Striving toward translation: strategies for reliable fMRI measurement

Consistent frontal-limbic-occipital connections in distinguishing treatment-resistant and non-treatment-resistant schizophrenia

BACKGROUND AND HYPOTHESIS

Treatment-resistant schizophrenia (TR-SZ) and non-treatment-resistant schizophrenia (NTR-SZ) lack specific biomarkers to distinguish from each other. This investigation aims to identify consistent dysfunctional brain connections with different atlases, multiple feature selection strategies, and several classifiers in distinguishing TR-SZ and NTR-SZ.

STUDY DESIGN

55 TR-SZs, 239 NTR-SZs, and 87 healthy controls (HCs) were recruited from the Affiliated Brain Hospital of Nanjing Medical University. Resting-state functional connection (FC) matrices were constructed from automated anatomical labeling (AAL), Yeo-Networks (YEO) and Brainnetome (BNA) atlases. Two feature selection methods (Select From Model and Recursive Feature Elimination) and four classifiers (Adaptive Boost, Bernoulli Naïve Bayes, Gradient Boosting and Random Forest) were combined to identify the consistent FCs in distinguishing TR-SZ and HC, NTR-SZ and HC, TR-SZ and NTR-SZ.

STUDY RESULTS

The whole brain FCs, except the temporal-occipital FC, were consistent in distinguishing SZ and HC. Abnormal frontal-limbic, frontal-parietal and occipital-temporal FCs were consistent in distinguishing TR-SZ and NTR-SZ, that were further correlated with disease progression, symptoms and medication dosage. Moreover, the frontal-limbic and frontal-parietal FCs were highly consistent for the diagnosis of SZ (TR-SZ vs. HC, NTR-SZ vs. HC and TR-SZ vs. NTR-SZ). The BNA atlas achieved the highest classification accuracy (>90 %) comparing with AAL and YEO in the most diagnostic tasks.

CONCLUSIONS

These results indicate that the frontal-limbic and the frontal-parietal FCs are the robust neural pathways in the diagnosis of SZ, whereas the frontal-limbic, frontal-parietal and occipital-temporal FCs may be informative in recognizing those TR-SZ in the clinical practice.

Brain structural damage networks at different stages of schizophrenia

BACKGROUND

Neuroimaging studies have documented brain structural changes in schizophrenia at different stages of the illness, including clinical high-risk (cHR), genetic high-risk (gHR), first-episode schizophrenia (FES), and chronic schizophrenia (ChS). There is growing awareness that neuropathological processes associated with a disease fail to map to a specific brain region but do map to a specific brain network. We sought to investigate brain structural damage networks across different stages of schizophrenia.

METHODS

We initially identified gray matter alterations in 523 cHR, 855 gHR, 2162 FES, and 2640 ChS individuals relative to 6963 healthy controls. By applying novel functional connectivity network mapping to large-scale discovery and validation resting-state functional magnetic resonance imaging datasets, we mapped these affected brain locations to four specific networks.

RESULTS

Brain structural damage networks of cHR and gHR had limited and non-overlapping spatial distributions, with the former mainly involving the frontoparietal network and the latter principally implicating the subcortical network, indicative of distinct neuropathological mechanisms underlying cHR and gHR. By contrast, brain structural damage networks of FES and ChS manifested as similar patterns of widespread brain areas predominantly involving the somatomotor, ventral attention, and subcortical networks, suggesting an emergence of more prominent brain structural abnormalities with illness onset that have trait-like stability over time.

CONCLUSIONS

Our findings may not only provide a refined picture of schizophrenia neuropathology from a network perspective, but also potentially contribute to more targeted and effective intervention strategies for individuals at different schizophrenia stages.

An Honest Reckoning With the Amygdala and Mental Illness

Anxiety disorders are a leading source of human misery, morbidity, and premature mortality. Existing treatments are far from curative for many, underscoring the need to clarify the underlying neural mechanisms. Although many brain regions contribute, the amygdala has received the most intense scientific attention. Over the past several decades, this scrutiny has yielded a detailed understanding of amygdala function, but it has failed to produce new clinical assays, biomarkers, or cures. Rising to this urgent public health challenge demands an honest reckoning with the functional-neuroanatomical complexity of the amygdala and a shift from theories anchored on "the amygdala" to models centered on specific amygdala nuclei and cell types. This review begins by examining evidence from studies of rodents, monkeys, and humans for the "canonical model," the idea that the amygdala plays a central role in fear- and anxiety-related states, traits, and disorders. Next, the authors selectively highlight work indicating that the canonical model, while true, is overly simplistic and fails to adequately capture the actual state of the evidentiary record, the breadth of amygdala-associated functions and illnesses, or the complexity of the amygdala's functional architecture. The authors describe the implications of these facts for basic and clinical neuroimaging research. The review concludes with some general recommendations for grappling with the complexity of the amygdala and accelerating efforts to understand and more effectively treat amygdala-related psychopathology.

Resting-State Functional Connectivity of the Amygdala in Autism: A Preregistered Large-Scale Study

OBJECTIVE

Three leading neurobiological hypotheses about autism spectrum disorder (ASD) propose underconnectivity between brain regions, atypical function of the amygdala, and generally higher variability between individuals with ASD than between neurotypical individuals. Past work has often failed to generalize, because of small sample sizes, unquantified data quality, and analytic flexibility. This study addressed these limitations while testing the above three hypotheses, applied to amygdala functional connectivity.

METHODS

In a comprehensive preregistered study, the three hypotheses were tested in a subset (N=488 after exclusions; N=212 with ASD) of the Autism Brain Imaging Data Exchange data sets. The authors analyzed resting-state functional connectivity (FC) from functional MRI data from two anatomically defined amygdala subdivisions, in three hypotheses with respect to magnitude, pattern similarity, and variability, across different anatomical scales ranging from whole brain to specific regions and networks.

RESULTS

A Bayesian approach to hypothesis evaluation produced inconsistent evidence in ASD for atypical amygdala FC magnitude, strong evidence that the multivariate pattern of FC was typical, and no consistent evidence of increased interindividual variability in FC. The results strongly depended on analytic choices, including preprocessing pipeline for the neuroimaging data, anatomical specificity, and subject exclusions.

CONCLUSIONS

A preregistered set of analyses found no reliable evidence for atypical functional connectivity of the amygdala in autism, contrary to leading hypotheses. Future studies should test an expanded set of hypotheses across multiple processing pipelines, collect deeper data per individual, and include a greater diversity of participants to ensure robust generalizability of findings on amygdala FC in ASD.

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

Brain-wide association studies (BWASs) have attempted to relate cognitive abilities with brain phenotypes, but have been challenged by issues such as predictability, test-retest reliability, and cross-cohort generalisability. To tackle these challenges, we proposed a machine-learning "stacking" approach that draws information from whole-brain magnetic resonance imaging (MRI) across different modalities, from task-fMRI contrasts and functional connectivity during tasks and rest to structural measures, into one prediction model. We benchmarked the benefits of stacking, using the Human Connectome Projects: Young Adults (n=873, 22-35 years old) and Human Connectome Projects-Aging (n=504, 35-100 years old) and the Dunedin Multidisciplinary Health and Development Study (Dunedin Study, n=754, 45 years old). For predictability, stacked models led to out-of-sample r~.5-.6 when predicting cognitive abilities at the time of scanning, primarily driven by task-fMRI contrasts. Notably, using the Dunedin Study, we were able to predict participants' cognitive abilities at ages 7, 9, and 11 using their multimodal MRI at age 45, with an out-of-sample r of 0.52. For test-retest reliability, stacked models reached an excellent level of reliability (ICC>.75), even when we stacked only task-fMRI contrasts together. For generalisability, a stacked model with non-task MRI built from one dataset significantly predicted cognitive abilities in other datasets. Altogether, stacking is a viable approach to undertake the three challenges of BWAS for cognitive abilities.

