Small sample sizes reduce the replicability of task-based fMRI studies

Functional magnetic resonance imaging of headache: Issues, best-practices, and new directions, a narrative review

OBJECTIVE

To ensure readers are informed consumers of functional magnetic resonance imaging (fMRI) research in headache, to outline ongoing challenges in this area of research, and to describe potential considerations when asked to collaborate on fMRI research in headache, as well as to suggest future directions for improvement in the field.

BACKGROUND

Functional MRI has played a key role in understanding headache pathophysiology, and mapping networks involved with headache-related brain activity have the potential to identify intervention targets. Some investigators have also begun to explore its use for diagnosis.

METHODS/RESULTS

The manuscript is a narrative review of the current best practices in fMRI in headache research, including guidelines on transparency and reproducibility. It also contains an outline of the fundamentals of MRI theory, task-related study design, resting-state functional connectivity, relevant statistics and power analysis, image preprocessing, and other considerations essential to the field.

CONCLUSION

Best practices to increase reproducibility include methods transparency, eliminating error, using a priori hypotheses and power calculations, using standardized instruments and diagnostic criteria, and developing large-scale, publicly available datasets.

Data Augmentation in High Dimensional Low Sample Size Setting Using a Geometry-Based Variational Autoencoder

In this paper, we propose a new method to perform data augmentation in a reliable way in the High Dimensional Low Sample Size (HDLSS) setting using a geometry-based variational autoencoder (VAE). Our approach combines the proposal of 1) a new VAE model, the latent space of which is modeled as a Riemannian manifold and which combines both Riemannian metric learning and normalizing flows and 2) a new generation scheme which produces more meaningful samples especially in the context of small data sets. The method is tested through a wide experimental study where its robustness to data sets, classifiers and training samples size is stressed. It is also validated on a medical imaging classification task on the challenging ADNI database where a small number of 3D brain magnetic resonance images (MRIs) are considered and augmented using the proposed VAE framework. In each case, the proposed method allows for a significant and reliable gain in the classification metrics. For instance, balanced accuracy jumps from 66.3% to 74.3% for a state-of-the-art convolutional neural network classifier trained with 50 MRIs of cognitively normal (CN) and 50 Alzheimer disease (AD) patients and from 77.7% to 86.3% when trained with 243 CN and 210 AD while improving greatly sensitivity and specificity metrics.

Structural But Not Functional Connectivity Differences within Default Mode Network Indicate Conversion to Dementia

BACKGROUND

Malfunctioning of the default mode network (DMN) has been consistently related to mild cognitive impairment (MCI) and Alzheimer's disease (AD). However, evidence on differences in this network between MCI converters (MCI-c) and non-converters (MCI-nc), which could mark progression to AD, is still inconsistent.

OBJECTIVE

To multimodally investigate the DMN in the AD continuum.

METHODS

We measured gray matter (GM) volume, white matter (WM) integrity, and functional connectivity (FC) at rest in healthy elderly controls, MCI-c, MCI-nc, and AD patients, matched on sociodemographic variables.

RESULTS

Significant differences between AD patients and controls were found in the structure of most of the regions of the DMN. MCI-c only differed from MCI-nc in GM volume of the left parahippocampus and bilateral hippocampi and middle frontal gyri, as well as in WM integrity of the parahippocampal cingulum connecting the left hippocampus and precuneus. We found significant correlations between integrity in some of those regions and global neuropsychological status, as well as an excellent discrimination ability between converters and non-converters for the sum of GM volume of left parahippocampus, bilateral hippocampi, and middle frontal gyri, and WM integrity of left parahippocampal cingulum. However, we found no significant differences in FC.

CONCLUSION

These results further support the relationship between abnormalities in the DMN and AD, and suggest that structural measures could be more accurate than resting-state estimates as markers of conversion from MCI to AD.

Evaluation of a decided sample size in machine learning applications

BACKGROUND

An appropriate sample size is essential for obtaining a precise and reliable outcome of a study. In machine learning (ML), studies with inadequate samples suffer from overfitting of data and have a lower probability of producing true effects, while the increment in sample size increases the accuracy of prediction but may not cause a significant change after a certain sample size. Existing statistical approaches using standardized mean difference, effect size, and statistical power for determining sample size are potentially biased due to miscalculations or lack of experimental details. This study aims to design criteria for evaluating sample size in ML studies. We examined the average and grand effect sizes and the performance of five ML methods using simulated datasets and three real datasets to derive the criteria for sample size. We systematically increase the sample size, starting from 16, by randomly sampling and examine the impact of sample size on classifiers' performance and both effect sizes. Tenfold cross-validation was used to quantify the accuracy.

RESULTS

The results demonstrate that the effect sizes and the classification accuracies increase while the variances in effect sizes shrink with the increment of samples when the datasets have a good discriminative power between two classes. By contrast, indeterminate datasets had poor effect sizes and classification accuracies, which did not improve by increasing sample size in both simulated and real datasets. A good dataset exhibited a significant difference in average and grand effect sizes. We derived two criteria based on the above findings to assess a decided sample size by combining the effect size and the ML accuracy. The sample size is considered suitable when it has appropriate effect sizes (≥ 0.5) and ML accuracy (≥ 80%). After an appropriate sample size, the increment in samples will not benefit as it will not significantly change the effect size and accuracy, thereby resulting in a good cost-benefit ratio.

CONCLUSION

We believe that these practical criteria can be used as a reference for both the authors and editors to evaluate whether the selected sample size is adequate for a study.

Speaking in gestures: Left dorsal and ventral frontotemporal brain systems underlie communication in conducting

Conducting constitutes a well-structured system of signs anticipating information concerning the rhythm and dynamic of a musical piece. Conductors communicate the musical tempo to the orchestra, unifying the individual instrumental voices to form an expressive musical Gestalt. In a functional magnetic resonance imaging (fMRI) experiment, 12 professional conductors and 16 instrumentalists conducted real-time novel pieces with diverse complexity in orchestration and rhythm. For control, participants either listened to the stimuli or performed beat patterns, setting the time of a metronome or complex rhythms played by a drum. Activation of the left superior temporal gyrus (STG), supplementary and premotor cortex and Broca's pars opercularis (F3op) was shared in both musician groups and separated conducting from the other conditions. Compared to instrumentalists, conductors activated Broca's pars triangularis (F3tri) and the STG, which differentiated conducting from time beating and reflected the increase in complexity during conducting. In comparison to conductors, instrumentalists activated F3op and F3tri when distinguishing complex rhythm processing from simple rhythm processing. Fibre selection from a normative human connectome database, constructed using a global tractography approach, showed that the F3op and STG are connected via the arcuate fasciculus, whereas the F3tri and STG are connected via the extreme capsule. Like language, the anatomical framework characterising conducting gestures is located in the left dorsal system centred on F3op. This system reflected the sensorimotor mapping for structuring gestures to musical tempo. The ventral system centred on F3Tri may reflect the art of conductors to set this musical tempo to the individual orchestra's voices in a global, holistic way.

The longitudinal trajectory of emotion regulation and associated neural activity in patients with bipolar disorder: A prospective fMRI study

OBJECTIVES

Impaired emotion regulation is a key feature of bipolar disorder (BD) that presents during acute mood episodes and in remission. The neural correlates of voluntary emotion regulation seem to involve deficient prefrontal top-down regulation already at BD illness onset. However, the trajectory of aberrant neuronal activity during emotion regulation in BD is unclear.

