Neural timescales reflect behavioral demands in freely moving rhesus macaques.

Previous work demonstrated a highly reproducible cortical hierarchy of neural timescales at rest, with sensory areas displaying fast, and higher-order association areas displaying slower timescales. The question arises how such stable hierarchies give rise to adaptive behavior that requires flexible adjustment of temporal coding and integration demands. Potentially, this lack of variability in the hierarchical organization of neural timescales could reflect the structure of the laboratory contexts. We posit that unconstrained paradigms are ideal to test whether the dynamics of neural timescales reflect behavioral demands. Here we measured timescales of local field potential activity while male rhesus macaques foraged in an open space. We found a hierarchy of neural timescales that differs from previous work. Importantly, although the magnitude of neural timescales expanded with task engagement, the brain areas' relative position in the hierarchy was stable. Next, we demonstrated that the change in neural timescales is dynamic and contains functionally-relevant information, differentiating between similar events in terms of motor demands and associated reward. Finally, we demonstrated that brain areas are differentially affected by these behavioral demands. These results demonstrate that while the space of neural timescales is anatomically constrained, the observed hierarchical organization and magnitude is dependent on behavioral demands.

Assessment of substance exposures in nail clipping samples: A systematic review.

BACKGROUND: Studies of prenatal substance exposure often rely on self-report, urine drug screens, and/or analyses of blood or meconium biomarkers. Accuracy of these measures is limited when assessing exposure over many weeks or months of gestation. Nails are increasingly being considered as a matrix from which to assess substance exposure. This systematic review synthesizes data on the validity of detecting alcohol, nicotine, cannabis, and opioid from nail clippings, with an emphasis on prenatal exposure assessment. METHODS: The systematic review was conducted using PRISMA 2020 guidelines. Seven databases were searched with keywords relevant to the four substances of interest. Results were summarized grouping manuscripts by the exposure of interest with focus on accuracy and feasibility. RESULTS: Of 2384 papers initially identified, 35 manuscripts were included in our qualitative synthesis. Only a few studies specifically looked at pregnant individuals or mother-child dyads. Across the four substances, many studies demonstrated a dose-response relationship between exposure and concentration of analytes in nails. Nail assays appear to detect lower level of exposure compared to hair; however, sample insufficiency, especially for multi-substance assays, remains a limitation. CONCLUSIONS: Based on the reviewed studies, nail clippings are an acceptable and potentially preferable matrix for the evaluation of these four prenatal substances when sampling frequency and/or study design necessitates assessment of past exposures over an extended period. Nails have the advantage of infrequent sampling and minimal invasiveness to assess a broad exposure period. Future studies should examine validity of analytes in toenail versus fingernail clippings.

Whole-brain resting-state connectivity underlying impaired inhibitory control during early versus longer-term abstinence in cocaine addiction.

Lapses in inhibitory control have been linked to relapse in human drug addiction. Evidence suggests differences in inhibitory control depending on abstinence duration, but the underlying neural mechanisms remain unknown. We hypothesized that early abstinence (2-5 days) would be characterized by the strongest impairments of inhibitory control and most wide-spread deviations in resting-state functional connectivity of brain networks, while longer-term abstinence (>30 days) would be characterized by weaker impairments as compared to healthy controls. In this laboratory-based cross-sectional study, we compared individuals with Cocaine Use Disorder (iCUD) during early (cocaine urine-positive: N = 19, iCUD+; 32% female; mean age: 46.8 years) and longer-term abstinence (cocaine urine-negative: N = 29, iCUD-; 15% female; mean age: 46.6 years) to healthy controls (N = 33; 24% female; mean age: 40.9 years). We compared the groups on inhibitory control performance (Stop-Signal Task) and, using a whole-brain graph theory analysis (638 region parcellation) of functional magnetic resonance imaging (fMRI) data, we tested for group differences in resting-state brain function (local/global efficiency). We characterized how resting-state brain function was associated with inhibitory control performance within iCUD. Inhibitory control performance was worst in the early abstinence group, and intermediate in the longer-term abstinence group, as compared to the healthy control group (P < 0.01). More recent use of cocaine (CUD+ > CUD- > healthy controls) was characterized by decreased efficiency in fronto-temporal and subcortical networks (primarily in the salience, semantic, and basal ganglia networks) and increased efficiency in visual networks. Importantly, a similar functional connectivity pattern characterized impaired inhibitory control performance within iCUD (all brain analyses P < 0.05, FWE-corrected). Together, we demonstrated that a similar pattern of systematic and widespread deviations in resting-state brain efficiency, extending beyond the networks commonly investigated in human drug addiction, is linked to both abstinence duration and inhibitory control deficits in iCUD.

