anticipate performance on 1-back (easy) or 2-back (difficult) tasks with happy, fearful, or neutral faces, and then, perform the task. Anticipation-related and task-related brain activation was measured in the whole brain using functional magnetic resonance imagining. We used an elastic-net regression for variable selection, and a random forest classifier for BD vs. MDD classification. The former selected the activation differences (1-back minus 2-back) in the lateral and medial prefrontal cortices (PFC) during task anticipation and performance on the working memory tasks with fearful and neutral faces as variables relevant for BD vs. MDD classification. BD vs. MDD were classified with 70.7% accuracy (p < 0.01) based on the neuroimaging measures alone, with 80.5% accuracy (p = 0.001) based on clinical measures alone, and with 85.4% accuracy (p < 0.001) based on clinical and neuroimaging measures together. These findings suggest that PFC activation during working memory task anticipation and performance may be an important biological marker distinguishing BD from MDD.

Prefrontal reinstatement of contextual task demand is predicted by separable hippocampal patterns

Goal-directed behavior requires the representation of a task-set that defines the task-relevance of stimuli and guides stimulus-action mappings. Past experience provides one source of knowledge about likely task demands in the present, with learning enabling future predictions about anticipated demands. We examine whether spatial contexts serve to cue retrieval of associated task demands (e.g., context A and B probabilistically cue retrieval of task demands X and Y, respectively), and the role of the hippocampus and dorsolateral prefrontal cortex (dlPFC) in mediating such retrieval. Using 3D virtual environments, we induce context-task demand probabilistic associations and find that learned associations affect goal-directed behavior. Concurrent fMRI data reveal that, upon entering a context, differences between hippocampal representations of contexts (i.e., neural pattern separability) predict proactive retrieval of the probabilistically dominant associated task demand, which is reinstated in dlPFC. These findings reveal how hippocampal-prefrontal interactions support memory-guided cognitive control and adaptive behavior.

Spatial contexts are often predictive of the tasks to be performed in them (e.g., a kitchen predicts cooking). Here the authors show that the retrieval of task demand when encountering a spatial context depends on hippocampal-prefrontal interactions.

Greater Individual Variability in Functional Brain Activity during Working Memory Performance in young people with Autism and Executive Function Impairment

BACKGROUND

Individuals with autism spectrum disorder (ASD) often present with executive functioning (EF) deficits, including spatial working memory (SWM) impairment, which impedes real-world functioning. The present study examined task-related brain activity, connectivity and individual variability in fMRI-measured neural response during an SWM task in older youth and young adults with autism and clinically significant EF impairment.

METHODS

Neuroimaging was analyzed in 29 individuals with ASD without intellectual disability who had clinically significant EF impairment on the Behavior Rating Inventory of Executive Function, and 20 typically developing controls (participant age range=16-34). An SWM N-Back task was performed during fMRI. SWM activity (2-Back vs. 0-Back) and task-related dorsolateral prefrontal cortex (DLPFC) connectivity was examined within and between groups. Variability of neural response during SWM was also examined.

RESULTS

During SWM performance both groups activated the expected networks, and no group differences in network activation or task-related DLPFC-connectivity were found. However, greater individual variability in the pattern of SWM activity was found in the ASD versus the typically developing control group.

CONCLUSIONS

While there were no group differences in SWM task-evoked activity or connectivity, fronto-parietal network engagement was found to be more variable/idiosyncratic in ASD. Our results suggest that the fronto-parietal network may be shifted or sub-optimally engaged during SWM performance in participants with ASD with clinically significant EF impairment, with implications for developing targeted interventions for this subgroup.

Deep learning in mental health outcome research: a scoping review

Mental illnesses, such as depression, are highly prevalent and have been shown to impact an individual's physical health. Recently, artificial intelligence (AI) methods have been introduced to assist mental health providers, including psychiatrists and psychologists, for decision-making based on patients' historical data (e.g., medical records, behavioral data, social media usage, etc.). Deep learning (DL), as one of the most recent generation of AI technologies, has demonstrated superior performance in many real-world applications ranging from computer vision to healthcare. The goal of this study is to review existing research on applications of DL algorithms in mental health outcome research. Specifically, we first briefly overview the state-of-the-art DL techniques. Then we review the literature relevant to DL applications in mental health outcomes. According to the application scenarios, we categorize these relevant articles into four groups: diagnosis and prognosis based on clinical data, analysis of genetics and genomics data for understanding mental health conditions, vocal and visual expression data analysis for disease detection, and estimation of risk of mental illness using social media data. Finally, we discuss challenges in using DL algorithms to improve our understanding of mental health conditions and suggest several promising directions for their applications in improving mental health diagnosis and treatment.