Quantification choices for individual differences: An example of mapping self-report to psychophysiological responses

A popular focus in affective neuroscience research has been to map the relationships between individual differences (e.g. personality and environmental experiences) and psychophysiological responses, in order to further understand the effect of individual differences upon neurobehavioral systems that support affect and arousal. Despite this trend, there have been a lack of practical examples demonstrating how the quantification of individual differences (e.g. categorical or continuous) impacts the observed relationships between different units of analysis (e.g. self-report > psychophysiological responses). To address this gap, we conducted a two-stage aggregated meta-analysis of self-reported intolerance of uncertainty (IU) and skin conductance responses during threat extinction (k = 18, n = 1006) using different quantification choices for individual differences in self-reported intolerance of uncertainty (continuous, categorical via median split, and categorical via extremes - one standard deviation above/below). Results from the meta-analyses revealed that the different quantification techniques produced some consistent (e.g. higher IU was significantly associated with skin conductance responding during late extinction training) and inconsistent IU-related effects. Furthermore, the number of statistically significant effects and effect sizes varied based on the quantification of individual differences in IU (e.g. categorical, compared to continuous was associated with more statistically significant effects, and larger effect sizes). The current study highlights how conducting different quantification methods for individual differences may help researchers understand the individual difference construct of interest (e.g. characterisation, measurement), as well as examine the stability and reliability of individual difference-based effects and correspondence between various units of analysis.

Spatiotemporal integration of contextual and sensory information within the cortical hierarchy in human pain experience

Pain is not a mere reflection of noxious input. Rather, it is constructed through the dynamic integration of current predictions with incoming sensory input. However, the temporal dynamics of the behavioral and neural processes underpinning this integration remain elusive. In the current study involving 59 human participants, we identified a series of brain mediators that integrated cue-induced expectations with noxious inputs into ongoing pain predictions using a semicircular scale designed to capture rating trajectories. Temporal mediation analysis revealed that during the early-to-mid stages of integration, the frontoparietal and dorsal attention network regions, such as the lateral prefrontal, premotor, and parietal cortex, mediated the cue effects. Conversely, during the mid-to-late stages of integration, the somatomotor network regions mediated the effects of stimulus intensity, suggesting that the integration occurs along the cortical hierarchy from the association to sensorimotor brain systems. Our findings advance the understanding of how the brain integrates contextual and sensory information into pain experience over time.

Using precision approaches to improve brain-behavior prediction

Predicting individual behavioral traits from brain idiosyncrasies has broad practical implications, yet predictions vary widely. This constraint may be driven by a combination of signal and noise in both brain and behavioral variables. Here, we expand on this idea, highlighting the potential of extended sampling 'precision' studies. First, we discuss their relevance to improving the reliability of individualized estimates by minimizing measurement noise. Second, we review how targeted within-subject experiments, when combined with individualized analysis or modeling frameworks, can maximize signal. These improvements in signal-to-noise facilitated by precision designs can help boost prediction studies. We close by discussing the integration of precision approaches with large-sample consortia studies to leverage the advantages of both.

Integrating dynamic psychophysiological indices across time and contexts: Elucidating mechanisms, risk markers, and intervention targets

Why should researchers measure psychophysiological processes repeatedly over time? The study of psychophysiology inherently involves sampling biological processes as they manifest over time. The most common approach is to use a brief sample to make conclusions about how individuals or groups differ. Although these types of between-subject comparisons have utility for understanding individual and group differences, many of the important conceptual questions in the field involve processes that are dynamic, varying within individuals over time. Using examples from the literature on affect regulation, this conceptual review contrasts three types of study designs: the classic single-observation design and the aggregated and temporally linked repeated observation designs, which have great promise for measuring variables that fluctuate dynamically over time. Importantly, these designs can be integrated to elucidate research questions about risk (when and for whom will the likelihood of an unwanted outcome occurring increase?), mechanisms (how and why does a change in psychophysiology contribute to a change in another process of interest?), and interventions (how and when should interventions take place to modify an outcome?). Researchers are encouraged to implement intensive sampling in their research, which can be conducted in traditional laboratory settings (e.g., fMRI, event-related brain potentials, and heart rate variability) and in ecologically valid contexts in everyday life using ambulatory assessment.

Individual Structural Covariance Network Predicts Long-Term Motor Improvement in Parkinson Disease with Subthalamic Nucleus Deep Brain Stimulation

BACKGROUND AND PURPOSE

The efficacy of long-term chronic subthalamic nucleus deep brain stimulation (STN-DBS) in treating Parkinson disease (PD) exhibits substantial variability among individuals. The preoperative identification of suitable deep brain stimulation (DBS) candidates through predictive means becomes crucial. Our study aims to investigate the predictive value of characterizing individualized structural covariance networks for long-term efficacy of DBS, offering patients a precise and cost-effective preoperative screening tool.

MATERIALS AND METHODS

We included 138 patients with PD and 40 healthy controls. We developed individualized structural covariance networks from T1-weighted images utilizing network template perturbation, and computed the networks' topological characteristics. Patients were categorized according to their long-term motor improvement following STN-DBS. Intergroup analyses were conducted on individual network edges and topological indices, alongside correlation analyses with long-term outcomes for the entire patient cohort. Finally, machine learning algorithms were employed for regression and classification to predict post-DBS motor improvement.

RESULTS

Among the patients with PD, 6 edges (left middle frontal and left caudate nucleus, right olfactory and right insula, left superior medial frontal gyrus and right insula, right middle frontal and left paracentral lobule, right middle frontal and cerebellum, left lobule VIIb of the cerebellum and the vermis of the cerebellum) exhibited significant results in intergroup comparisons and correlation analyses. Increased degree centrality and local efficiency of the cerebellum, parahippocampal gyrus, and postcentral gyrus were associated with DBS improvement. A regression model constructed from these 6 edges revealed a significant correlation between predicted and observed changes in the unified PD rating scale (R = 0.671, P < .001) and receiver operating characteristic analysis demonstrated an area under the curve of 0.802, effectively distinguishing between patients with good and moderate improvement post-DBS.

CONCLUSIONS

Our findings reveal the link between individual structural covariance network fingerprints in patients with PD and long-term motor outcome following STN-DBS. Additionally, binary and continuous cerebellum-basal ganglia-frontal structural covariance network edges have emerged as potential predictive biomarkers for DBS motor outcome.

Neural basis underlying the association between thought control ability and happiness: The moderating role of the amygdala

Thought control ability (TCA) plays an important role in individuals' health and happiness. Previous studies demonstrated that TCA was closely conceptually associated with happiness. However, empirical research supporting this relationship was limited. In addition, the neural basis underlying TCA and how this neural basis influences the relationship between TCA and happiness remain unexplored. In the present study, the voxel-based morphometry (VBM) method was adopted to investigate the neuroanatomical basis of TCA in 314 healthy subjects. The behavioral results revealed a significant positive association between TCA and happiness. On the neural level, there was a significant negative correlation between TCA and the gray matter density (GMD) of the bilateral amygdala. Split-half validation analysis revealed similar results, further confirming the stability of the VBM analysis findings. Furthermore, gray matter covariance network and graph theoretical analyses showed positive association between TCA and both the node degree and node strength of the amygdala. Moderation analysis revealed that the GMD of the amygdala moderated the relationship between TCA and happiness. Specifically, the positive association between TCA and self-perceived happiness was stronger in subjects with a lower GMD of the amygdala. The present study indicated the neural basis underlying the association between TCA and happiness and offered a method of improving individual well-being.