METHODS

We investigated neural activity during emotion regulation in response to aversive pictures from the International Affective Picture System in patients with recently diagnosed BD (n = 43) in full or partial remission and in healthy controls (HC) (n = 38) longitudinally at baseline and 16 months later.

RESULTS

Patients with BD exhibited stable hypo-activity in the left dorsomedial prefrontal cortex (DMPFC) and right dorsolateral prefrontal cortex (DLPFC) and impaired emotion regulation compared to HC over the 16 months follow-up time. More DLPFC hypo-activity during emotion regulation correlated with less successful down-regulation (r = 0.16, p = 0.045), more subsyndromal depression (r = -0.18, p = 0.02) and more functional impairment (r = -0.24, p = 0.002), while more DMPFC hypo-activity correlated with less efficient emotion regulation (r = 0.16, p = 0.048). Finally, more DMPFC hypo-activity during emotion regulation at baseline was associated with an increased likelihood of subsequent relapse during the 16 months follow-up time (β = -2.26, 95% CI [0.01; 0.99], p = 0.048).

CONCLUSION

The stable DLPFC and DMPFC hypo-activity during emotion regulation represents a neuronal trait-marker of persistent emotion regulation difficulties in BD. Hypo-activity in the DMPFC may contribute to greater risk of relapse.

Associated functional network connectivity between callous-unemotionality and cognitive and affective empathy

BACKGROUND

Low empathy is one component of affective impairments defining the antisocial youth phenotype callous-unemotional (CU) traits. Research suggests CU traits may be negatively associated with neural networks that are positively associated with cognitive and affective empathy - specifically the default mode (DMN), frontoparietal (FPN), and salience (SAL) networks. Determining which functional network connections are shared between CU traits and empathy could elucidate the extent to which CU traits shares neural substrates with cognitive versus affective empathy. The present study tested whether CU traits and both cognitive and affective empathy share network connections within and between the DMN, FPN, and SAL.

METHODS

Participants (n = 112, aged 13-17, 43 % female) completed resting-state functional magnetic resonance imaging and self-reports for CU traits and empathy as part of a Nathan-Kline Institute study.

RESULTS

Analyses revealed inverse associations with shared network connections between CU traits and both cognitive and affective empathy. Specifically, within-DMN connectivity negatively associated with CU traits, but positively associated with cognitive empathy; and between DMN-SAL connectivity positively associated with CU traits, but negatively associated with both cognitive and affective empathy. However, joint models revealed little variance explained by CU traits and empathy overlapped.

LIMITATIONS

The sample was cross-sectional collection with limited participants (n = 112) from the community that may not generalize to incarcerated adolescents.

CONCLUSIONS

Results demonstrate CU traits inversely associated with similar connectivity patterns as cognitive and affective empathy though prediction among constructs did not significantly overlap. Further investigation of these connections can inform a mechanistic understanding of empathy impairments in CU traits.

Cross-cohort replicability and generalizability of connectivity-based psychometric prediction patterns

An increasing number of studies have investigated the relationships between inter-individual variability in brain regions' connectivity and behavioral phenotypes, making use of large population neuroimaging datasets. However, the replicability of brain-behavior associations identified by these approaches remains an open question. In this study, we examined the cross-dataset replicability of brain-behavior association patterns for fluid cognition and openness predictions using a previously developed region-wise approach, as well as using a standard whole-brain approach. Overall, we found moderate similarity in patterns for fluid cognition predictions across cohorts, especially in the Human Connectome Project Young Adult, Human Connectome Project Aging, and Enhanced Nathan Kline Institute Rockland Sample cohorts, but low similarity in patterns for openness predictions. In addition, we assessed the generalizability of prediction models in cross-dataset predictions, by training the model in one dataset and testing in another. Making use of the region-wise prediction approach, we showed that first, a moderate extent of generalizability could be achieved with fluid cognition prediction, and that, second, a set of common brain regions related to fluid cognition across cohorts could be identified. Nevertheless, the moderate replicability and generalizability could only be achieved in specific contexts. Thus, we argue that replicability and generalizability in connectivity-based prediction remain limited and deserve greater attention in future studies.

Structural and Functional Brain Alterations in Populations with Familial Risk for Depression: A Narrative Review

LEARNING OBJECTIVES

After completing this activity, practitioners will be better able to:• Discuss the association between brain alterations and vulnerability or resilience to MDD in people with familial risk• Define how structural and functional brain alterations associated with vulnerability or resilience could lead to a better understanding of the pathophysiology of MDD.

AIM

Familial history is associated with an increased risk for major depressive disorder (MDD). Despite the increased risk, some members of the familial high-risk population remain healthy, that is, resilient. Defining the structural and functional brain alterations associated with vulnerability or resilience could lead to a better understanding of the pathophysiology of MDD. This study aimed to review the current literature and discuss the association between brain alterations and vulnerability or resilience to MDD in people with familial risk.

METHODS

A literature search on MRI studies investigating structural and functional alterations in populations at familial risk for MDD was performed using the PubMed and SCOPUS databases. The search was conducted through June 13, 2022.

RESULTS

We reviewed and summarized the data of 72 articles (25 structural MRI, 35 functional MRI, 10 resting-state fMRI, one structural/functional MRI combined, and one structural/functional/resting-state fMRI combined). These findings suggested that resilience in high-risk individuals is related to the amygdala structure, frontal lobe activity, and functional connectivity between the amygdala and multiple frontal regions.

CONCLUSION

Resilient and vulnerable individuals exhibit structural and functional differences in multiple frontal and limbic regions. However, further systematic longitudinal research incorporating environmental factors is required to validate the current findings.

Is Psychological Science Self-Correcting? Citations Before and After Successful and Failed Replications

In principle, successful replications should enhance the credibility of scientific findings, and failed replications should reduce credibility. Yet it is unknown how replication typically affects the influence of research. We analyzed the citation history of 98 articles. Each was published by a selective psychology journal in 2008 and subjected to a replication attempt published in 2015. Relative to successful replications, failed replications reduced citations of replicated studies by only 5% to 9% on average, an amount that did not differ significantly from zero. Less than 3% of articles citing the original studies cited the replication attempt. It does not appear that replication failure much reduced the influence of nonreplicated findings in psychology. To increase the influence of replications, we recommend (a) requiring authors to cite replication studies alongside the individual findings and (b) enhancing reference databases and search engines to give higher priority to replication studies.

Are metaphors embodied? The neural evidence

concepts can potentially be represented using metaphorical mappings to concrete domains. This view predicts that when linguistic metaphors are processed, they will invoke sensory-motor simulations. Here, I examine evidence from neuroimaging and lesion studies that addresses whether metaphors in language are embodied in this manner. Given the controversy in this area, I first outline some criteria by which the quality of neuroimaging and lesion studies might be evaluated. I then review studies of metaphors in various sensory-motor domains, such as action, motion, texture, taste, and time, and examine their strengths and weaknesses. Studies of idioms are evaluated next. I also address some neuroimaging studies that can speak to the question of metaphoric conceptual organization without explicit use of linguistic metaphors. I conclude that the weight of the evidence suggests that metaphors are indeed grounded in sensory-motor systems. The case of idioms is less clear, and I suggest that they might be grounded in a qualitatively different manner than metaphors at higher levels of the action hierarchy. While metaphors are unlikely to explain all aspects of abstract concept representation, for some specific abstract concepts, there is also nonlinguistic neural evidence for metaphoric conceptual organization.

