From gut to brain: unveiling probiotic effects through a neuroimaging perspective-A systematic review of randomized controlled trials.

The gut-brain axis, a bidirectional communication network between the gastrointestinal system and the brain, significantly influences mental health and behavior. Probiotics, live microorganisms conferring health benefits, have garnered attention for their potential to modulate this axis. However, their effects on brain function through gut microbiota modulation remain controversial. This systematic review examines the effects of probiotics on brain activity and functioning, focusing on randomized controlled trials using both resting-state and task-based functional magnetic resonance imaging (fMRI) methodologies. Studies investigating probiotic effects on brain activity in healthy individuals and clinical populations (i.e., major depressive disorder and irritable bowel syndrome) were identified. In healthy individuals, task-based fMRI studies indicated that probiotics modulate brain activity related to emotional regulation and cognitive processing, particularly in high-order areas such as the amygdala, precuneus, and orbitofrontal cortex. Resting-state fMRI studies revealed changes in connectivity patterns, such as increased activation in the Salience Network and reduced activity in the Default Mode Network. In clinical populations, task-based fMRI studies showed that probiotics could normalize brain function in patients with major depressive disorder and irritable bowel syndrome. Resting-state fMRI studies further suggested improved connectivity in mood-regulating networks, specifically in the subcallosal cortex, amygdala and hippocampus. Despite promising findings, methodological variability and limited sample sizes emphasize the need for rigorous, longitudinal research to clarify the beneficial effects of probiotics on the gut-brain axis and mental health.

Brain activity associated with emotion regulation predicts individual differences in working memory ability.

Previous behavioral research has found that working memory is associated with emotion regulation efficacy. However, there has been mixed evidence as to whether the neural mechanisms between emotion regulation and working memory overlap. The present study tested the prediction that individual differences on the working memory subtest of the Weschler Adult Intelligence Scale (WAIS-IV) could be predicted from the pattern of brain activity produced during emotion regulation in regions typically associated with working memory, such as the dorsal lateral prefrontal cortex (dlPFC). A total of 101 participants completed an emotion regulation fMRI task in which they either viewed or reappraised negative images. Participants also completed working memory test outside the scanner. A whole brain covariate analysis contrasting the reappraise negative and view negative BOLD response found that activity in the right dlPFC positively related to working memory ability. Moreover, a multivoxel pattern analysis approach using tenfold cross-validated support vector regression in regions-of-interest associated with working memory, including bilateral dlPFC, demonstrated that we could predict individual differences in working memory ability from the pattern of activity associated with emotion regulation. These findings support the idea that emotion regulation shares underlying cognitive processes and neural mechanisms with working memory, particularly in the dlPFC.

Pre-choice midbrain fluctuations affect self-control in food choice: A functional magnetic resonance imaging (fMRI) study.

Recent studies have shown that spontaneous pre-stimulus fluctuations in brain activity affect higher-order cognitive processes, including risky decision-making, cognitive flexibility, and aesthetic judgments. However, there is currently no direct evidence to suggest that pre-choice activity influences value-based decisions that require self-control. We examined the impact of fluctuations in pre-choice activity in key regions of the reward system on self-control in food choice. In the functional magnetic resonance imaging (fMRI) scanner, 49 participants made 120 food choices that required self-control in high and low working memory load conditions. The task was designed to ensure that participants were cognitively engaged and not thinking about upcoming choices. We defined self-control success as choosing a food item that was healthier over one that was tastier. The brain regions of interest (ROIs) were the ventral tegmental area (VTA), putamen, nucleus accumbens (NAc), and caudate nucleus. For each participant and condition, we calculated the mean activity in the 3-s interval preceding the presentation of food stimuli in successful and failed self-control trials. These activities were then used as predictors of self-control success in a fixed-effects logistic regression model. The results indicate that increased pre-choice VTA activity was linked to a higher probability of self-control success in a subsequent food-choice task within the low-load condition, but not in the high-load condition. We posit that pre-choice fluctuations in VTA activity change the reference point for immediate (taste) reward evaluation, which may explain our finding. This suggests that the neural context of decisions may be a key factor influencing human behavior.

Delineating Region-Specific contributions and connectivity patterns for semantic association and categorization through ROI and Granger causality analysis.