Test-retest reliability of the attention network test from the perspective of intrinsic network organization

The attention network test (ANT), developed based on the triple-network taxonomy by Posner and colleagues, has been widely used to examine the efficacy of alerting, orienting and executive control in clinical and developmental neuroscience studies. Recent research suggests the imperfect reliability of the behavioural ANT and its variants. However, the classical ANT fMRI task's test-retest reliability has received little attention. Moreover, it remains ambiguous whether the attention-related intrinsic network components, especially the dorsal attention, ventral attention and frontoparietal network, manifest acceptable reliability. The present study approaches these issues by utilizing an openly available ANT fMRI dataset for participants with Parkinson's disease and healthy elderly. The reproducibility of group-level activations across sessions and participant groups and the test-retest reliability at the individual level were examined at the voxel, region and network levels. The intrinsic network was defined using the Yeo-Schaefer atlas. Our results reveal three critical facets: (1) the overlapping of the group-level contrast map between sessions and between participant groups was unsatisfactory; (2) the reliability of alerting, orienting and executive, defined as a contrast between conditions, was worse than estimates of specific conditions. (3) Dorsal attention, ventral attention, visual and somatomotor networks showed acceptable reliability for the congruent and incongruent conditions. Our results suggest that specific condition estimates might be used instead of the contrast map for individual or group-difference studies.

Reliability of brain metrics derived from a Time-Domain Functional Near-Infrared Spectroscopy System

With the growing interest in establishing brain-based biomarkers for precision medicine, there is a need for noninvasive, scalable neuroimaging devices that yield valid and reliable metrics. Kernel's second-generation Flow2 Time-Domain Functional Near-Infrared Spectroscopy (TD-fNIRS) system meets the requirements of noninvasive and scalable neuroimaging, and uses a validated modality to measure brain function. In this work, we investigate the test-retest reliability (TRR) of a set of metrics derived from the Flow2 recordings. We adopted a repeated-measures design with 49 healthy participants, and quantified TRR over multiple time points and different headsets-in different experimental conditions including a resting state, a sensory, and a cognitive task. Results demonstrated high reliability in resting state features including hemoglobin concentrations, head tissue light attenuation, amplitude of low frequency fluctuations, and functional connectivity. Additionally, passive auditory and Go/No-Go inhibitory control tasks each exhibited similar activation patterns across days. Notably, areas with the highest reliability were in auditory regions during the auditory task, and right prefrontal regions during the Go/No-Go task, consistent with prior literature. This study underscores the reliability of Flow2-derived metrics, supporting its potential to actualize the vision of using brain-based biomarkers for diagnosis, treatment selection and treatment monitoring of neuropsychiatric and neurocognitive disorders.

The mediating effect of the amygdala-frontal circuit on the association between depressive symptoms and cognitive function in Alzheimer's disease

Depressive symptoms occur commonly in Alzheimer's disease (AD). Although abnormalities in the amygdala-frontal circuit have been linked to emotional dysregulation and cognitive impairment, the neurological basis underlying these associations in AD patients with depressive symptoms (ADD) is unclear. We aimed to investigate the relationship between the amygdala-frontal circuit and depressive symptoms and cognitive function in ADD. We recruited 60 ADD, 60 AD patients without depressive symptoms (ADND), and 60 healthy controls (HC). Functional connectivity (FC) maps of the bilateral amygdala were compared. Fractional anisotropy (FA) of the amygdala-frontal circuit connected by the uncinate fasciculus (UF) was calculated using automated fiber quantification (AFQ). In addition, mediation analysis was performed to explore the effects of the amygdala-frontal circuit on the relationship between depressive symptoms and cognitive function. We found decreased bilateral amygdala FC with the inferior frontal gyrus (IFG) in the ADD group compared to the ADND and HC groups. Moreover, FA in the left frontal UF (nodes 64-97) was significantly lower in the ADD group than ADND group. Notably, amygdala-based FC with IFG and the left frontal UF FA mediated the relationship between depressive symptoms and cognitive function in ADD, with mediating effects ranging between 15 and 18%. Our study is the first to demonstrate the mediating effect of functional and microstructural abnormalities in the amygdala-frontal circuit in ADD. The findings suggest that the amygdala-frontal circuit may underlie emotional dysregulation in ADD, providing potential targets for treatment strategies.

Reliability of variability and complexity measures for task and task-free BOLD fMRI

Brain activity continuously fluctuates over time, even if the brain is in controlled (e.g., experimentally induced) states. Recent years have seen an increasing interest in understanding the complexity of these temporal variations, for example with respect to developmental changes in brain function or between-person differences in healthy and clinical populations. However, the psychometric reliability of brain signal variability and complexity measures-which is an important precondition for robust individual differences as well as longitudinal research-is not yet sufficiently studied. We examined reliability (split-half correlations) and test-retest correlations for task-free (resting-state) BOLD fMRI as well as split-half correlations for seven functional task data sets from the Human Connectome Project to evaluate their reliability. We observed good to excellent split-half reliability for temporal variability measures derived from rest and task fMRI activation time series (standard deviation, mean absolute successive difference, mean squared successive difference), and moderate test-retest correlations for the same variability measures under rest conditions. Brain signal complexity estimates (several entropy and dimensionality measures) showed moderate to good reliabilities under both, rest and task activation conditions. We calculated the same measures also for time-resolved (dynamic) functional connectivity time series and observed moderate to good reliabilities for variability measures, but poor reliabilities for complexity measures derived from functional connectivity time series. Global (i.e., mean across cortical regions) measures tended to show higher reliability than region-specific variability or complexity estimates. Larger subcortical regions showed similar reliability as cortical regions, but small regions showed lower reliability, especially for complexity measures. Lastly, we also show that reliability scores are only minorly dependent on differences in scan length and replicate our results across different parcellation and denoising strategies. These results suggest that the variability and complexity of BOLD activation time series are robust measures well-suited for individual differences research. Temporal variability of global functional connectivity over time provides an important novel approach to robustly quantifying the dynamics of brain function. PRACTITIONER POINTS: Variability and complexity measures of BOLD activation show good split-half reliability and moderate test-retest reliability. Measures of variability of global functional connectivity over time can robustly quantify neural dynamics. Length of fMRI data has only a minor effect on reliability.

Development of human visual cortical function: A scoping review of task- and naturalistic-fMRI studies through the interactive specialization and maturational frameworks

Overarching theories such as the interactive specialization and maturational frameworks have been proposed to describe human functional brain development. However, these frameworks have not yet been systematically examined across the fMRI literature. Visual processing is one of the most well-studied fields in neuroimaging, and research in this area has recently expanded to include naturalistic paradigms that facilitate study in younger age ranges, allowing for an in-depth critical appraisal of these frameworks across childhood. To this end, we conducted a scoping review of 94 developmental visual fMRI studies, including both traditional experimental task and naturalistic studies, across multiple sub-domains (early visual processing, category-specific higher order processing, naturalistic visual processing). We found that across domains, many studies reported progressive development, but few studies describe regressive or emergent changes necessary to fit the maturational or interactive specialization frameworks. Our findings suggest a need for the expansion of developmental frameworks and clearer reporting of both progressive and regressive changes, along with well-powered, longitudinal studies.