The rediscovered motor-related area 55b emerges as a core hub of music perception

Passive listening to music, without sound production or evident movement, is long known to activate motor control regions. Nevertheless, the exact neuroanatomical correlates of the auditory-motor association and its underlying neural mechanisms have not been fully determined. Here, based on a NeuroSynth meta-analysis and three original fMRI paradigms of music perception, we show that the long-ignored pre-motor region, area 55b, an anatomically unique and functionally intriguing region, is a core hub of music perception. Moreover, results of a brain-behavior correlation analysis implicate neural entrainment as the underlying mechanism of area 55b’s contribution to music perception. In view of the current results and prior literature, area 55b is proposed as a keystone of sensorimotor integration, a fundamental brain machinery underlying simple to hierarchically complex behaviors. Refining the neuroanatomical and physiological understanding of sensorimotor integration is expected to have a major impact on various fields, from brain disorders to artificial general intelligence.

Functional magnetic resonance imaging data acquired during passive listening to music suggest that pre-motor area 55b acts as a core hub of music processing in humans.

Task-Based Functional Connectivity in Attention-Deficit/Hyperactivity Disorder: A Systematic Review

Altered neurocognitive functioning is a key feature of attention-deficit/hyperactivity disorder (ADHD), and increasing numbers of studies assess task-based functional connectivity in the disorder. We systematically reviewed and critically appraised functional magnetic resonance imaging (fMRI) task-based functional connectivity studies in ADHD. A systematic search conducted up to September 2020 found 34 studies, including 51 comparisons. Comparisons were divided into investigations of ADHD neuropathology (37 comparing ADHD and typical development, 2 comparing individuals with ADHD and their nonsymptomatic siblings, 2 comparing remitted and persistent ADHD, and 1 exploring ADHD symptom severity) and the effects of interventions (8 investigations of stimulant effects and 1 study of fMRI neurofeedback). Large heterogeneity in study methodologies prevented a meta-analysis; thus, the data were summarized as a narrative synthesis. Across cognitive domains, functional connectivity in the cingulo-opercular, sensorimotor, visual, subcortical, and executive control networks in ADHD consistently differed from neurotypical populations. Furthermore, literature comparing individuals with ADHD and their nonsymptomatic siblings as well as adults with ADHD and their remitted peers showed ADHD-related abnormalities in similar sensorimotor and subcortical (primarily striatal) networks. Interventions modulated those dysfunctional networks, with the most consistent action on functional connections with the striatum, anterior cingulate cortex, occipital regions, and midline default mode network structures. Although methodological issues limited many of the reviewed studies, the use of task-based functional connectivity approaches has the potential to broaden the understanding of the neural underpinnings of ADHD and the mechanisms of action of ADHD treatments.

Functional gradients in the human lateral prefrontal cortex revealed by a comprehensive coordinate-based meta-analysis

The lateral prefrontal cortex (LPFC) of humans enables flexible goal-directed behavior. However, its functional organization remains actively debated after decades of research. Moreover, recent efforts aiming to map the LPFC through meta-analysis are limited, either in scope or in the inferred specificity of structure-function associations. These limitations are in part due to the limited expressiveness of commonly-used data analysis tools, which restricts the breadth and complexity of questions that can be expressed in a meta-analysis. Here, we adopt NeuroLang, a novel approach to more expressive meta-analysis based on probabilistic first-order logic programming, to infer the organizing principles of the LPFC from 14,371 neuroimaging studies. Our findings reveal a rostrocaudal and a dorsoventral gradient, respectively explaining the most and second most variance in meta-analytic connectivity across the LPFC. Moreover, we identify a unimodal-to-transmodal spectrum of coactivation patterns along with a concrete-to-abstract axis of structure-function associations extending from caudal to rostral regions of the LPFC. Finally, we infer inter-hemispheric asymmetries along the principal rostrocaudal gradient, identifying hemisphere-specific associations with topics of language, memory, response inhibition, and sensory processing. Overall, this study provides a comprehensive meta-analytic mapping of the LPFC, grounding future hypothesis generation on a quantitative overview of past findings.

A behavioral and brain imaging dataset with focus on emotion regulation of women with fibromyalgia

Fibromyalgia is a chronic condition characterized by widespread pain, as well as numerous symptoms related to central sensitization such as: fatigue, cognitive disturbances, constipation/diarrhea and sensory hypersensitivity. Furthermore, depression and anxiety are prevalent comorbidities, accompanied by emotion processing and regulation difficulties. Although fibromyalgia physiopathology is still not fully understood, neuroimaging research methods have shown brain structural and functional alterations as well as neuroinflammation abnormalities. We believe that open access to data may help fibromyalgia research advance more. Here, we present an open dataset of 33 fibromyalgia female patients and 33 paired healthy controls recruited from a Mexican population. Dataset includes demographic, clinical, behavioural and magnetic resonance imaging (MRI) data. The MRI data consists of: structural (T1- and T2- weighted) and functional (task-based and resting state) sequences. The task was an emotion processing and regulation task based on visual stimuli. The MRI data contained in the repository are unprocessed, presented in Brain Imaging Data Structure (BIDS) format and available on the OpenNeuro platform for future analysis.

Another failure of the latent disease model? The case of compulsive sexual behavior disorder •

Recent debates have evolved regarding the classification/conceptualization of compulsive sexual behavior disorder (CSBD). Conclusions regarding an agreed upon CSBD model are hindered by reliance on the latent disease model. Competing biological-based frameworks are moving forward to replace latent disease classification more broadly but have been met with limited success. We suggest that CSBD researchers move towards developing dimensional, transtheoretical, process-based models. We further suggest additional research, particularly mixed methods and longitudinal studies. Finally, we request that federal funding bodies take a more active role in supporting CSBD research.

How reliable are amygdala findings in psychopathy? A systematic review of MRI studies

The amygdala is a key component in predominant neural circuitry models of psychopathy. Yet, after two decades of neuroimaging research on psychopathy, the reproducibility of amygdala findings is questionable. We systematically reviewed MRI studies (81 of adults, 53 of juveniles) to determine the consistency of amygdala findings across studies, as well as within specific types of experimental tasks, community versus forensic populations, and the lowest- versus highest-powered studies. Three primary findings emerged. First, the majority of studies found null relationships between psychopathy and amygdala structure and function, even in the context of theoretically relevant tasks. Second, findings of reduced amygdala activity were more common in studies with low compared to high statistical power. Third, the majority of peak coordinates of reduced amygdala activity did not fall primarily within the anatomical bounds of the amygdala. Collectively, these findings demonstrate significant gaps in the empirical support for the theorized role of the amygdala in psychopathy and indicate the need for novel research perspectives and approaches in this field.

Abnormalities in the default mode network in late-life depression: A study of resting-state fMRI

Background/Objective

Neuroimaging studies have reported abnormalities in the examination of functional connectivity in late-life depression (LLD) in the default mode network (DMN). The present study aims to study resting-state functional connectivity within the DMN in people diagnosed with late-life major depressive disorder (MDD) compared to healthy controls (HCs). Moreover, we would like to differentiate these same connectivity patterns between participants with high vs. low anxiety levels.

Method

The sample comprised 56 participants between the ages of 60 and 75; 27 of them were patients with a diagnosis of MDD. Patients were further divided into two samples according to anxiety level: the four people with the highest anxiety level and the five with the lowest anxiety level. Clinical aspects were measured using psychological questionnaires. Each participant underwent functional magnetic resonance imaging (fMRI) acquisition in different regions of interest (ROIs) of the DMN.

Results

There was a greater correlation between pairs of ROIs in the control group than in patients with LLD, being this effect preferentially observed in patients with higher anxiety levels.