Closing the loop between brain and electrical stimulation: towards precision neuromodulation treatments.

One of the most critical challenges in using noninvasive brain stimulation (NIBS) techniques for the treatment of psychiatric and neurologic disorders is inter- and intra-individual variability in response to NIBS. Response variations in previous findings suggest that the one-size-fits-all approach does not seem the most appropriate option for enhancing stimulation outcomes. While there is a growing body of evidence for the feasibility and effectiveness of individualized NIBS approaches, the optimal way to achieve this is yet to be determined. Transcranial electrical stimulation (tES) is one of the NIBS techniques showing promising results in modulating treatment outcomes in several psychiatric and neurologic disorders, but it faces the same challenge for individual optimization. With new computational and methodological advances, tES can be integrated with real-time functional magnetic resonance imaging (rtfMRI) to establish closed-loop tES-fMRI for individually optimized neuromodulation. Closed-loop tES-fMRI systems aim to optimize stimulation parameters based on minimizing differences between the model of the current brain state and the desired value to maximize the expected clinical outcome. The methodological space to optimize closed-loop tES fMRI for clinical applications includes (1) stimulation vs. data acquisition timing, (2) fMRI context (task-based or resting-state), (3) inherent brain oscillations, (4) dose-response function, (5) brain target trait and state and (6) optimization algorithm. Closed-loop tES-fMRI technology has several advantages over non-individualized or open-loop systems to reshape the future of neuromodulation with objective optimization in a clinically relevant context such as drug cue reactivity for substance use disorder considering both inter and intra-individual variations. Using multi-level brain and behavior measures as input and desired outcomes to individualize stimulation parameters provides a framework for designing personalized tES protocols in precision psychiatry.

Neural timescales reflect behavioral demands in freely moving rhesus macaques.

Previous work has demonstrated remarkably reproducible and consistent hierarchies of neural timescales across cortical areas at rest. The question arises how such stable hierarchies give rise to adaptive behavior that requires flexible adjustment of temporal coding and integration demands. Potentially, this previously found lack of variability in the hierarchical organization of neural timescales could be a reflection of the structure of the laboratory contexts in which they were measured. Indeed, computational work demonstrates the existence of multiple temporal hierarchies within the same anatomical network when the input structure is altered. We posit that unconstrained behavioral environments where relatively little temporal demands are imposed from the experimenter are an ideal test bed to address the question of whether the hierarchical organization and the magnitude of neural timescales reflect ongoing behavioral demands. To tackle this question, we measured timescales of local field potential activity while rhesus macaques were foraging freely in a large open space. We find a hierarchy of neural timescales that is unique to this foraging environment. Importantly, although the magnitude of neural timescales generally expanded with task engagement, the brain areas' relative position in the hierarchy was stable across the recording sessions. Notably, the magnitude of neural timescales monotonically expanded with task engagement across a relatively long temporal scale spanning the duration of the recording session. Over shorter temporal scales, the magnitude of neural timescales changed dynamically around foraging events. Moreover, the change in the magnitude of neural timescales contained functionally relevant information, differentiating between seemingly similar events in terms of motor demands and associated reward. That is, the patterns of change were associated with the cognitive and behavioral meaning of these events. Finally, we demonstrated that brain areas were differentially affected by these behavioral demands - i.e., the expansion of neural timescales was not the same across all areas. Together, these results demonstrate that the observed hierarchy of neural timescales is context-dependent and that changes in the magnitude of neural timescales are closely related to overall task engagement and behavioral demands.

Subtypes in addiction and their neurobehavioral profiles across three functional domains.