The neural mechanisms supporting semantic association and categorization are examined in this study. Semantic association involves linking concepts through shared themes, events, or scenes, while semantic categorization organizes meanings hierarchically based on defining features. Twenty-three adults participated in an fMRI study performing categorization and association judgment tasks. Results showed stronger activation in the inferior frontal gyrus during association and marginally weaker activation in the posterior middle temporal gyrus (pMTG) during categorization. Granger causality analysis revealed bottom-up connectivity from the visual cortex to the hippocampus during semantic association, whereas semantic categorization exhibited strong reciprocal connections between the pMTG and frontal semantic control regions, together with information flow from the visual association area and hippocampus to the pars triangularis. We propose that demands on semantic retrieval, precision of semantic representation, perceptual experiences and world knowledge result in observable differences between these two semantic relations.

Meta-analysis of resting-state fMRI in cervical spondylosis patients using AES-SDM.

Background: Resting-state functional magnetic resonance imaging (rs-fMRI) reveals diverse neural activity patterns in cervical spondylosis (CS) patients. However, the reported results are inconsistent. Therefore, our objective was to conduct a meta-analysis to synthesize the findings from existing rs-fMRI studies and identify consistent patterns of neural brain activity alterations in patients with CS. Materials and methods: A systematic search was conducted across PubMed, Web of Knowledge, Embase, Google Scholar, and CNKI for rs-fMRI studies that compared CS patients with healthy controls (HCs), up to January 28, 2024. Significant cluster coordinates were extracted for comprehensive analysis. Results: We included 16 studies involving 554 CS patients and 488 HCs. CS patients demonstrated decreased brain function in the right superior temporal gyrus and left postcentral gyrus, and increased function in the left superior frontal gyrus. Jackknife sensitivity analysis validated the robustness of these findings, and Egger's test confirmed the absence of significant publication bias (p > 0.05). Meta-regression showed no significant impact of age or disease duration differences on the results. Conclusion: This meta-analysis confirms consistent alterations in specific brain regions in CS patients, highlighting the potential of rs-fMRI to refine diagnostic and therapeutic strategies. Systematic review registration: https://www.crd.york.ac.uk/prospero/, identifier CRD42024496263.

FunMaps: a method for parcellating functional brain networks using resting-state functional MRI data.

Parcellations of resting-state functional magnetic resonance imaging (rs-fMRI) data are widely used to create topographical maps of functional networks in the human brain. While such network maps are highly useful for studying brain organization and function, they usually require large sample sizes to make them, thus creating practical limitations for researchers that would like to carry out parcellations on data collected in their labs. Furthermore, it can be difficult to quantitatively evaluate the results of a parcellation since networks are usually identified using a clustering algorithm, like principal components analysis, on the results of a single group-averaged connectivity map. To address these challenges, we developed the FunMaps method: a parcellation routine that intrinsically incorporates stability and replicability of the parcellation by keeping only network distinctions that agree across halves of the data over multiple random iterations. Here, we demonstrate the efficacy and flexibility of FunMaps, while describing step-by-step instructions for running the program. The FunMaps method is publicly available on GitHub (https://github.com/persichetti-lab/FunMaps). It includes source code for running the parcellation and auxiliary code for preparing data, evaluating the parcellation, and displaying the results.

Exploring spontaneous brain activity changes in high-altitude smokers: Insights from ALFF/fALFF analysis.

INTRODUCTION: This study aims to explore the impact of smoking on intrinsic brain activity among high-altitude (HA) populations. Smoking is associated with various neural alterations, but it remains unclear whether smokers in HA environments exhibit specific neural characteristics. METHODS: We employed ALFF and fALFF methods across different frequency bands to investigate differences in brain functional activity between high-altitude smokers and non-smokers. 31 smokers and 31 non-smokers from HA regions participated, undergoing resting-state functional magnetic resonance imaging (rs-fMRI) scans. ALFF/fALFF values were compared between the two groups. Correlation analyses explored relationships between brain activity and clinical data. RESULTS: Smokers showed increased ALFF values in the right superior frontal gyrus (R-SFG), right middle frontal gyrus (R-MFG), right anterior cingulate cortex (R-ACC), right inferior frontal gyrus (R-IFG), right superior/medial frontal gyrus (R-MSFG), and left SFG compared to non-smokers in HA. In sub-frequency bands (0.01-0.027 Hz and 0.027-0.073 Hz), smokers showed increased ALFF values in R-SFG, R-MFG, right middle cingulate cortex (R-MCC), R-MSFG, Right precentral gyrus and L-SFG while decreased fALFF values were noted in the right postcentral and precentral gyrus in the 0.01-0.027 Hz band. Negative correlations were found between ALFF values in the R-SFG and smoking years. CONCLUSION: Our study reveals the neural characteristics of smokers in high-altitude environments, highlighting the potential impact of smoking on brain function. These results provide new insights into the neural mechanisms of high-altitude smoking addiction and may inform the development of relevant intervention measures.