Conclusions

There are differences in functional connectivity within the DMN depending on the level of psychopathology. This can be reflected in these correlations and in the number of clusters and how the brain lateralizes (clustering).

Neuroscout, a unified platform for generalizable and reproducible fMRI research

Functional magnetic resonance imaging (fMRI) has revolutionized cognitive neuroscience, but methodological barriers limit the generalizability of findings from the lab to the real world. Here, we present Neuroscout, an end-to-end platform for analysis of naturalistic fMRI data designed to facilitate the adoption of robust and generalizable research practices. Neuroscout leverages state-of-the-art machine learning models to automatically annotate stimuli from dozens of fMRI studies using naturalistic stimuli-such as movies and narratives-allowing researchers to easily test neuroscientific hypotheses across multiple ecologically-valid datasets. In addition, Neuroscout builds on a robust ecosystem of open tools and standards to provide an easy-to-use analysis builder and a fully automated execution engine that reduce the burden of reproducible research. Through a series of meta-analytic case studies, we validate the automatic feature extraction approach and demonstrate its potential to support more robust fMRI research. Owing to its ease of use and a high degree of automation, Neuroscout makes it possible to overcome modeling challenges commonly arising in naturalistic analysis and to easily scale analyses within and across datasets, democratizing generalizable fMRI research.

Functional MRI in Radiology—A Personal Review

We, here, provide a personal review article on the development of a functional MRI in the radiology departments of two German university medicine units. Although the international community for human brain mapping has met since 1995, the researchers fascinated by human brain function are still young and innovative. However, the impact of functional magnetic resonance imaging (fMRI) on prognosis and treatment decisions is restricted, even though standardized methods have been developed. The tradeoff between the groundbreaking studies on brain function and the attempt to provide reliable biomarkers for clinical decisions is large. By describing some historical developments in the field of fMRI, from a personal view, the rise of this method in clinical neuroscience during the last 25 years might be understandable. We aim to provide some background for (a) the historical developments of fMRI, (b) the establishment of two research units for fMRI in the departments of radiology in Germany, and (c) a description of some contributions within the selected fields of systems neuroscience, clinical neurology, and behavioral psychology.

Functional neuroimaging in psychiatry and the case for failing better

Psychiatric disorders encompass complex aberrations of cognition and affect and are among the most debilitating and poorly understood of any medical condition. Current treatments rely primarily on interventions that target brain function (drugs) or learning processes (psychotherapy). A mechanistic understanding of how these interventions mediate their therapeutic effects remains elusive. From the early 1990s, non-invasive functional neuroimaging, coupled with parallel developments in the cognitive neurosciences, seemed to signal a new era of neurobiologically grounded diagnosis and treatment in psychiatry. Yet, despite three decades of intense neuroimaging research, we still lack a neurobiological account for any psychiatric condition. Likewise, functional neuroimaging plays no role in clinical decision making. Here, we offer a critical commentary on this impasse and suggest how the field might fare better and deliver impactful neurobiological insights.

A review of functional brain differences predicting relapse in substance use disorder: Actionable targets for new methods of noninvasive brain stimulation

Neuroimaging studies have identified a variety of brain regions whose activity predicts substance use (i.e., relapse) in patients with substance use disorder (SUD), suggesting that malfunctioning brain networks may exacerbate relapse. However, this knowledge has not yet led to a marked improvement in treatment outcomes. Noninvasive brain stimulation (NIBS) has shown some potential for treating SUDs, and a new generation of NIBS technologies offers the possibility of selectively altering activity in both superficial and deep brain structures implicated in SUDs. The goal of the current review was to identify deeper brain structures involved in relapse to SUD and give an account of innovative methods of NIBS that might be used to target them. Included studies measured fMRI in currently abstinent SUD patients and tracked treatment outcomes, and fMRI results were organized with the framework of the Addictions Neuroclinical Assessment (ANA). Four brain structures were consistently implicated: the anterior and posterior cingulate cortices, ventral striatum and insula. These four deeper brain structures may be appropriate future targets for the treatment of SUD using these innovative NIBS technologies.

A semi-supervised classification RBM with an improved fMRI representation algorithm

BACKGROUND AND OBJECTIVE

Training an effective and robust supervised learning classifier is not easy due to the limitations of acquiring and labeling considerable human functional magnetic resonance imaging (fMRI) data. Semi-supervised learning uses unlabeled data for feature learning and combines them into labeled data to build better classification models.

METHODS

Since no premises or assumptions are required, a restricted Boltzmann machine (RBM) is suitable for learning data representation of neuroimages. In our study, an improved fMRI representation algorithm with a hybrid L1/L2 regularization method (HRBM) was proposed to optimize the original model for sparsity. Different from common semi-supervised classification models that treat feature learning and classification as two separate training steps, we then constructed a new semi-supervised classification RBM based on a joint training algorithm with HRBM, named Semi-HRBM. This joint training algorithm jointly trains the objective function of feature learning and classification process, so that the learned features can effectively represent the original fMRI data and adapt to the classification tasks.

RESULTS

This study uses fMRI data to identify categories of visual stimuli. In the fMRI data classification task under four visual stimuli (house, face, car, and cat), our HRBM has satisfactory feature representation capabilities and better performance for multiple classification tasks. Taking the supervised RBM (sup-RBM) as an example, our Semi-HRBM classification model improves the average accuracy of the four-classification task by 7.68%, and improves the average F1 score of each visual stimulus task by 8.90%. In addition, the generalization ability of the model was also improved.

CONCLUSION

This research might contribute to enrich solutions for insufficiently labeled neuroimaging samples, which could help to identify complex brain states under different stimuli or tasks.

Cross-site harmonization of multi-shell diffusion MRI measures based on rotational invariant spherical harmonics (RISH)

Quantification methods based on the acquisition of diffusion magnetic resonance imaging (dMRI) with multiple diffusion weightings (e.g., multi-shell) are becoming increasingly applied to study the in-vivo brain. Compared to single-shell data for diffusion tensor imaging (DTI), multi-shell data allows to apply more complex models such as diffusion kurtosis imaging (DKI), which attempts to capture both diffusion hindrance and restriction effects, or biophysical models such as NODDI, which attempt to increase specificity by separating biophysical components. Because of the strong dependence of the dMRI signal on the measurement hardware, DKI and NODDI metrics show scanner and site differences, much like other dMRI metrics. These effects limit the implementation of multi-shell approaches in multicenter studies, which are needed to collect large sample sizes for robust analyses. Recently, a post-processing technique based on rotation invariant spherical harmonics (RISH) features was introduced to mitigate cross-scanner differences in DTI metrics. Unlike statistical harmonization methods, which require repeated application to every dMRI metric of choice, RISH harmonization is applied once on the raw data, and can be followed by any analysis. RISH features harmonization has been tested on DTI features but not its generalizability to harmonize multi-shell dMRI. In this work, we investigated whether performing the RISH features harmonization of multi-shell dMRI data removes cross-site differences in DKI and NODDI metrics while retaining longitudinal effects. To this end, 46 subjects underwent a longitudinal (up to 3 time points) two-shell dMRI protocol at 3 imaging sites. DKI and NODDI metrics were derived before and after harmonization and compared both at the whole brain level and at the voxel level. Then, the harmonization effects on cross-sectional and on longitudinal group differences were evaluated. RISH features averaged for each of the 3 sites exhibited prominent between-site differences in the frontal and posterior part of the brain. Statistically significant differences in fractional anisotropy, mean diffusivity and mean kurtosis were observed both at the whole brain and voxel level between all the acquisition sites before harmonization, but not after. The RISH method also proved effective to harmonize NODDI metrics, particularly in white matter. The RISH based harmonization maintained the magnitude and variance of longitudinal changes as compared to the non-harmonized data of all considered metrics. In conclusion, the application of RISH feature based harmonization to multi-shell dMRI data can be used to remove cross-site differences in DKI metrics and NODDI analyses, while retaining inherent relations between longitudinal acquisitions.