Rates of return to use in addiction treatment remain high. We argue that the development of improved treatment options will require advanced understanding of individual heterogeneity in Substance Use Disorders (SUDs). We hypothesized that considerable individual differences exist in the three functional domains underlying addiction-approach-related behavior, executive function, and negative emotionality. We included N = 593 participants from the enhanced Nathan Kline Institute-Rockland Sample community sample (ages 18-59, 67% female) that included N = 420 Controls and N = 173 with past SUDs [54% female; N = 75 Alcohol Use Disorder (AUD) only, N = 30 Cannabis Use Disorder (CUD) only, and N = 68 Multiple SUDs]. To test our a priori hypothesis that distinct neuro-behavioral subtypes exist within individuals with past SUDs, we conducted a latent profile analysis with all available phenotypic data as input (74 subscales from 18 measures), and then characterized resting-state brain function for each discovered subtype. Three subtypes with distinct neurobehavioral profiles were recovered (p < 0.05, Cohen's D: 0.4-2.8): a "Reward type" with higher approach-related behavior (N = 69); a "Cognitive type" with lower executive function (N = 70); and a "Relief type" with high negative emotionality (N = 34). For those in the Reward type, substance use mapped onto resting-state connectivity in the Value/Reward, Ventral-Frontoparietal and Salience networks; for the Cognitive type in the Auditory, Parietal Association, Frontoparietal and Salience networks; and for the Relief type in the Parietal Association, Higher Visual and Salience networks (pFDR < 0.05). Subtypes were equally distributed amongst individuals with different primary SUDs (χ2 = 4.71, p = 0.32) and gender (χ2 = 3.44, p = 0.18). Results support functionally derived subtypes, demonstrating considerable individual heterogeneity in the multi-dimensional impairments in addiction. This confirms the need for mechanism-based subtyping to inform the development of personalized addiction medicine approaches.

Current Approaches in Computational Psychiatry for the Data-Driven Identification of Brain-Based Subtypes.

The ability of our current psychiatric nosology to accurately delineate clinical populations and inform effective treatment plans has reached a critical point with only moderately successful interventions and high relapse rates. These challenges continue to motivate the search for approaches to better stratify clinical populations into more homogeneous delineations, to better inform diagnosis and disease evaluation, and prescribe and develop more precise treatment plans. The promise of brain-based subtyping based on neuroimaging data is that finding subgroups of individuals with a common biological signature will facilitate the development of biologically grounded, targeted treatments. This review provides a snapshot of the current state of the field in empirical brain-based subtyping studies in child, adolescent, and adult psychiatric populations published between 2019 and March 2022. We found that there is vast methodological exploration and a surprising number of new methods being created for the specific purpose of brain-based subtyping. However, this methodological exploration and advancement is not being met with rigorous validation approaches that assess both reproducibility and clinical utility of the discovered brain-based subtypes. We also found evidence for a collaboration crisis, in which methodological exploration and advancements are not clearly grounded in clinical goals. We propose several steps that we believe are crucial to address these shortcomings in the field. We conclude, and agree with the authors of the reviewed studies, that the discovery of biologically grounded subtypes would be a significant advancement for treatment development in psychiatry.

Explainable machine learning analysis reveals sex and gender differences in the phenotypic and neurobiological markers of Cannabis Use Disorder.

Cannabis Use Disorder (CUD) has been linked to a complex set of neuro-behavioral risk factors. While many studies have revealed sex and gender differences, the relative importance of these risk factors by sex and gender has not been described. We used an "explainable" machine learning approach that combined decision trees [gradient tree boosting, XGBoost] with factor ranking tools [SHapley's Additive exPlanations (SHAP)] to investigate sex and gender differences in CUD. We confirmed that previously identified environmental, personality, mental health, neurocognitive, and brain factors highly contributed to the classification of cannabis use levels and diagnostic status. Risk factors with larger effect sizes in men included personality (high openness), mental health (high externalizing, high childhood conduct disorder, high fear somaticism), neurocognitive (impulsive delay discounting, slow working memory performance) and brain (low hippocampal volume) factors. Conversely, risk factors with larger effect sizes in women included environmental (low education level, low instrumental support) factors. In summary, environmental factors contributed more strongly to CUD in women, whereas individual factors had a larger importance in men.

Gender Differences in the Psychosocial Determinants Underlying the Onset and Maintenance of Alcohol Use Disorder.