Neural mechanisms underlying placebo and nocebo effects in tonic muscle pain.

Pain is a highly subjective and multidimensional experience, significantly influenced by various psychological factors. Placebo analgesia and nocebo hyperalgesia exemplify this influence, where inert treatments result in pain relief or exacerbation, respectively. While extensive research has elucidated the psychological and neural mechanisms behind these effects, most studies have focused on transient pain stimuli. To explore these mechanisms in the context of tonic pain, we conducted a study using a 15-minute tonic muscle pain induction procedure, where hypertonic saline was infused into the left masseter of healthy participants. We collected real-time Visual Analogue Scale (VAS) scores and functional magnetic resonance imaging (fMRI) data during the induction of placebo analgesia and nocebo hyperalgesia via conditioned learning. Our findings revealed that placebo analgesia was more pronounced and lasted longer than nocebo hyperalgesia. Real-time pain ratings correlated significantly with neural activity in several brain regions. Notably, the putamen was implicated in both effects, while the caudate and other regions were differentially involved in placebo and nocebo effects. These findings confirm that the tonic muscle pain paradigm can be used to investigate the mechanisms of placebo and nocebo effects and indicate that placebo analgesia and nocebo hyperalgesia may have more distinct than common neural bases.

A review of brain structural and functional changes using MRI technology in patients who received bariatric surgery.

According to the World Health Organization, obesity is one of the most significant health issues currently because it increases risk for type 2 diabetes and cancer, heart disease, bone health, reproduction, and quality of living and it impacts approximately 500 million adults worldwide. This review analyzed the existing literature focusing on the effects of Metabolic and bariatric surgeries (MBS), including Roux-en-Y gastric bypass and sleeve gastrectomy on changes in brain function and anatomy using magnetic resonance imaging (MRI) technology. A PubMed search using the key words bariatric surgery and MRI conducted in December 2023 resulted in 544 articles. Our literature review identified 24 studies addressing neuroanatomic, neurophysiological, cognitive, and behavioral changes that occurred at different time intervals after different types of bariatric surgery. Our review of the literature found several reports indicating that MBS reverse neuroanatomic alterations and changes in functional connectivity associated with obesity. There were also reported improvements in cognitive performance, memory, executive function, attention, as well as decreased gustatory brain responses to food cues and resting state measures following bariatric surgery. There were instances of improved neural functioning associated with weight loss, suggesting that some neuroanatomic changes can be reversed following weight loss induced by bariatric surgery. Additionally, there were data suggesting that brain connectivity and metabolic health are improved following a bariatric surgical intervention. Together, the existing literature indicates an overall improvement in brain connectivity and health outcomes following bariatric surgery.

Frequency modulation increases the specificity of time-resolved connectivity: A resting-state fMRI study.

Representing data using time-resolved networks is valuable for analyzing functional data of the human brain. One commonly used method for constructing time-resolved networks from data is sliding window Pearson correlation (SWPC). One major limitation of SWPC is that it applies a high-pass filter to the activity time series. Therefore, if we select a short window (desirable to estimate rapid changes in connectivity), we will remove important low-frequency information. Here, we propose an approach based on single sideband modulation (SSB) in communication theory. This allows us to select shorter windows to capture rapid changes in the time-resolved functional network connectivity (trFNC). We use simulation and real resting-state functional magnetic resonance imaging (fMRI) data to demonstrate the superior performance of SSB+SWPC compared to SWPC. We also compare the recurring trFNC patterns between individuals with the first episode of psychosis (FEP) and typical controls (TC) and show that FEPs stay more in states that show weaker connectivity across the whole brain. A result exclusive to SSB+SWPC is that TCs stay more in a state with negative connectivity between subcortical and cortical regions. Based on all the results, we argue that SSB+SWPC is more sensitive for capturing temporal variation in trFNC.

Analyzing asymmetry in brain hierarchies with a linear state-space model of resting-state fMRI data.

This study challenges the traditional focus on zero-lag statistics in resting-state functional magnetic resonance imaging (rsfMRI) research. Instead, it advocates for considering time-lag interactions to unveil the directionality and asymmetries of the brain hierarchy. Effective connectivity (EC), the state matrix in dynamical causal modeling (DCM), is a commonly used metric for studying dynamical properties and causal interactions within a linear state-space system description. Here, we focused on how time-lag statistics are incorporated within the framework of DCM resulting in an asymmetric EC matrix. Our approach involves decomposing the EC matrix, revealing a steady-state differential cross-covariance matrix that is responsible for modeling information flow and introducing time-irreversibility. Specifically, the system's dynamics, influenced by the off-diagonal part of the differential covariance, exhibit a curl steady-state flow component that breaks detailed balance and diverges the dynamics from equilibrium. Our empirical findings indicate that the EC matrix's outgoing strengths correlate with the flow described by the differential cross covariance, while incoming strengths are primarily driven by zero-lag covariance, emphasizing conditional independence over directionality.