Dissociation between individual differences in self-reported pain intensity and underlying fMRI brain activation

Pain is an individual experience. Previous studies have highlighted changes in brain activation and morphology associated with within- and interindividual pain perception. In this study we sought to characterize brain mechanisms associated with between-individual differences in pain in a sample of healthy adolescent and adult participants (N = 101). Here we show that pain ratings varied widely across individuals and that individuals reported changes in pain evoked by small differences in stimulus intensity in a manner congruent with their pain sensitivity, further supporting the utility of subjective reporting as a measure of the true individual experience. Furthermore, brain activation related to interindividual differences in pain was not detected, despite clear sensitivity of the Blood Oxygenation Level-Dependent (BOLD) signal to small differences in noxious stimulus intensities within individuals. These findings suggest fMRI may not be a useful objective measure to infer reported pain intensity.

Functional Connectivity of the Chemosenses: A Review

Functional connectivity approaches have long been used in cognitive neuroscience to establish pathways of communication between and among brain regions. However, the use of these analyses to better understand how the brain processes chemosensory information remains nascent. In this review, we conduct a literature search of all functional connectivity papers of olfaction, gustation, and chemesthesis, with 103 articles discovered in total. These publications largely use approaches of seed-based functional connectivity and psychophysiological interactions, as well as effective connectivity approaches such as Granger Causality, Dynamic Causal Modeling, and Structural Equation Modeling. Regardless of modality, studies largely focus on elucidating neural correlates of stimulus qualities such as identity, pleasantness, and intensity, with task-based paradigms most frequently implemented. We call for further "model free" or data-driven approaches in predictive modeling to craft brain-behavior relationships that are free from a priori hypotheses and not solely based on potentially irreproducible literature. Moreover, we note a relative dearth of resting-state literature, which could be used to better understand chemosensory networks with less influence from motion artifacts induced via gustatory or olfactory paradigms. Finally, we note a lack of genomics data, which could clarify individual and heritable differences in chemosensory perception.

A longitudinal multi-scanner multimodal human neuroimaging dataset

Human neuroimaging has led to an overwhelming amount of research into brain function in healthy and clinical populations. However, a better appreciation of the limitations of small sample studies has led to an increased number of multi-site, multi-scanner protocols to understand human brain function. As part of a multi-site project examining social cognition in schizophrenia, a group of "travelling human phantoms" had structural T1, diffusion, and resting-state functional MRIs obtained annually at each of three sites. Scan protocols were carefully harmonized across sites prior to the study. Due to scanner upgrades at each site (all sites acquired PRISMA MRIs during the study) and one participant being replaced, the end result was 30 MRI scans across 4 people, 6 MRIs, and 4 years. This dataset includes multiple neuroimaging modalities and repeated scans across six MRIs. It can be used to evaluate differences across scanners, consistency of pipeline outputs, or test multi-scanner harmonization approaches.

Future Directions for Chemosensory Connectomes: Best Practices and Specific Challenges

Ecological chemosensory stimuli almost always evoke responses in more than one sensory system. Moreover, any sensory processing takes place along a hierarchy of brain regions. So far, the field of chemosensory neuroimaging is dominated by studies that examine the role of brain regions in isolation. However, to completely understand neural processing of chemosensation, we must also examine interactions between regions. In general, the use of connectivity methods has increased in the neuroimaging field, providing important insights to physical sensory processing, such as vision, audition, and touch. A similar trend has been observed in chemosensory neuroimaging, however, these established techniques have largely not been rigorously applied to imaging studies on the chemical senses, leaving network insights overlooked. In this article, we first highlight some recent work in chemosensory connectomics and we summarize different connectomics techniques. Then, we outline specific challenges for chemosensory connectome neuroimaging studies. Finally, we review best practices from the general connectomics and neuroimaging fields. We recommend future studies to develop or use the following methods we perceive as key to improve chemosensory connectomics: (1) optimized study designs, (2) reporting guidelines, (3) consensus on brain parcellations, (4) consortium research, and (5) data sharing.

Evaluation of thresholding methods for activation likelihood estimation meta-analysis via large-scale simulations

In recent neuroimaging studies, threshold‐free cluster enhancement (TFCE) gained popularity as a sophisticated thresholding method for statistical inference. It was shown to feature higher sensitivity than the frequently used approach of controlling the cluster‐level family‐wise error (cFWE) and it does not require setting a cluster‐forming threshold at voxel level. Here, we examined the applicability of TFCE to a widely used method for coordinate‐based neuroimaging meta‐analysis, Activation Likelihood Estimation (ALE), by means of large‐scale simulations. We created over 200,000 artificial meta‐analysis datasets by independently varying the total number of experiments included and the amount of spatial convergence across experiments. Next, we applied ALE to all datasets and compared the performance of TFCE to both voxel‐level and cluster‐level FWE correction approaches. All three multiple‐comparison correction methods yielded valid results, with only about 5% of the significant clusters being based on spurious convergence, which corresponds to the nominal level the methods were controlling for. On average, TFCE's sensitivity was comparable to that of cFWE correction, but it was slightly worse for a subset of parameter combinations, even after TFCE parameter optimization. cFWE yielded the largest significant clusters, closely followed by TFCE, while voxel‐level FWE correction yielded substantially smaller clusters, showcasing its high spatial specificity. Given that TFCE does not outperform the standard cFWE correction but is computationally much more expensive, we conclude that employing TFCE for ALE cannot be recommended to the general user.

Theoretical false positive psychology

A fundamental goal of scientific research is to generate true positives (i.e., authentic discoveries). Statistically, a true positive is a significant finding for which the underlying effect size (δ) is greater than 0, whereas a false positive is a significant finding for which δ equals 0. However, the null hypothesis of no difference (δ = 0) may never be strictly true because innumerable nuisance factors can introduce small effects for theoretically uninteresting reasons. If δ never equals zero, then with sufficient power, every experiment would yield a significant result. Yet running studies with higher power by increasing sample size (N) is one of the most widely agreed upon reforms to increase replicability. Moreover, and perhaps not surprisingly, the idea that psychology should attach greater value to small effect sizes is gaining currency. Increasing N without limit makes sense for purely measurement-focused research, where the magnitude of δ itself is of interest, but it makes less sense for theory-focused research, where the truth status of the theory under investigation is of interest. Increasing power to enhance replicability will increase true positives at the level of the effect size (statistical true positives) while increasing false positives at the level of theory (theoretical false positives). With too much power, the cumulative foundation of psychological science would consist largely of nuisance effects masquerading as theoretically important discoveries. Positive predictive value at the level of theory is maximized by using an optimal N, one that is neither too small nor too large.