A large number of different mechanisms have been linked to Alcohol Use Disorder (AUD), including psychosocial, neurocognitive, affective, and neurobiological factors. Gender has been shown to impact the presentation and progression of AUD; yet, little work has been done to parse the different mechanisms underlying AUD within the lens of gender differences. A review of the literature on adolescence revealed that psychosocial factors, in particular lack of family social support and interactions with peers, drive the onset of alcohol use more strongly in girls relative to boys. However, research done on gender differences in disease progression in adults remains limited. Our gender-specific analysis of the mechanisms underlying AUD in adults revealed that lack of social support was causally linked to negative affect, mental health symptoms, and AUD symptom severity in women, but not men. These novel results suggest that psychosocial factors may play a gender-specific role not only in the onset of use in adolescence, but also in the maintenance of addiction in adults. If confirmed, this suggests the need for investigating gender-specific recovery trajectories. In this perspective piece, we review the literature regarding gender differences in the onset and maintenance of AUD and present original data that support unique risk factors in women.

The resting-state causal human connectome is characterized by hub connectivity of executive and attentional networks.

We demonstrate a data-driven approach for calculating a "causal connectome" of directed connectivity from resting-state fMRI data using a greedy adjacency search and pairwise non-Gaussian edge orientations. We used this approach to construct n = 442 causal connectomes. These connectomes were very sparse in comparison to typical Pearson correlation-based graphs (roughly 2.25% edge density) yet were fully connected in nearly all cases. Prominent highly connected hubs of the causal connectome were situated in attentional (dorsal attention) and executive (frontoparietal and cingulo-opercular) networks. These hub networks had distinctly different connectivity profiles: attentional networks shared incoming connections with sensory regions and outgoing connections with higher cognitive networks, while executive networks primarily connected to other higher cognitive networks and had a high degree of bidirected connectivity. Virtual lesion analyses accentuated these findings, demonstrating that attentional and executive hub networks are points of critical vulnerability in the human causal connectome. These data highlight the central role of attention and executive control networks in the human cortical connectome and set the stage for future applications of data-driven causal connectivity analysis in psychiatry.

Intrinsic timescales as an organizational principle of neural processing across the whole rhesus macaque brain.

Hierarchical temporal dynamics are a fundamental computational property of the brain; however, there are no whole brain, noninvasive investigations into timescales of neural processing in animal models. To that end, we used the spatial resolution and sensitivity of ultrahigh field functional magnetic resonance imaging (fMRI) performed at 10.5 T to probe timescales across the whole macaque brain. We uncovered within-species consistency between timescales estimated from fMRI and electrophysiology. Crucially, we extended existing electrophysiological hierarchies to whole-brain topographies. Our results validate the complementary use of hemodynamic and electrophysiological intrinsic timescales, establishing a basis for future translational work. Further, with these results in hand, we were able to show that one facet of the high-dimensional functional connectivity (FC) topography of any region in the brain is closely related to hierarchical temporal dynamics. We demonstrated that intrinsic timescales are organized along spatial gradients that closely match FC gradient topographies across the whole brain. We conclude that intrinsic timescales are a unifying organizational principle of neural processing across the whole brain.

Computational validity: using computation to translate behaviours across species.

We propose a new conceptual framework (computational validity) for translation across species and populations based on the computational similarity between the information processing underlying parallel tasks. Translating between species depends not on the superficial similarity of the tasks presented, but rather on the computational similarity of the strategies and mechanisms that underlie those behaviours. Computational validity goes beyond construct validity by directly addressing questions of information processing. Computational validity interacts with circuit validity as computation depends on circuits, but similar computations could be accomplished by different circuits. Because different individuals may use different computations to accomplish a given task, computational validity suggests that behaviour should be understood through the subject's point of view; thus, behaviour should be characterized on an individual level rather than a task level. Tasks can constrain the computational algorithms available to a subject and the observed subtleties of that behaviour can provide information about the computations used by each individual. Computational validity has especially high relevance for the study of psychiatric disorders, given the new views of psychiatry as identifying and mediating information processing dysfunctions that may show high inter-individual variability, as well as for animal models investigating aspects of human psychiatric disorders. This article is part of the theme issue 'Systems neuroscience through the lens of evolutionary theory'.

Reducing craving and consumption in individuals with drug addiction, obesity or overeating through neuromodulation intervention: a systematic review and meta-analysis of its follow-up effects.