Reliability of task-based fMRI in the dorsal horn of the human spinal cord

The application of functional magnetic resonance imaging (fMRI) to the human spinal cord is still a relatively small field of research and faces many challenges. Here we aimed to probe the limitations of task-based spinal fMRI at 3T by investigating the reliability of spinal cord blood oxygen level dependent (BOLD) responses to repeated nociceptive stimulation across two consecutive days in 40 healthy volunteers. We assessed the test-retest reliability of subjective ratings, autonomic responses, and spinal cord BOLD responses to short heat pain stimuli (1s duration) using the intraclass correlation coefficient (ICC). At the group level, we observed robust autonomic responses as well as spatially specific spinal cord BOLD responses at the expected location, but no spatial overlap in BOLD response patterns across days. While autonomic indicators of pain processing showed good-to-excellent reliability, both β-estimates and z-scores of task-related BOLD responses showed poor reliability across days in the target region (gray matter of the ipsilateral dorsal horn). When taking into account the sensitivity of gradient-echo echo planar imaging (GE-EPI) to draining vein signals by including the venous plexus in the analysis, we observed BOLD responses with fair reliability across days. Taken together, these results demonstrate that heat pain stimuli as short as one second are able to evoke a robust and spatially specific BOLD response, which is however strongly variable within participants across time, resulting in low reliability in the dorsal horn gray matter. Further improvements in data acquisition and analysis techniques are thus necessary before event-related spinal cord fMRI as used here can be reliably employed in longitudinal designs or clinical settings.

Better together: A systematic review of studies combining magnetic resonance imaging with ecological momentary assessment

Social neuroscientists often use magnetic resonance imaging (MRI) to understand the relationship between social experiences and their neural substrates. Although MRI is a powerful method, it has several limitations in the study of social experiences, first and foremost its low ecological validity. To address this limitation, researchers have conducted multimethod studies combining MRI with Ecological Momentary Assessment (EMA). However, there are no existing recommendations for best practices for conducting and reporting such studies. To address the absence of standards in the field, we conducted a systematic review of papers that combined the methods. A systematic search of peer-reviewed papers resulted in a pool of 11,558 articles. Inclusion criteria were studies in which participants completed (a) Structural or functional MRI and (b) an EMA protocol that included self-report. Seventy-one papers met inclusion criteria. The following review compares these studies based on several key parameters (e.g., sample size) with the aim of determining feasibility and current standards for design and reporting in the field. The review concludes with recommendations for future research. A special focus is given to the ways in which the two methods were combined analytically and suggestions for novel computational methods that could further advance the field of social neuroscience.

Cross-cohort replicable resting-state functional connectivity in predicting symptoms and cognition of schizophrenia

Schizophrenia (SZ) is a debilitating mental illness characterized by adolescence or early adulthood onset of psychosis, positive and negative symptoms, as well as cognitive impairments. Despite a plethora of studies leveraging functional connectivity (FC) from functional magnetic resonance imaging (fMRI) to predict symptoms and cognitive impairments of SZ, the findings have exhibited great heterogeneity. We aimed to identify congruous and replicable connectivity patterns capable of predicting positive and negative symptoms as well as cognitive impairments in SZ. Predictable functional connections (FCs) were identified by employing an individualized prediction model, whose replicability was further evaluated across three independent cohorts (BSNIP, SZ = 174; COBRE, SZ = 100; FBIRN, SZ = 161). Across cohorts, we observed that altered FCs in frontal-temporal-cingulate-thalamic network were replicable in prediction of positive symptoms, while sensorimotor network was predictive of negative symptoms. Temporal-parahippocampal network was consistently identified to be associated with reduced cognitive function. These replicable 23 FCs effectively distinguished SZ from healthy controls (HC) across three cohorts (82.7%, 90.2%, and 86.1%). Furthermore, models built using these replicable FCs showed comparable accuracies to those built using the whole-brain features in predicting symptoms/cognition of SZ across the three cohorts (r = .17-.33, p < .05). Overall, our findings provide new insights into the neural underpinnings of SZ symptoms/cognition and offer potential targets for further research and possible clinical interventions.

An advance in novel intelligent sensory technologies: From an implicit-tracking perspective of food perception

Food sensory evaluation mainly includes explicit and implicit measurement methods. Implicit measures of consumer perception are gaining significant attention in food sensory and consumer science as they provide effective, subconscious, objective analysis. A wide range of advanced technologies are now available for analyzing physiological and psychological responses, including facial analysis technology, neuroimaging technology, autonomic nervous system technology, and behavioral pattern measurement. However, researchers in the food field often lack systematic knowledge of these multidisciplinary technologies and struggle with interpreting their results. In order to bridge this gap, this review systematically describes the principles and highlights the applications in food sensory and consumer science of facial analysis technologies such as eye tracking, facial electromyography, and automatic facial expression analysis, as well as neuroimaging technologies like electroencephalography, magnetoencephalography, functional magnetic resonance imaging, and functional near-infrared spectroscopy. Furthermore, we critically compare and discuss these advanced implicit techniques in the context of food sensory research and then accordingly propose prospects. Ultimately, we conclude that implicit measures should be complemented by traditional explicit measures to capture responses beyond preference. Facial analysis technologies offer a more objective reflection of sensory perception and attitudes toward food, whereas neuroimaging techniques provide valuable insight into the implicit physiological responses during food consumption. To enhance the interpretability and generalizability of implicit measurement results, further sensory studies are needed. Looking ahead, the combination of different methodological techniques in real-life situations holds promise for consumer sensory science in the field of food research.

fNIRS a novel neuroimaging tool to investigate olfaction, olfactory imagery, and crossmodal interactions: a systematic review

Olfaction is understudied in neuroimaging research compared to other senses, but there is growing evidence of its therapeutic benefits on mood and well-being. Olfactory imagery can provide similar health benefits as olfactory interventions. Harnessing crossmodal visual-olfactory interactions can facilitate olfactory imagery. Understanding and employing these cross-modal interactions between visual and olfactory stimuli could aid in the research and applications of olfaction and olfactory imagery interventions for health and wellbeing. This review examines current knowledge, debates, and research on olfaction, olfactive imagery, and crossmodal visual-olfactory integration. A total of 56 papers, identified using the PRISMA method, were evaluated to identify key brain regions, research themes and methods used to determine the suitability of fNIRS as a tool for studying these topics. The review identified fNIRS-compatible protocols and brain regions within the fNIRS recording depth of approximately 1.5 cm associated with olfactory imagery and crossmodal visual-olfactory integration. Commonly cited regions include the orbitofrontal cortex, inferior frontal gyrus and dorsolateral prefrontal cortex. The findings of this review indicate that fNIRS would be a suitable tool for research into these processes. Additionally, fNIRS suitability for use in naturalistic settings may lead to the development of new research approaches with greater ecological validity compared to existing neuroimaging techniques.