Identifying a brain network for musical rhythm: A functional neuroimaging meta-analysis and systematic review

We conducted a systematic review and meta-analysis of 30 functional magnetic resonance imaging studies investigating processing of musical rhythms in neurotypical adults. First, we identified a general network for musical rhythm, encompassing all relevant sensory and motor processes (Beat-based, rest baseline, 12 contrasts) which revealed a large network involving auditory and motor regions. This network included the bilateral superior temporal cortices, supplementary motor area (SMA), putamen, and cerebellum. Second, we identified more precise loci for beat-based musical rhythms (Beat-based, audio-motor control, 8 contrasts) in the bilateral putamen. Third, we identified regions modulated by beat based rhythmic complexity (Complexity, 16 contrasts) which included the bilateral SMA-proper/pre-SMA, cerebellum, inferior parietal regions, and right temporal areas. This meta-analysis suggests that musical rhythm is largely represented in a bilateral cortico-subcortical network. Our findings align with existing theoretical frameworks about auditory-motor coupling to a musical beat and provide a foundation for studying how the neural bases of musical rhythm may overlap with other cognitive domains.

Examining the neural correlates of error awareness in a large fMRI study

Goal-directed behavior is dependent upon the ability to detect errors and implement appropriate posterror adjustments. Accordingly, several studies have explored the neural activity underlying error-monitoring processes, identifying the insula cortex as crucial for error awareness and reporting mixed findings with respect to the anterior cingulate cortex (ACC). Variable patterns of activation have previously been attributed to insufficient statistical power. We therefore sought to clarify the neural correlates of error awareness in a large event-related functional magnetic resonance imaging (fMRI) study. Four hundred and two healthy participants undertook the error awareness task, a motor Go/No-Go response inhibition paradigm in which participants were required to indicate their awareness of commission errors. Compared to unaware errors, aware errors were accompanied by significantly greater activity in a network of regions, including the insula cortex, supramarginal gyrus (SMG), and midline structures, such as the ACC and supplementary motor area (SMA). Error awareness activity was related to indices of task performance and dimensional measures of psychopathology in selected regions, including the insula, SMG, and SMA. Taken together, we identified a robust and reliable neural network associated with error awareness.

A methodological checklist for fMRI drug cue reactivity studies: development and expert consensus

Cue reactivity is one of the most frequently used paradigms in functional magnetic resonance imaging (fMRI) studies of substance use disorders (SUDs). Although there have been promising results elucidating the neurocognitive mechanisms of SUDs and SUD treatments, the interpretability and reproducibility of these studies is limited by incomplete reporting of participants' characteristics, task design, craving assessment, scanning preparation and analysis decisions in fMRI drug cue reactivity (FDCR) experiments. This hampers clinical translation, not least because systematic review and meta-analysis of published work are difficult. This consensus paper and Delphi study aims to outline the important methodological aspects of FDCR research, present structured recommendations for more comprehensive methods reporting and review the FDCR literature to assess the reporting of items that are deemed important. Forty-five FDCR scientists from around the world participated in this study. First, an initial checklist of items deemed important in FDCR studies was developed by several members of the Enhanced NeuroImaging Genetics through Meta-Analyses (ENIGMA) Addiction working group on the basis of a systematic review. Using a modified Delphi consensus method, all experts were asked to comment on, revise or add items to the initial checklist, and then to rate the importance of each item in subsequent rounds. The reporting status of the items in the final checklist was investigated in 108 recently published FDCR studies identified through a systematic review. By the final round, 38 items reached the consensus threshold and were classified under seven major categories: 'Participants' Characteristics', 'General fMRI Information', 'General Task Information', 'Cue Information', 'Craving Assessment Inside Scanner', 'Craving Assessment Outside Scanner' and 'Pre- and Post-Scanning Considerations'. The review of the 108 FDCR papers revealed significant gaps in the reporting of the items considered important by the experts. For instance, whereas items in the 'General fMRI Information' category were reported in 90.5% of the reviewed papers, items in the 'Pre- and Post-Scanning Considerations' category were reported by only 44.7% of reviewed FDCR studies. Considering the notable and sometimes unexpected gaps in the reporting of items deemed to be important by experts in any FDCR study, the protocols could benefit from the adoption of reporting standards. This checklist, a living document to be updated as the field and its methods advance, can help improve experimental design, reporting and the widespread understanding of the FDCR protocols. This checklist can also provide a sample for developing consensus statements for protocols in other areas of task-based fMRI.

Detecting the Inverted-U in fMRI Studies of Schizophrenia: A Comparison of Three Analysis Methods

OBJECTIVE

Cognitive tasks are used to probe neuronal activity during functional magnetic resonance imaging (fMRI) to detect signs of aberrant cognitive functioning in patients diagnosed with schizophrenia (SZ). However, nonlinear (inverted-U-shaped) associations between neuronal activity and task difficulty can lead to misinterpretation of group differences between patients and healthy comparison subjects (HCs). In this paper, we evaluated a novel method for correcting these misinterpretations based on conditional performance analysis.

METHOD

Participants included 25 HCs and 27 SZs who performed a working memory (WM) task (N-back) with 5 load conditions while undergoing fMRI. Neuronal activity was regressed onto: 1) task load (i.e., parametric task levels), 2) marginal task performance (i.e., performance averaged over all load conditions), or 3) conditional task performance (i.e., performance within each load condition).

RESULTS

In most regions of interest, conditional performance analysis uniquely revealed inverted-U-shaped neuronal activity in both SZs and HCs. After accounting for conditional performance differences between groups, we observed few difference in both the pattern and level of neuronal activity between SZs and HCs within regions that are classically associated with WM functioning (e.g., posterior dorsolateral prefrontal and parietal association cortices). However, SZs did show aberrant activity within the anterior dorsolateral prefrontal cortex.

CONCLUSIONS

Interpretations of differences in neuronal activity between groups, and of associations between neuronal activity and performance, should be considered within the context of task performance. Whether conditional performance-based differences reflect compensation, dedifferentiation, or other processes is not a question that is easily resolved by examining activation and performance data alone.

Validation of a new diagnostic method for quantification of sleep bruxism activity

OBJECTIVES

To validate a new diagnostic method (DIABRUX) for quantifying sleep bruxism (SB) activity using the current gold standard, polysomnography (PSG), as a criterion in an adequate sample size investigation.

MATERIALS AND METHODS

For SB diagnosis, each participant received a two-night ambulatory PSG including audio-video recordings. The 0.5-mm-thick sheet is produced in a thermoforming process. After diagnosis via PSG, each subject wore the diagnostic sheet for five consecutive nights. The resulting total abrasion on the surface was automatically quantified in pixels by a software specially designed for this purpose.

RESULTS

Forty-five participants (10 SB and 35 non-SB subjects) were included. The difference of the mean pixel score between the SB (M = 1,306, SD = 913) and the non-SB group (M = 381, SD = 483; 3.4 times higher for SB) was statistically significant (p < 0.001). The receiver operator characteristic (ROC) analysis revealed a value of 507 pixels as the most appropriate cut-off criterion with a sensitivity of 1.0, a specificity to 0.8, and an area under the curve (AUC) of 0.88. The positive and negative predictive value accounted for 0.59 and 1.0.

CONCLUSIONS

The present data confirm that the new diagnostic method is valid and user-friendly that may be used for therapeutic evaluation, and for the acquisition of larger sample sizes within sophisticated study designs.

CLINICAL RELEVANCE

The verified properties of the new diagnostic method allow estimating SB activity before damages occur due to long-standing bruxism activity. Therefore, it might be utilized for preventive dentistry.

TRIAL REGISTRATION NUMBER

NC T03325920 (September 22, 2017).