Differential contribution of anterior and posterior midline regions during mental simulation of counterfactual and perspective shifts in autobiographical memories

Retrieving autobiographical memories induces a natural tendency to mentally simulate alternate versions of past events, either by reconstructing the perceptual details of the originally experienced perspective or the conceptual information of what actually occurred. Here we examined whether the episodic system recruited during imaginative experiences functionally dissociates depending on the nature of this reconstruction. Using fMRI, we evaluated differential patterns of neural activity and hippocampal connectivity when twenty-nine participants naturally recalled past negative events, shifted visual perspective, or imagined better or worse outcomes than what actually occurred. We found that counterfactual thoughts were distinguished by neural recruitment in dorsomedial prefrontal cortex, whereas shifts in visual perspective were uniquely supported by the precuneus. Additionally, connectivity with the anterior hippocampus changed depending upon the mental simulation that was performed - with enhanced hippocampal connectivity with medial prefrontal cortex for counterfactual simulations and precuneus for shifted visual perspectives. Together, our findings provide a novel assessment of differences between these common methods of mental simulation and a more detailed account for the neural network underlying episodic retrieval and reconstruction.

Colocalized White Matter Plasticity and Increased Cerebral Blood Flow Mediate the Beneficial Effect of Cardiovascular Exercise on Long-Term Motor Learning

Cardiovascular exercise (CE) is a promising intervention strategy to facilitate cognition and motor learning in healthy and diseased populations of all ages. CE elevates humoral parameters, such as growth factors, and stimulates brain changes potentially relevant for learning and behavioral adaptations. However, the causal relationship between CE-induced brain changes and human's ability to learn remains unclear. We tested the hypothesis that CE elicits a positive effect on learning via alterations in brain structure (morphological changes of gray and white matter) and function (functional connectivity and cerebral blood flow in resting state). We conducted a randomized controlled trial with healthy male and female human participants to compare the effects of a 2 week CE intervention against a non-CE control group on subsequent learning of a challenging new motor task (dynamic balancing; DBT) over 6 consecutive weeks. We used multimodal neuroimaging [T1-weighted magnetic resonance imaging (MRI), diffusion-weighted MRI, perfusion-weighted MRI, and resting state functional MRI] to investigate the neural mechanisms mediating between CE and learning. As expected, subjects receiving CE subsequently learned the DBT at a higher rate. Using a modified nonparametric combination approach along with multiple mediator analysis, we show that this learning boost was conveyed by CE-induced increases in cerebral blood flow in frontal brain regions and changes in white matter microstructure in frontotemporal fiber tracts. Our study revealed neural mechanisms for the CE-learning link within the brain, probably allowing for a higher flexibility to adapt to highly novel environmental stimuli, such as learning a complex task.SIGNIFICANCE STATEMENT It is established that cardiovascular exercise (CE) is an effective approach to promote learning and memory, yet little is known about the underlying neural transfer mechanisms through which CE acts on learning. We provide evidence that CE facilitates learning in human participants via plasticity in prefrontal white matter tracts and a colocalized increase in cerebral blood flow. Our findings are among the first to demonstrate a transfer potential of experience-induced brain plasticity. In addition to practical implications for health professionals and coaches, our work paves the way for future studies investigating effects of CE in patients suffering from prefrontal hypoperfusion or white matter diseases.

Response Dissociation in Hierarchical Cortical Circuits: a Unique Feature of Autism Spectrum Disorder

A prominent hypothesis regarding the pathophysiology of autism is that an increase in the balance between neural excitation and inhibition results in an increase in neural responses. However, previous reports of population-level response magnitude in individuals with autism have been inconsistent. Critically, network interactions have not been considered in previous neuroimaging studies of excitation and inhibition imbalance in autism. In particular, a defining characteristic of cortical organization is its hierarchical and interactive structure; sensory and cognitive systems are comprised of networks where later stages inherit and build upon the processing of earlier input stages, and also influence and shape earlier stages by top-down modulation. Here we used the well established connections of the human visual system to examine response magnitudes in a higher-order motion processing region [middle temporal area (MT+)] and its primary input region (V1). Simple visual stimuli were presented to adult individuals with autism spectrum disorders (ASD; n = 24, mean age 23 years, 8 females) and neurotypical controls (n = 24, mean age 22, 8 females) during fMRI scanning. We discovered a strong dissociation of fMRI response magnitude between region MT+ and V1 in individuals with ASD: individuals with high MT+ responses had attenuated V1 responses. The magnitude of MT+ amplification and of V1 attenuation was associated with autism severity, appeared to result from amplified suppressive feedback from MT+ to V1, and was not present in neurotypical controls. Our results reveal the potential role of altered hierarchical network interactions in the pathophysiology of ASD.SIGNIFICANCE STATEMENT An imbalance between neural excitation and inhibition, resulting in increased neural responses, has been suggested as a pathophysiological pathway to autism, but direct evidence from humans is lacking. In the current study we consider the role of interactions between stages of sensory processing when testing increased neural responses in individuals with autism. We used the well known hierarchical structure of the visual motion pathway to demonstrate dissociation in the fMRI response magnitude between adjacent stages of processing in autism: responses are attenuated in a primary visual area but amplified in a subsequent higher-order area. This response dissociation appears to rely on enhanced suppressive feedback between regions and reveals a previously unknown cortical network alteration in autism.