Multimodal measures of spontaneous brain activity reveal both common and divergent patterns of cortical functional organization

Large-scale functional networks have been characterized in both rodent and human brains, typically by analyzing fMRI-BOLD signals. However, the relationship between fMRI-BOLD and underlying neural activity is complex and incompletely understood, which poses challenges to interpreting network organization obtained using this technique. Additionally, most work has assumed a disjoint functional network organization (i.e., brain regions belong to one and only one network). Here, we employ wide-field Ca2+ imaging simultaneously with fMRI-BOLD in mice expressing GCaMP6f in excitatory neurons. We determine cortical networks discovered by each modality using a mixed-membership algorithm to test the hypothesis that functional networks exhibit overlapping organization. We find that there is considerable network overlap (both modalities) in addition to disjoint organization. Our results show that multiple BOLD networks are detected via Ca2+ signals, and networks determined by low-frequency Ca2+ signals are only modestly more similar to BOLD networks. In addition, the principal gradient of functional connectivity is nearly identical for BOLD and Ca2+ signals. Despite similarities, important differences are also detected across modalities, such as in measures of functional connectivity strength and diversity. In conclusion, Ca2+ imaging uncovers overlapping functional cortical organization in the mouse that reflects several, but not all, properties observed with fMRI-BOLD signals.

Efficient functional localization of language regions in the brain

Important recent advances in the cognitive neuroscience of language have been made using functional localizers to demarcate language-selective regions in individual brains. Although single-subject localizers offer insights that are unavailable in classic group analyses, they require additional scan time that imposes costs on investigators and participants. In particular, the unique practical challenges of scanning children and other special populations has led to less adoption of localizers for neuroimaging research with these theoretically and clinically important groups. Here, we examined how measurements of the spatial extent and functional response profiles of language regions are affected by the duration of an auditory language localizer. We compared how parametrically smaller amounts of data collected from one scanning session affected (i) consistency of group-level whole-brain parcellations, (ii) functional selectivity of subject-level activation in individually defined functional regions of interest (fROIs), (iii) sensitivity and specificity of subject-level whole-brain and fROI activation, and (iv) test-retest reliability of subject-level whole-brain and fROI activation. For many of these metrics, the localizer duration could be reduced by 50-75% while preserving the stability and reliability of both the spatial extent and functional response profiles of language areas. These results indicate that, for most measures relevant to cognitive neuroimaging studies, the brain's language network can be localized just as effectively with 3.5 min of scan time as it can with 12 min. Minimizing the time required to reliably localize the brain's language network allows more effective localizer use in situations where each minute of scan time is particularly precious.

A systematic review of childhood maltreatment and resting state functional connectivity

Resting-state functional connectivity (rsFC) has the potential to shed light on how childhood abuse and neglect relates to negative psychiatric outcomes. However, a comprehensive review of the impact of childhood maltreatment on the brain's resting state functional organization has not yet been undertaken. We systematically searched rsFC studies in children and youth exposed to maltreatment. Nineteen studies (total n = 3079) met our inclusion criteria. Two consistent findings were observed. Childhood maltreatment was linked to reduced connectivity between the anterior insula and dorsal anterior cingulate cortex, and with widespread heightened amygdala connectivity with key structures in the salience, default mode, and prefrontal regulatory networks. Other brain regions showing altered connectivity included the ventral anterior cingulate cortex, dorsolateral prefrontal cortex, and hippocampus. These patterns of altered functional connectivity associated with maltreatment exposure were independent of symptoms, yet comparable to those seen in individuals with overt clinical disorder. Summative findings indicate that rsFC alterations associated with maltreatment experience are related to poor cognitive and social functioning and are prognostic of future symptoms. In conclusion, maltreatment is associated with altered rsFC in emotional reactivity, regulation, learning, and salience detection brain circuits. This indicates patterns of recalibration of putative mechanisms implicated in maladaptive developmental outcomes.

Neurocomputational Substrates Underlying the Effect of Identifiability on Third-Party Punishment

The identifiable target effect refers to the preference for helping identified victims and punishing identifiable perpetrators compared with equivalent but unidentifiable counterparts. The identifiable target effect is often attributed to the heightened moral emotions evoked by identified targets. However, the specific neurocognitive processes that mediate and/or modulate this effect remain largely unknown. Here, we combined a third-party punishment game with brain imaging and computational modeling to unravel the neurocomputational underpinnings of the identifiable transgressor effect. Human participants (males and females) acted as bystanders and punished identified or anonymous wrongdoers. Participants were more punitive toward identified wrongdoers than anonymous wrongdoers because they took a vicarious perspective of victims and adopted lower reference points of inequity (i.e., more stringent norms) in the identified context than in the unidentified context. Accordingly, there were larger activity of the ventral anterior insula, more distinct multivariate neural patterns in the dorsal anterior insula and dorsal anterior cingulate cortex, and lower strength between ventral anterior insula and dorsolateral PFC and between dorsal anterior insula and ventral striatum connectivity in response to identified transgressors than anonymous transgressors. These findings implicate the interplay of expectancy violations, emotions, and self-interest in the identifiability effect. Last, individual differences in the identifiability effect were associated with empathic concern/social dominance orientation, activity in the precuneus/cuneus and temporo-parietal junction, and intrinsic functional connectivity of the dorsolateral PFC. Together, our work is the first to uncover the neurocomputational processes mediating identifiable transgressor effect and to characterize psychophysiological profiles modulating the effect.SIGNIFICANCE STATEMENT The identifiable target effect, more help to identified victims or stronger punishment to identifiable perpetrators, is common in daily life. We examined the neurocomputational mechanisms mediating/modulating the identifiability effect on third-party punishment by bridging literature from economics and cognitive neuroscience. Our findings reveal that identifiable transgressor effect is mediated by lower reference points of inequity (i.e., more stringent norms), which might be associated with a stronger involvement of the emotion processes and a weaker engagement of the analytic/deliberate processes. Furthermore, personality traits, altered brain activity, and intrinsic functional connectivity contribute to the individual variance in the identifiability effect. Overall, our study advances the understanding of the identifiability effect by shedding light on its component processes and modulating factors.

Optimizing network neuroscience computation of individual differences in human spontaneous brain activity for test-retest reliability

A rapidly emerging application of network neuroscience in neuroimaging studies has provided useful tools to understand individual differences in intrinsic brain function by mapping spontaneous brain activity, namely intrinsic functional network neuroscience (ifNN). However, the variability of methodologies applied across the ifNN studies-with respect to node definition, edge construction, and graph measurements-makes it difficult to directly compare findings and also challenging for end users to select the optimal strategies for mapping individual differences in brain networks. Here, we aim to provide a benchmark for best ifNN practices by systematically comparing the measurement reliability of individual differences under different ifNN analytical strategies using the test-retest design of the Human Connectome Project. The results uncovered four essential principles to guide ifNN studies: (1) use a whole brain parcellation to define network nodes, including subcortical and cerebellar regions; (2) construct functional networks using spontaneous brain activity in multiple slow bands; and (3) optimize topological economy of networks at individual level; and (4) characterize information flow with specific metrics of integration and segregation. We built an interactive online resource of reliability assessments for future ifNN (https://ibraindata.com/research/ifNN).