BACKGROUND AND AIMS: Non-invasive brain stimulation has shown potential in clinical applications aiming at reducing craving and consumption levels in individuals with drug addiction or overeating behaviour. However, it is unclear whether these intervention effects are maintained over time. This study aimed to measure the immediate, short- and long-term effects of excitatory transcranial direct current stimulation (tDCS) and high-frequency repetitive transcranial magnetic stimulation (rTMS) targeting at dorsolateral prefrontal cortex (dlPFC) in people with drug addiction or overeating. METHODS: A systematic review and random effects meta-analysis. We included 20 articles (total of 22 studies using randomized controlled trials: 3 alcohol dependence, 3 drug dependence, 12 smoking, 4 overeating; total: 720 participants) from January 2000 to June 2020, which reported at least one follow-up assessment of craving, consumption or abstinence levels after the intervention. We compared effects of active versus sham stimulation immediately after the intervention and at the last follow-up assessment, as compared with baseline. RESULTS: Excitatory neuromodulation of dlPFC activity reduced craving and consumption immediately after the intervention (craving: g = 0.734, CI = 0.447-1.021, P < 0.001; consumption: g = 0.527, CI = 0.309-0.745; P < 0.001), as well as during short-, mid- and long-term abstinence (craving: g = 0.677, CI = 0.440-0.914, P < 0.001; consumption: g = 0.445, CI = 0.245-0.645, P < 0.001; abstinence levels: g = 0.698, CI = 0.433-0.963, P < 0.001; average time of follow-up: 84 ± 83 days after last stimulation). Additional analysis demonstrated that the intervention effects were sustained in all populations studied (food, nicotine, alcohol or drug abuse) and with both stimulation techniques used (rTMS, tDCS). Interventions targeting at the left (vs right) hemisphere may be more effective. CONCLUSIONS: Excitatory neuromodulation targeting the dorsolateral prefrontal cortex appears to lead to a sustained reduction of craving and consumption in individuals with addiction or overeating behaviour.

Attention bias modification in drug addiction: Enhancing control of subsequent habits.

A relapse in addiction is often precipitated by heightened attention bias to drug-related cues, underpinned by a subcortically mediated transition to habitual/automatized responding and reduced prefrontal control. Modification of such automatized attention bias is a fundamental, albeit elusive, target for relapse reduction. Here, on a trial-by-trial basis, we used electroencephalography and eye tracking with a task that assessed, in this order, drug cue reactivity, its instructed self-regulation via reappraisal, and the immediate aftereffects on spontaneous (i.e., not instructed and automatized) attention bias. The results show that cognitive reappraisal, a facet of prefrontal control, decreased spontaneous attention bias to drug-related cues in cocaine-addicted individuals, more so in those with less frequent recent use. The results point to the mechanisms underlying the disruption of automatized maladaptive drug-related attention bias in cocaine addiction. These results pave the way for future studies to examine the role of such habit disruption in reducing compulsive drug seeking outside the controlled laboratory environment, with the ultimate goal of developing a readily deployable cognitive-behavioral and personalized intervention for drug addiction.

An integrated multimodal model of alcohol use disorder generated by data-driven causal discovery analysis.

Alcohol use disorder (AUD) has high prevalence and adverse societal impacts, but our understanding of the factors driving AUD is hampered by a lack of studies that describe the complex neurobehavioral mechanisms driving AUD. We analyzed causal pathways to AUD severity using Causal Discovery Analysis (CDA) with data from the Human Connectome Project (HCP; n = 926 [54% female], 22% AUD [37% female]). We applied exploratory factor analysis to parse the wide HCP phenotypic space (100 measures) into 18 underlying domains, and we assessed functional connectivity within 12 resting-state brain networks. We then employed data-driven CDA to generate a causal model relating phenotypic factors, fMRI network connectivity, and AUD symptom severity, which highlighted a limited set of causes of AUD. The model proposed a hierarchy with causal influence propagating from brain connectivity to cognition (fluid/crystalized cognition, language/math ability, & working memory) to social (agreeableness/social support) to affective/psychiatric function (negative affect, low conscientiousness/attention, externalizing symptoms) and ultimately AUD severity. Our data-driven model confirmed hypothesized influences of cognitive and affective factors on AUD, while underscoring that addiction models need to be expanded to highlight the importance of social factors, amongst others.

Ultra-high field (10.5 T) resting state fMRI in the macaque.