Modeling large-scale brain dynamics offers insight into the main principles of brain self-organization. In particular, the identification of traces of nonequilibrium steady-state dynamics also at the mascroscale level has been recently linked to the presence of intrinsic brain networks. Quantifying these aspects is generally limited by numerical difficulties. However, for resting-state BOLD data, a linear stochastic state-space model has demonstrated efficacy, simplifying analysis. Specifically, the asymmetric structure of effective connectivity, that is, the state interaction matrix, directly reflects nonequilibrium steady-state dynamics and time-irreversibility. By disentangling this asymmetry, we quantified departure from equilibrium and discerned primary directions of information propagation, identifying brain regions as sources or sinks.

Topological cluster statistic (TCS): Toward structural connectivity-guided fMRI cluster enhancement.

Functional magnetic resonance imaging (fMRI) studies most commonly use cluster-based inference to detect local changes in brain activity. Insufficient statistical power and disproportionate false-positive rates reportedly hinder optimal inference. We propose a structural connectivity-guided clustering framework, called topological cluster statistic (TCS), that enhances sensitivity by leveraging white matter anatomical connectivity information. TCS harnesses multimodal information from diffusion tractography and functional imaging to improve task fMRI activation inference. Compared to conventional approaches, TCS consistently improves power over a wide range of effects. This improvement results in a 10%-50% increase in local sensitivity with the greatest gains for medium-sized effects. TCS additionally enables inspection of underlying anatomical networks and thus uncovers knowledge regarding the anatomical underpinnings of brain activation. This novel approach is made available in the PALM software to facilitate usability. Given the increasing recognition that activation reflects widespread, coordinated processes, TCS provides a way to integrate the known structure underlying widespread activations into neuroimaging analyses moving forward.

Neuroimaging studies often encounter challenges in reliable inference of statistical maps due to limited statistical power. This article introduces TCS, a novel method that integrates anatomical connectivity data from diffusion tractography into cluster-based inference techniques. Our findings demonstrate that TCS enhances statistical power, improves the detection of spatially disjoint localized activations, and identifies the underlying network linking distant inferred active regions. By elucidating the coordinated network supporting inferred effects, TCS enables data-driven interpretation of inference results. The availability of TCS as a publicly accessible tool offers a promising avenue for future neuroimaging research to leverage anatomical connectivity for enhanced inference and interpretation.

The "limbic network," comprising orbitofrontal and anterior temporal cortex, is part of an extended default network: Evidence from multi-echo fMRI.

Resting-state functional magnetic resonance imaging (fMRI) investigations have provided a view of the default network (DN) as composed of a specific set of frontal, parietal, and temporal cortical regions. This spatial topography is typically defined with reference to an influential network parcellation scheme that designated the DN as one of seven large-scale networks (Yeo et al., 2011). However, the precise functional organization of the DN is still under debate, with studies arguing for varying subnetwork configurations and the inclusion of subcortical regions. In this vein, the so-called limbic network-defined as a distinct large-scale network comprising the bilateral temporal poles, ventral anterior temporal lobes, and orbitofrontal cortex-is of particular interest. A large multi-modal and multi-species literature on the anatomical, functional, and cognitive properties of these regions suggests a close relationship to the DN. Notably, these regions have poor signal quality with conventional fMRI acquisition, likely obscuring their network affiliation in most studies. Here, we leverage a multi-echo fMRI dataset with high temporal signal-to-noise and whole-brain coverage, including orbitofrontal and anterior temporal regions, to examine the large-scale network resting-state functional connectivity of these regions and assess their associations with the DN. Consistent with our hypotheses, our results support the inclusion of the majority of the orbitofrontal and anterior temporal cortex as part of the DN and reveal significant heterogeneity in their functional connectivity. We observed that left-lateralized regions within the temporal poles and ventral anterior temporal lobes, as well as medial orbitofrontal regions, exhibited the greatest resting-state functional connectivity with the DN, with heterogeneity across DN subnetworks. Overall, our findings suggest that, rather than being a functionally distinct network, the orbitofrontal and anterior temporal regions comprise part of a larger, extended default network.