Changes in patterns of age-related network connectivity are associated with risk for schizophrenia

Alterations in fMRI-based brain functional network connectivity (FNC) are associated with schizophrenia (SCZ) and the genetic risk or subthreshold clinical symptoms preceding the onset of SCZ, which often occurs in early adulthood. Thus, age-sensitive FNC changes may be relevant to SCZ risk-related FNC. We used independent component analysis to estimate FNC from childhood to adulthood in 9,236 individuals. To capture individual brain features more accurately than single-session fMRI, we studied an average of three fMRI scans per individual. To identify potential familial risk-related FNC changes, we compared age-related FNC in first-degree relatives of SCZ patients mostly including unaffected siblings (SIB) with neurotypical controls (NC) at the same age stage. Then, we examined how polygenic risk scores for SCZ influenced risk-related FNC patterns. Finally, we investigated the same risk-related FNC patterns in adult SCZ patients (oSCZ) and young individuals with subclinical psychotic symptoms (PSY). Age-sensitive risk-related FNC patterns emerge during adolescence and early adulthood, but not before. Young SIB always followed older NC patterns, with decreased FNC in a cerebellar-occipitoparietal circuit and increased FNC in two prefrontal-sensorimotor circuits when compared to young NC. Two of these FNC alterations were also found in oSCZ, with one exhibiting reversed pattern. All were linked to polygenic risk for SCZ in unrelated individuals (R2 varied from 0.02 to 0.05). Young PSY showed FNC alterations in the same direction as SIB when compared to NC. These results suggest that age-related neurotypical FNC correlates with genetic risk for SCZ and is detectable with MRI in young participants.

The nature and neurobiology of fear and anxiety: State of the science and opportunities for accelerating discovery

Fear and anxiety play a central role in mammalian life, and there is considerable interest in clarifying their nature, identifying their biological underpinnings, and determining their consequences for health and disease. Here we provide a roundtable discussion on the nature and biological bases of fear- and anxiety-related states, traits, and disorders. The discussants include scientists familiar with a wide variety of populations and a broad spectrum of techniques. The goal of the roundtable was to take stock of the state of the science and provide a roadmap to the next generation of fear and anxiety research. Much of the discussion centered on the key challenges facing the field, the most fruitful avenues for future research, and emerging opportunities for accelerating discovery, with implications for scientists, funders, and other stakeholders. Understanding fear and anxiety is a matter of practical importance. Anxiety disorders are a leading burden on public health and existing treatments are far from curative, underscoring the urgency of developing a deeper understanding of the factors governing threat-related emotions.

Functional MRI responses to naturalistic stimuli are increasingly typical across early childhood

While findings show that throughout development, there are child- and age-specific patterns of brain functioning, there is also evidence for significantly greater inter-individual response variability in young children relative to adults. It is currently unclear whether this increase in functional "typicality" (i.e., inter-individual similarity) is a developmental process that occurs across early childhood, and what changes in BOLD response may be driving changes in typicality. We collected fMRI data from 81 typically developing 4-8-year-old children during passive viewing of age-appropriate television clips and asked whether there is increasing typicality of brain response across this age range. We found that the "increasing typicality" hypothesis was supported across many regions engaged by passive viewing. Post hoc analyses showed that in a priori ROIs related to language and face processing, the strength of the group-average shared component of activity increased with age, with no concomitant decline in residual signal or change in spatial extent or variability. Together, this suggests that increasing inter-individual similarity of functional responses to audiovisual stimuli is an important feature of early childhood functional brain development.

Stability, change, and reliable individual differences in electroencephalography measures: a lifespan perspective on progress and opportunities

Electroencephalographic (EEG) methods have great potential to serve both basic and clinical science approaches to understand individual differences in human neural function. Importantly, the psychometric properties of EEG data, such as internal consistency and test-retest reliability, constrain their ability to differentiate individuals successfully. Rapid and recent technological and computational advancements in EEG research make it timely to revisit the topic of psychometric reliability in the context of individual difference analyses. Moreover, pediatric and clinical samples provide some of the most salient and urgent opportunities to apply individual difference approaches, but the changes these populations experience over time also provide unique challenges from a psychometric perspective. Here we take a developmental neuroscience perspective to consider progress and new opportunities for parsing the reliability and stability of individual differences in EEG measurements across the lifespan. We first conceptually map the different profiles of measurement reliability expected for different types of individual difference analyses over the lifespan. Next, we summarize and evaluate the state of the field's empirical knowledge and need for testing measurement reliability, both internal consistency and test-retest reliability, across EEG measures of power, event-related potentials, nonlinearity, and functional connectivity across ages. Finally, we highlight how standardized pre-processing software for EEG denoising and empirical metrics of individual data quality may be used to further improve EEG-based individual differences research moving forward. We also include recommendations and resources throughout that individual researchers can implement to improve the utility and reproducibility of individual differences analyses with EEG across the lifespan.

Multimodal measures of spontaneous brain activity reveal both common and divergent patterns of cortical functional organization

Large-scale functional networks have been characterized in both rodent and human brains, typically by analyzing fMRI-BOLD signals. However, the relationship between fMRI-BOLD and underlying neural activity is complex and incompletely understood, which poses challenges to interpreting network organization obtained using this technique. Additionally, most work has assumed a disjoint functional network organization (i.e., brain regions belong to one and only one network). Here, we employed wide-field Ca2+ imaging simultaneously with fMRI-BOLD in mice expressing GCaMP6f in excitatory neurons. We determined cortical networks discovered by each modality using a mixed-membership algorithm to test the hypothesis that functional networks are overlapping rather than disjoint. Our results show that multiple BOLD networks are detected via Ca2+ signals; there is considerable network overlap (both modalities); networks determined by low-frequency Ca2+ signals are only modestly more similar to BOLD networks; and, despite similarities, important differences are detected across modalities (e.g., brain region "network diversity"). In conclusion, Ca2+ imaging uncovered overlapping functional cortical organization in the mouse that reflected several, but not all, properties observed with fMRI-BOLD signals.

Global diversity in individualized cortical network topography

Individualized cortical network topography (ICNT) varies between people and exhibits great variability in the association networks in the human brain. However, these findings were mainly discovered in Western populations. It remains unclear whether and how ICNT is shaped by the non-Western populations. Here, we leveraged a multisession hierarchical Bayesian model to define individualized functional networks in White American and Han Chinese populations with data from both US and Chinese Human Connectome Projects. We found that both the size and spatial topography of individualized functional networks differed between White American and Han Chinese groups, especially in the heteromodal association cortex (including the ventral attention, control, language, dorsal attention, and default mode networks). Employing a support vector machine, we then demonstrated that ethnicity-related ICNT diversity can be used to identify an individual's ethnicity with high accuracy (74%, pperm < 0.0001), with heteromodal networks contributing most to the classification. This finding was further validated through mass-univariate analyses with generalized additive models. Moreover, we reveal that the spatial heterogeneity of ethnic diversity in ICNT correlated with fundamental properties of cortical organization, including evolutionary cortical expansion, brain myelination, and cerebral blood flow. Altogether, this case study highlights a need for more globally diverse and publicly available neuroimaging datasets.

Robust dynamic brain coactivation states estimated in individuals

A confluence of evidence indicates that brain functional connectivity is not static but rather dynamic. Capturing transient network interactions in the individual brain requires a technology that offers sufficient within-subject reliability. Here, we introduce an individualized network-based dynamic analysis technique and demonstrate that it is reliable in detecting subject-specific brain states during both resting state and a cognitively challenging language task. We evaluate the extent to which brain states show hemispheric asymmetries and how various phenotypic factors such as handedness and gender might influence network dynamics, discovering a right-lateralized brain state that occurred more frequently in men than in women and more frequently in right-handed versus left-handed individuals. Longitudinal brain state changes were also shown in 42 patients with subcortical stroke over 6 months. Our approach could quantify subject-specific dynamic brain states and has potential for use in both basic and clinical neuroscience research.