Resting state functional connectivity refers to the temporal correlations between spontaneous hemodynamic signals obtained using functional magnetic resonance imaging. This technique has demonstrated that the structure and dynamics of identifiable networks are altered in psychiatric and neurological disease states. Thus, resting state network organizations can be used as a diagnostic, or prognostic recovery indicator. However, much about the physiological basis of this technique is unknown. Thus, providing a translational bridge to an optimal animal model, the macaque, in which invasive circuit manipulations are possible, is of utmost importance. Current approaches to resting state measurements in macaques face unique challenges associated with signal-to-noise, the need for contrast agents limiting translatability, and within-subject designs. These limitations can, in principle, be overcome through ultra-high magnetic fields. However, imaging at magnetic fields above 7T has yet to be adapted for fMRI in macaques. Here, we demonstrate that the combination of high channel count transmitter and receiver arrays, optimized pulse sequences, and careful anesthesia regimens, allows for detailed single-subject resting state analysis at high resolutions using a 10.5 Tesla scanner. In this study, we uncover thirty spatially detailed resting state components that are highly robust across individual macaques and closely resemble the quality and findings of connectomes from large human datasets. This detailed map of the rsfMRI 'macaque connectome' will be the basis for future neurobiological circuit manipulation work, providing valuable biological insights into human connectomics.

Altered brain network organization in romantic love as measured with resting-state fMRI and graph theory.

Romantic love is a complex state that has been seen as similar to addiction. Previous task-based functional magnetic resonance imaging (fMRI) studies have shown that being in love is closely associated with functional brain changes in the reward and motivation system. However, romantic love-related functional connectivity network organization in resting-state fMRI has yet to be elucidated. To that end, here we used resting-state fMRI and graph theory to compare whole-brain functional network topology between an "in-love" group (n = 34, 16 females, currently in love and in a romantic relationship) and a "single" group (n = 32, 14 females, never in love and not in a romantic relationship). Compared to the single group, we found lower network segregation in the love group (i.e., lower small-worldness, mean clustering coefficient, and modularity), and these metrics were negatively associated with scores on the Passionate Love Scale (PLS) (an index of intense passionate/romantic love). Additionally, the love group displayed altered connectivity degree (reflecting the importance of a node): decreased degree in left angular gyrus and left medial orbitofrontal cortex, but increased degree in left fusiform gyrus. Furthermore, local efficiency or degree of these regions was significantly correlated to PLS scores. Taken together, results showed decreased overall brain functional segregation but enhanced emotional-social processing in romantic lovers. These findings provide the first evidence of love-related brain network organization changes and suggest similar but different brain network alterations between romantic love and addiction, providing new insights on the neural systems underlying romantic love.

A preliminary study on prenatal polybrominated diphenyl ether serum concentrations and intrinsic functional network organization and executive functioning in childhood.

BACKGROUND: The prenatal period is a period of vulnerability during which neurotoxic exposures exert persistent changes in brain development and behavior. Polybrominated diphenyl ethers (PBDEs), used as flame retardants in commercial products, are known to be developmental neurotoxicants. PBDEs were phased out of use in the United States a decade ago, but exposure remains widespread due to their release from existing products and biopersistence. Despite consistent animal and epidemiological evidence of developmental neurotoxicity, the neural substrates linking prenatal PBDE serum concentrations to impaired neurodevelopment are poorly understood. METHODS: In the present study, we used resting state functional magnetic resonance imaging (fMRI) to examine associations between prenatal PBDE concentrations measured in maternal serum and intrinsic functional network organization (i.e., global and local efficiency; estimated using a graph-theoretical approach) in 5-year-old children (n = 34). We explored whether PBDE serum concentrations were associated with executive functioning (EF) assessed using a parent-report questionnaire (BRIEF-P) (n = 106) and whether changes in intrinsic functional network organization linked the association between prenatal PBDE serum concentrations and EF problems. RESULTS: Children with higher prenatal PBDE serum concentrations showed: (a) increased global efficiency of brain areas involved in visual attention (e.g., inferior occipital gyrus) (ß's = .01, FDR-corrected p's ≤ .05); (b) more reported EF problems (ß's = .001, FDR-corrected p's ≤ .05). Higher global efficiency of brain areas involved in visual attention was associated with more EF problems (ß's = .01, FDR-corrected p's < .05). CONCLUSIONS: Intrinsic functional network organization of visual attention brain areas linked prenatal PBDE concentrations to EF problems in childhood. Visual attention may contribute to the development of higher-order cognitive functions, such as EF, which could be explored in future studies.