Effect sizes and test-retest reliability of the fMRI-based neurologic pain signature

Identifying biomarkers that predict mental states with large effect sizes and high test-retest reliability is a growing priority for fMRI research. We examined a well-established multivariate brain measure that tracks pain induced by nociceptive input, the Neurologic Pain Signature (NPS). In N = 295 participants across eight studies, NPS responses showed a very large effect size in predicting within-person single-trial pain reports (d = 1.45) and medium effect size in predicting individual differences in pain reports (d = 0.49). The NPS showed excellent short-term (within-day) test-retest reliability (ICC = 0.84, with average 69.5 trials/person). Reliability scaled with the number of trials within-person, with ≥60 trials required for excellent test-retest reliability. Reliability was tested in two additional studies across 5-day (N = 29, ICC = 0.74, 30 trials/person) and 1-month (N = 40, ICC = 0.46, 5 trials/person) test-retest intervals. The combination of strong within-person correlations and only modest between-person correlations between the NPS and pain reports indicate that the two measures have different sources of between-person variance. The NPS is not a surrogate for individual differences in pain reports but can serve as a reliable measure of pain-related physiology and mechanistic target for interventions.

Testing the adolescent social reorientation model during self and other evaluation using hierarchical growth curve modeling with parcellated fMRI data

Adolescence is characterized as a period when relationships and experiences shift toward peers. The social reorientation model of adolescence posits this shift is driven by neurobiological changes that increase the salience of social information related to peer integration and acceptance. Although influential, this model has rarely been subjected to tests that could falsify it, or studied in longitudinal samples assessing within-person development. We focused on two phenomena that are highly salient and dynamic during adolescence—social status and self-perception—and examined longitudinal changes in neural responses during a self/other evaluation task. We expected status-related social information to uniquely increase across adolescence in social brain regions. Despite using hierarchical growth curve modeling with parcellated whole-brain data to increase power to detect developmental effects, we didn’t find evidence in support of this hypothesis. Social brain regions showed increased responsivity across adolescence, but this trajectory was not unique to status-related information. Additionally, brain regions associated with self-focused cognition showed heightened responses during self-evaluation in the transition to mid-adolescence, especially for status-related information. These results qualify existing models of adolescent social reorientation and highlight the multifaceted changes in self and social development that could be leveraged in novel ways to support adolescent health and well-being.

fMRI replicability during emotional scene viewing: Functional regions and sample size

Recent findings have questioned the replicability of functional magnetic resonance imaging (fMRI) in the study of affective processing, reporting low replicability of emotional enhancement during a face-matching task. However, poor replicability may instead reflect a lack of emotional engagement for face matching. In the current study, replicability of emotional enhancement was tested in a large (N = 160) sample when emotional engagement was assessed during pleasant, neutral, and unpleasant picture viewing, which reliably engages affective reactions in both the brain and the body. Replicability was computed using a subsampling technique, in which random sets of subjects of different sample sizes (N = 20, 40, 60, 80) were selected from the entire dataset, and replicability of emotional enhancement for peaks, clusters, and voxels were averaged across 500 permutations for each sample size. Consistent with previous findings, fMRI replicability increased with increasing sample size. On the other hand, even with relatively small samples, fMRI replicability for peaks, clusters, and voxels during emotional, compared to neutral, scene viewing was good to excellent. Importantly, replicability varied in different brain regions, with excellent replicability at both the cluster and peak level with an N of 40, at the most conservative threshold (p < .001), in the amygdala and the visual cortex. The data argue against general recommendations regarding sample size in fMRI studies of emotion, suggesting instead that degree of replicability depends on successful emotional engagement in task-related brain regions.

The C-BIG Repository: an Institution-Level Open Science Platform

In January 2016, the Montreal Neurological Institute-Hospital (The Neuro) declared itself an Open Science organization. This vision extends beyond efforts by individual scientists seeking to release individual datasets, software tools, or building platforms that provide for the free dissemination of such information. It involves multiple stakeholders and an infrastructure that considers governance, ethics, computational resourcing, physical design, workflows, training, education, and intra-institutional reporting structures. The C-BIG repository was built in response as The Neuro's institutional biospecimen and clinical data repository, and collects biospecimens as well as clinical, imaging, and genetic data from patients with neurological disease and healthy controls. It is aimed at helping scientific investigators, in both academia and industry, advance our understanding of neurological diseases and accelerate the development of treatments. As many neurological diseases are quite rare, they present several challenges to researchers due to their small patient populations. Overcoming these challenges required the aggregation of datasets from various projects and locations. The C-BIG repository achieves this goal and stands as a scalable working model for institutions to collect, track, curate, archive, and disseminate multimodal data from patients. In November 2020, a Registered Access layer was made available to the wider research community at https://cbigr-open.loris.ca , and in May 2021 fully open data will be released to complement the Registered Access data. This article outlines many of the aspects of The Neuro's transition to Open Science by describing the data to be released, C-BIG's full capabilities, and the design aspects that were implemented for effective data sharing.

Sex Differences in Opioid and Psychostimulant Craving and Relapse: A Critical Review

A widely held dogma in the preclinical addiction field is that females are more vulnerable than males to drug craving and relapse. Here, we first review clinical studies on sex differences in psychostimulant and opioid craving and relapse. Next, we review preclinical studies on sex differences in psychostimulant and opioid reinstatement of drug seeking after extinction of drug self-administration, and incubation of drug craving (time-dependent increase in drug seeking during abstinence). We also discuss ovarian hormones' role in relapse and craving in humans and animal models and speculate on brain mechanisms underlying their role in cocaine craving and relapse in rodent models. Finally, we discuss imaging studies on brain responses to cocaine cues and stress in men and women.The results of the clinical studies reviewed do not appear to support the notion that women are more vulnerable to psychostimulant and opioid craving and relapse. However, this conclusion is tentative because most of the studies reviewed were correlational, not sufficiently powered, and not a priori designed to detect sex differences. Additionally, imaging studies suggest sex differences in brain responses to cocaine cues and stress. The results of the preclinical studies reviewed provide evidence for sex differences in stress-induced reinstatement and incubation of cocaine craving but not cue- or cocaine-induced reinstatement of cocaine seeking. These sex differences are modulated in part by ovarian hormones. In contrast, the available data do not support the notion of sex differences in craving and relapse/reinstatement for methamphetamine or opioids in rodent models. SIGNIFICANCE STATEMENT: This systematic review summarizes clinical and preclinical studies on sex differences in psychostimulant and opioid craving and relapse. Results of the clinical studies reviewed do not appear to support the notion that women are more vulnerable to psychostimulant and opioid craving and relapse. Results of preclinical studies reviewed provide evidence for sex differences in reinstatement and incubation of cocaine seeking but not for reinstatement or incubation of methamphetamine or opioid seeking.

Functional near infrared spectroscopy activation during an executive function task differs between healthy older and younger adults

Background

Healthy aging can include declines in processing speed and executive function. Further research is needed to characterize the neurobiological underpinnings of these cognitive changes in older adulthood. The current study used functional near infrared spectroscopy (fNIRS), an optical neuroimaging technique, to examine differences in cerebral oxygenation between healthy older adults (OA) and younger adults (YA) during a measure of cognitive interference.

Methods

Thirty-four participants were sampled from two age groups: YA (mean age = 28.1 years, SD = 2.8, F = 9) and OA (mean age = 70.9 years, SD = 5.4, F = 9). Participants completed the Multi-Source Interference Task (MSIT), a measure of executive function with high and low-demand conditions, while undergoing fNIRS recordings using a TechEn CW6 system with 34-source-detector channels, situated over the prefrontal cortex. Functional activation patterns, accuracy, and reaction time were compared between and within groups for each condition.