The Role of fMRI in Drug Development: An Update

Functional magnetic resonance imaging (fMRI) of the brain is a technology that holds great potential for increasing the efficiency of drug development for the central nervous system (CNS). In preclinical studies and both early- and late-phase human trials, fMRI has the potential to improve cross-species translation of drug effects, help to de-risk compounds early in development, and contribute to the portfolio of evidence for a compound's efficacy and mechanism of action. However, to date, the utilization of fMRI in the CNS drug development process has been limited. The purpose of this chapter is to explore this mismatch between potential and utilization. This chapter provides introductory material related to fMRI and drug development, describes what is required of fMRI measurements for them to be useful in a drug development setting, lists current capabilities of fMRI in this setting and challenges faced in its utilization, and ends with directions for future development of capabilities in this arena. This chapter is the 5-year update of material from a previously published workshop summary (Carmichael et al., Drug DiscovToday 23(2):333-348, 2018).

Personality Neuroscience: An Emerging Field with Bright Prospects

Personality neuroscience is the study of persistent psychological individual differences, typically in the general population, using neuroscientific methods. It has the potential to shed light on the neurobiological mechanisms underlying individual differences and their manifestation in ongoing behavior and experience. The field was inaugurated many decades ago, yet has only really gained momentum in the last two, as suitable technologies have become widely available. Personality neuroscience employs a broad range of methods, including molecular genetics, pharmacological assays or manipulations, electroencephalography, and various neuroimaging modalities, such as magnetic resonance imaging and positron emission tomography. Although exciting progress is being made in this young field, much remains unknown. In this brief review, we discuss discoveries that have been made, methodological challenges and advances, and important questions that remain to be answered. We also discuss best practices for personality neuroscience research and promising future directions for the field.

Treatment biomarkers for ADHD: Taking stock and moving forward

The development of treatment biomarkers for psychiatric disorders has been challenging, particularly for heterogeneous neurodevelopmental conditions such as attention-deficit/hyperactivity disorder (ADHD). Promising findings are also rarely translated into clinical practice, especially with regard to treatment decisions and development of novel treatments. Despite this slow progress, the available neuroimaging, electrophysiological (EEG) and genetic literature provides a solid foundation for biomarker discovery. This article gives an updated review of promising treatment biomarkers for ADHD which may enhance personalized medicine and novel treatment development. The available literature points to promising pre-treatment profiles predicting efficacy of various pharmacological and non-pharmacological treatments for ADHD. These candidate predictive biomarkers, particularly those based on low-cost and non-invasive EEG assessments, show promise for the future stratification of patients to specific treatments. Studies with repeated biomarker assessments further show that different treatments produce distinct changes in brain profiles, which track treatment-related clinical improvements. These candidate monitoring/response biomarkers may aid future monitoring of treatment effects and point to mechanistic targets for novel treatments, such as neurotherapies. Nevertheless, existing research does not support any immediate clinical applications of treatment biomarkers for ADHD. Key barriers are the paucity of replications and external validations, the use of small and homogeneous samples of predominantly White children, and practical limitations, including the cost and technical requirements of biomarker assessments and their unknown feasibility and acceptability for people with ADHD. We conclude with a discussion of future directions and methodological changes to promote clinical translation and enhance personalized treatment decisions for diverse groups of individuals with ADHD.

Intrinsic brain activity patterns across large-scale networks predict reciprocity propensity

Reciprocity is prevalent across human societies, but individuals are heterogeneous regarding their reciprocity propensity. Although a large body of task-based brain imaging measures has shed light on the neural underpinnings of reciprocity at group level, the neural basis underlying the individual differences in reciprocity propensity remains largely unclear. Here, we combined brain imaging and machine learning techniques to individually predict reciprocity propensity from resting-state brain activity measured by fractional amplitude of low-frequency fluctuation. The brain regions contributing to the prediction were then analyzed for functional connectivity and decoding analyses, allowing for a data-driven quantitative inference on psychophysiological functions. Our results indicated that patterns of resting-state brain activity across multiple brain systems were capable of predicting individual reciprocity propensity, with the contributing regions distributed across the salience (e.g., ventrolateral prefrontal cortex), fronto-parietal (e.g., dorsolateral prefrontal cortex), default mode (e.g., ventromedial prefrontal cortex), and sensorimotor (e.g., supplementary motor area) networks. Those contributing brain networks are implicated in emotion and cognitive control, mentalizing, and motor-based processes, respectively. Collectively, these findings provide novel evidence on the neural signatures underlying the individual differences in reciprocity, and lend support the assertion that reciprocity emerges from interactions among regions embodied in multiple large-scale brain networks.

Cognitive decline is associated with frequency-specific resting state functional changes in normal aging

Resting state low-frequency brain activity may aid in our understanding of the mechanisms of aging-related cognitive decline. Our purpose was to explore the characteristics of the amplitude of low-frequency fluctuations (ALFF) in different frequency bands of fMRI to better understand cognitive aging. Thirty-seven cognitively normal older individuals underwent a battery of neuropsychological tests and MRI scans at baseline and four years later. ALFF from five different frequency bands (typical band, slow-5, slow-4, slow-3, and slow-2) were calculated and analyzed. A two-way ANOVA was used to explore the interaction effects in voxel-wise whole brain ALFF of the time and frequency bands. Paired-sample t-test was used to explore within-group changes over four years. Partial correlation analysis was performed to assess associations between the altered ALFF and cognitive function. Significant interaction effects of time × frequency were distributed over inferior frontal gyrus, superior frontal gyrus, right rolandic operculum, left thalamus, and right putamen. Significant ALFF reductions in all five frequency bands were mainly found in the right hemisphere and the posterior cerebellum; whereas localization of the significantly increased ALFF were mainly found in the cerebellum at typical band, slow-5 and slow-4 bands, and left hemisphere and the cerebellum at slow-3, slow-2 bands. In addition, ALFF changes showed frequency-specific correlations with changes in cognition. These results suggest that changes of local brain activity in cognitively normal aging should be investigated in multiple frequency bands. The association between ALFF changes and cognitive function can potentially aid better understanding of the mechanisms underlying normal cognitive aging.

Reproducibility and repeatability of magnetic resonance imaging in dementia

PURPOSE

Individualised predictive models of cognitive decline require disease-monitoring markers that are repeatable. For wide-spread adoption, such markers also need to be reproducible at different locations. This study assessed the repeatability and reproducibility of MRI markers derived from a dementia protocol.

METHODS

Six participants were scanned at three different sites with a 3T MRI scanner. The protocol employed: T1-weighted (T1w) imaging, resting state functional MRI (rsfMRI), arterial spin labelling (ASL), diffusion-weighted imaging (DWI), T2-weighted fluid attenuation inversion recovery (FLAIR), T2-weighted (T2w) imaging, and susceptibility weighted imaging (SWI). Participants were scanned repeatedly, up to six times over a maximum period of five years. One participant was also scanned a further three times on sequential days on one scanner. Fifteen derived metrics were computed from the seven different modalities.

RESULTS

Reproducibility (coefficient of variation; CoV, across sites) was best for T1w derived grey matter, white matter and hippocampal volume (CoV < 1.5%), compared to rsfMRI and SWI derived metrics (CoV, 19% and 21%). For a given metric, long-term repeatability (CoV across time) was comparable to reproducibility, with short-term repeatability considerably better.

CONCLUSIONS

Reproducibility and repeatability were assessed for a suite of markers calculated from a dementia MRI protocol. In general, structural markers were less variable than functional MRI markers. Variability over time on the same scanner was comparable to variability measured across different scanners. Overall, the results support the viability of multi-site longitudinal studies for monitoring cognitive decline.

Video-evoked fMRI BOLD responses are highly consistent across different data acquisition sites

Naturalistic imaging paradigms, in which participants view complex videos in the scanner, are increasingly used in human cognitive neuroscience. Videos evoke temporally synchronized brain responses that are similar across subjects as well as within subjects, but the reproducibility of these brain responses across different data acquisition sites has not yet been quantified. Here, we characterize the consistency of brain responses across independent samples of participants viewing the same videos in functional magnetic resonance imaging (fMRI) scanners at different sites (Indiana University and Caltech). We compared brain responses collected at these different sites for two carefully matched datasets with identical scanner models, acquisition, and preprocessing details, along with a third unmatched dataset in which these details varied. Our overall conclusion is that for matched and unmatched datasets alike, video-evoked brain responses have high consistency across these different sites, both when compared across groups and across pairs of individuals. As one might expect, differences between sites were larger for unmatched datasets than matched datasets. Residual differences between datasets could in part reflect participant-level variability rather than scanner- or data- related effects. Altogether our results indicate promise for the development and, critically, generalization of video fMRI studies of individual differences in healthy and clinical populations alike.

Multivariate Brain Activity while Viewing and Reappraising Affective Scenes Does Not Predict the Multiyear Progression of Preclinical Atherosclerosis in Otherwise Healthy Midlife Adults

Cognitive reappraisal is an emotion regulation strategy that is postulated to reduce risk for atherosclerotic cardiovascular disease (CVD), particularly the risk due to negative affect. At present, however, the brain systems and vascular pathways that may link reappraisal to CVD risk remain unclear. This study thus tested whether brain activity evoked by using reappraisal to reduce negative affect would predict the multiyear progression of a vascular marker of preclinical atherosclerosis and CVD risk: carotid artery intima-media thickness (CA-IMT). Participants were 176 otherwise healthy adults (50.6% women; aged 30-51 years) who completed a functional magnetic resonance imaging task involving the reappraisal of unpleasant scenes from the International Affective Picture System. Ultrasonography was used to compute CA-IMT at baseline and a median of 2.78 (interquartile range, 2.67 to 2.98) years later among 146 participants. As expected, reappraisal engaged brain systems implicated in emotion regulation. Reappraisal also reduced self-reported negative affect. On average, CA-IMT progressed over the follow-up period. However, multivariate and cross-validated machine-learning models demonstrated that brain activity during reappraisal failed to predict CA-IMT progression. Contrary to hypotheses, brain activity during cognitive reappraisal to reduce negative affect does not appear to forecast the progression of a vascular marker of CVD risk.

Supplementary Information

The online version contains supplementary material available at 10.1007/s42761-021-00098-y.

Reliability and stability challenges in ABCD task fMRI data

Trait stability of measures is an essential requirement for individual differences research. Functional MRI has been increasingly used in studies that rely on the assumption of trait stability, such as attempts to relate task related brain activation to individual differences in behavior and psychopathology. However, recent research using adult samples has questioned the trait stability of task-fMRI measures, as assessed by test-retest correlations. To date, little is known about trait stability of task fMRI in children. Here, we examined within-session reliability and long-term stability of individual differences in task-fMRI measures using fMRI measures of brain activation provided by the adolescent brain cognitive development (ABCD) Study Release v4.0 as an individual's average regional activity, using its tasks focused on reward processing, response inhibition, and working memory. We also evaluated the effects of factors potentially affecting reliability and stability. Reliability and stability (quantified as the ratio of non-scanner related stable variance to all variances) was poor in virtually all brain regions, with an average value of 0.088 and 0.072 for short term (within-session) reliability and long-term (between-session) stability, respectively, in regions of interest (ROIs) historically-recruited by the tasks. Only one reliability or stability value in ROIs exceeded the 'poor' cut-off of 0.4, and in fact rarely exceeded 0.2 (only 4.9%). Motion had a pronounced effect on estimated reliability/stability, with the lowest motion quartile of participants having a mean reliability/stability 2.5 times higher (albeit still 'poor') than the highest motion quartile. Poor reliability and stability of task-fMRI, particularly in children, diminishes potential utility of fMRI data due to a drastic reduction of effect sizes and, consequently, statistical power for the detection of brain-behavior associations. This essential issue urgently needs to be addressed through optimization of task design, scanning parameters, data acquisition protocols, preprocessing pipelines, and data denoising methods.

Shared and unique brain network features predict cognitive, personality, and mental health scores in the ABCD study

How individual differences in brain network organization track behavioral variability is a fundamental question in systems neuroscience. Recent work suggests that resting-state and task-state functional connectivity can predict specific traits at the individual level. However, most studies focus on single behavioral traits, thus not capturing broader relationships across behaviors. In a large sample of 1858 typically developing children from the Adolescent Brain Cognitive Development (ABCD) study, we show that predictive network features are distinct across the domains of cognitive performance, personality scores and mental health assessments. On the other hand, traits within each behavioral domain are predicted by similar network features. Predictive network features and models generalize to other behavioral measures within the same behavioral domain. Although tasks are known to modulate the functional connectome, predictive network features are similar between resting and task states. Overall, our findings reveal shared brain network features that account for individual variation within broad domains of behavior in childhood.

The Temporal Dynamics of Spontaneous Emotional Brain States and Their Implications for Mental Health

Temporal processes play an important role in elaborating and regulating emotional responding during routine mind wandering. However, it is unknown whether the human brain reliably transitions among multiple emotional states at rest and how psychopathology alters these affect dynamics. Here, we combined pattern classification and stochastic process modeling to investigate the chronometry of spontaneous brain activity indicative of six emotions (anger, contentment, fear, happiness, sadness, and surprise) and a neutral state. We modeled the dynamic emergence of these brain states during resting-state fMRI and validated the results across two population cohorts-the Duke Neurogenetics Study and the Nathan Kline Institute Rockland Sample. Our findings indicate that intrinsic emotional brain dynamics are effectively characterized as a discrete-time Markov process, with affective states organized around a neutral hub. The centrality of this network hub is disrupted in individuals with psychopathology, whose brain state transitions exhibit greater inertia and less frequent resetting from emotional to neutral states. These results yield novel insights into how the brain signals spontaneous emotions and how alterations in their temporal dynamics contribute to compromised mental health.

Frontostriatal Functional Connectivity Underlies Self-Enhancement During Social Evaluation

Self-enhancement, the tendency to view oneself positively, is a pervasive social motive widely investigated in the psychological sciences. Relatively little is known about the neurocognitive mechanisms underlying this motive, specifically in social-evaluative situations. To investigate whether positive emotion regulation circuitry, circuitry involved in modulating positive affect, relates to the self-enhancement motive in social contexts, we conducted an functional magnetic resonance imaging (fMRI) study in a healthy young adult sample. We hypothesized that self-enhancement indices (state and trait self-esteem) would relate to greater functional connectivity between right ventrolateral prefrontal cortex (RVLPFC), a region implicated in emotion regulation, and the ventral striatum (VS), a region associated with reward-related affect, during a social feedback task. Following social evaluation, participants experienced stable or decreased state self-esteem. Results showed that stable state self-esteem from pre- to post-scan and higher trait self-esteem related to greater RVLPFC–VS connectivity during positive evaluation. Stable-state self-esteem also related to greater RVLPFC–VS connectivity during negative evaluation. Moreover, RVLPFC activation during all types of feedback processing and left VS activation during negative feedback processing was greater for participants with stable-state self-esteem. These findings implicate neurocognitive mechanisms underlying emotion regulation in the self-enhancement motive and highlight a pathway through which self-enhancement may restore feelings of self-worth during threatening situations.