Results

Behaviourally, during the control condition, OA and YA had comparable accuracy, although OA had significantly slower reaction times than YA. During the interference condition, OA had significantly lower accuracy and slower reaction times than YA. Results demonstrated a significant difference between groups with an age-related increase in HbO for OA in both conditions (p < 0.05). Within groups, OA showed greater activation during the control condition, while YA demonstrated greater activation during the interference condition.

Conclusions

The findings suggest that OA recruit additional neural resources to achieve similar behavioural performance during low-level cognitive interference, but that compensation in OA may be insufficient to support behavioural performance at higher levels of interference.

Detecting Suicide Ideation in the Era of Social Media: The Population Neuroscience Perspective

Social media platforms are increasingly used across many population groups not only to communicate and consume information, but also to express symptoms of psychological distress and suicidal thoughts. The detection of suicidal ideation (SI) can contribute to suicide prevention. Twitter data suggesting SI have been associated with negative emotions (e.g., shame, sadness) and a number of geographical and ecological variables (e.g., geographic location, environmental stress). Other important research contributions on SI come from studies in neuroscience. To date, very few research studies have been conducted that combine different disciplines (epidemiology, health geography, neurosciences, psychology, and social media big data science), to build innovative research directions on this topic. This article aims to offer a new interdisciplinary perspective, that is, a Population Neuroscience perspective on SI in order to highlight new ways in which multiple scientific fields interact to successfully investigate emotions and stress in social media to detect SI in the population. We argue that a Population Neuroscience perspective may help to better understand the mechanisms underpinning SI and to promote more effective strategies to prevent suicide timely and at scale.

Reward Processing in Children With Psychotic-Like Experiences

Alterations to striatal reward pathways have been identified in individuals with psychosis. They are hypothesized to be a key mechanism that generate psychotic symptoms through the production of aberrant attribution of motivational salience and are proposed to result from accumulated childhood adversity and genetic risk, making the striatal system hyper-responsive to stress. However, few studies have examined whether children with psychotic-like experiences (PLEs) also exhibit these alterations, limiting our understanding of how differences in reward processing relate to hallucinations and delusional ideation in childhood. Consequently, we examined whether PLEs and PLE-related distress were associated with reward-related activation in the nucleus accumbens (NAcc). The sample consisted of children (N = 6718) from the Adolescent Brain Cognitive Development (ABCD) study aged 9-10 years who had participated in the Monetary Incentive Delay (MID) task in functional MRI. We used robust mixed-effects linear regression models to investigate the relationship between PLEs and NAcc activation during the reward anticipation and reward outcome stages of the MID task. Analyses were adjusted for gender, household income, ethnicity, depressive symptoms, movement in the scanner, pubertal development, scanner ID, subject and family ID. There was no reliable association between PLEs and alterations to anticipation- or outcome-related striatal reward processing. We discuss the implications for developmental models of psychosis and suggest a developmental delay model of how PLEs may arise at this stage of development.

Magnetoencephalographic Correlates of Mood and Reward Dynamics in Human Adolescents

Despite its omnipresence in everyday interactions and its importance for mental health, mood and its neuronal underpinnings are poorly understood. Computational models can help identify parameters affecting self-reported mood during mood induction tasks. Here, we test if computationally modeled dynamics of self-reported mood during monetary gambling can be used to identify trial-by-trial variations in neuronal activity. To this end, we shifted mood in healthy (N = 24) and depressed (N = 30) adolescents by delivering individually tailored reward prediction errors while recording magnetoencephalography (MEG) data. Following a pre-registered analysis, we hypothesize that the expectation component of mood would be predictive of beta-gamma oscillatory power (25–40 Hz). We also hypothesize that trial variations in the source localized responses to reward feedback would be predicted by mood and by its reward prediction error component. Through our multilevel statistical analysis, we found confirmatory evidence that beta-gamma power is positively related to reward expectation during mood shifts, with localized sources in the posterior cingulate cortex. We also confirmed reward prediction error to be predictive of trial-level variations in the response of the paracentral lobule. To our knowledge, this is the first study to harness computational models of mood to relate mood fluctuations to variations in neural oscillations with MEG.

Brain responses to anticipatory cues and milkshake taste in obesity, and their relationship to bariatric surgery outcome

There is substantial variability in percent total weight loss (%TWL) following bariatric surgery. Functional brain imaging may explain more variance in post-surgical weight loss than psychological or metabolic information. Here we examined the neuronal responses during anticipatory cues and receipt of drops of milkshake in 52 pre-bariatric surgery men and women with severe obesity (OW, BMI = 35-60 kg/m2) (23 sleeve gastrectomy (SG), 24 Roux-en-Y gastric bypass (RYGB), 3 laparoscopic adjustable gastric banding (LAGB), 2 did not undergo surgery) and 21 healthy-weight (HW) controls (BMI = 19-27 kg/m2). One-year post-surgery weight loss ranged from 3.1 to 44.0 TWL%. Compared to HW, OW had a stronger response to milkshake cues (compared to water) in frontal and motor, somatosensory, occipital, and cerebellar regions. Responses to milkshake taste receipt (compared to water) differed from HW in frontal, motor, and supramarginal regions where OW showed more similar response to water. One year post-surgery, responses to high-fat milkshake cues normalized in frontal, motor, and somatosensory regions. This change in brain response was related to scores on a composite health index. We found no correlation between baseline response to milkshake cues or tastes and%TWL at 1-yr post-surgery. In RYGB participants only, a stronger response to low-fat milkshake and water cues (compared to high-fat) in supramarginal and cuneal regions respectively was associated with more weight loss. A stronger cerebellar response to high-fat vs low-fat milkshake receipt was also associated with more weight loss. We confirm differential responses to anticipatory milkshake cues in participants with severe obesity and HW in the largest adult cohort to date. Our brain wide results emphasizes the need to look beyond reward and cognitive control regions. Despite the lack of a correlation with post-surgical weight loss in the entire surgical group, participants who underwent RYGB showed predictive power in several regions and contrasts. Our findings may help in understanding the neuronal mechanisms associated with obesity.

Hyperbolic trade-off: The importance of balancing trial and subject sample sizes in neuroimaging

Here we investigate the crucial role of trials in task-based neuroimaging from the perspectives of statistical efficiency and condition-level generalizability. Big data initiatives have gained popularity for leveraging a large sample of subjects to study a wide range of effect magnitudes in the brain. On the other hand, most task-based FMRI designs feature a relatively small number of subjects, so that resulting parameter estimates may be associated with compromised precision. Nevertheless, little attention has been given to another important dimension of experimental design, which can equally boost a study's statistical efficiency: the trial sample size. The common practice of condition-level modeling implicitly assumes no cross-trial variability. Here, we systematically explore the different factors that impact effect uncertainty, drawing on evidence from hierarchical modeling, simulations and an FMRI dataset of 42 subjects who completed a large number of trials of cognitive control task. We find that, due to an approximately symmetic hyperbola-relationship between trial and subject sample sizes in the presence of relatively large cross-trial variability, 1) trial sample size has nearly the same impact as subject sample size on statistical efficiency; 2) increasing both the number of trials and subjects improves statistical efficiency more effectively than focusing on subjects alone; 3) trial sample size can be leveraged alongside subject sample size to improve the cost-effectiveness of an experimental design; 4) for small trial sample sizes, trial-level modeling, rather than condition-level modeling through summary statistics, may be necessary to accurately assess the standard error of an effect estimate. We close by making practical suggestions for improving experimental designs across neuroimaging and behavioral studies.