The precise functional organization of the default network is still under debate. Limitations in temporal signal-to-noise of functional MRI BOLD signal data may have restricted estimations of the topography of the default network. The "limbic network," defined as a distinct large-scale network comprising bilateral anterior temporal and orbitofrontal cortex, has been affiliated with the default network in nonhuman animal tractography and task-based fMRI studies in humans. We leverage a multi-echo fMRI dataset with high temporal signal-to-noise and whole-brain coverage to examine the large-scale network resting-state functional connectivity of these regions and assess their associations with the default network. Our results support the inclusion of anterior temporal and orbitofrontal cortex as part of the default network. Overall, our findings suggest that, rather than being a functionally distinct limbic network, the anterior temporal and orbitofrontal regions comprise part of an extended default network.

A Bayesian incorporated linear non-Gaussian acyclic model for multiple directed graph estimation to study brain emotion circuit development in adolescence.

Emotion perception is essential to affective and cognitive development which involves distributed brain circuits. Emotion identification skills emerge in infancy and continue to develop throughout childhood and adolescence. Understanding the development of the brain's emotion circuitry may help us explain the emotional changes during adolescence. In this work, we aim to deepen our understanding of emotion-related functional connectivity (FC) from association to causation. We proposed a Bayesian incorporated linear non-Gaussian acyclic model (BiLiNGAM), which incorporated association model into the estimation pipeline. Simulation results indicated stable and accurate performance over various settings, especially when the sample size was small. We used fMRI data from the Philadelphia Neurodevelopmental Cohort (PNC) to validate the approach. It included 855 individuals aged 8-22 years who were divided into five different adolescent stages. Our network analysis revealed the development of emotion-related intra- and intermodular connectivity and pinpointed several emotion-related hubs. We further categorized the hubs into two types: in-hubs and out-hubs, as the center of receiving and distributing information, respectively. In addition, several unique developmental hub structures and group-specific patterns were discovered. Our findings help provide a directed FC template of brain network organization underlying emotion processing during adolescence.

Our study introduces a novel method for analyzing directed graphs across multiple groups and demonstrates its effectiveness through a series of simulation studies. This method is applied to investigate the development of directed functional connectivity for emotion processing across diverse adolescent periods. Our findings unveil a notable increase in interfunctional connectivity with age, specifically involved with the executive control and memory retrieval, indicating the maturation of emotion processing function. Additionally, significant development of intraconnectivity in the subcortical areas emerges in early adolescence, whereas development of cerebellum emerges in the very end of adolescence. These insights offer valuable contributions to our understanding of the dynamic neural processes underlying emotion regulation during adolescence.

Individual variability in neural representations of mind-wandering.

Mind-wandering is a frequent, daily mental activity, experienced in unique ways in each person. Yet neuroimaging evidence relating mind-wandering to brain activity, for example in the default mode network (DMN), has relied on population- rather than individual-based inferences owing to limited within-person sampling. Here, three densely sampled individuals each reported hundreds of mind-wandering episodes while undergoing multi-session functional magnetic resonance imaging. We found reliable associations between mind-wandering and DMN activation when estimating brain networks within individuals using precision functional mapping. However, the timing of spontaneous DMN activity relative to subjective reports, and the networks beyond DMN that were activated and deactivated during mind-wandering, were distinct across individuals. Connectome-based predictive modeling further revealed idiosyncratic, whole-brain functional connectivity patterns that consistently predicted mind-wandering within individuals but did not fully generalize across individuals. Predictive models of mind-wandering and attention that were derived from larger-scale neuroimaging datasets largely failed when applied to densely sampled individuals, further highlighting the need for personalized models. Our work offers novel evidence for both conserved and variable neural representations of self-reported mind-wandering in different individuals. The previously unrecognized interindividual variations reported here underscore the broader scientific value and potential clinical utility of idiographic approaches to brain-experience associations.

While everyone experiences that their mind "wanders" throughout daily life, the content and form of inner experience is different in different people. In this study, we found that brain activity representing mind-wandering is different in each person, reflecting unique mental experiences. While people consistently engaged the brain's default mode network (DMN) during mind-wandering, there were inconsistencies in the way that the DMN was engaged and in the other networks throughout the brain that were engaged. Our study highlights that personalized approaches, which require that individuals are sampled more densely than is common in current practice, enable accurate insights into relationships between brain activity and inner experience.

Linking the neural signature of response time variability to Alzheimer's disease pathology and cognitive functioning.

Promising evidence has suggested potential links between mind-wandering and Alzheimer's disease (AD). Yet, older adults with diagnosable neurocognitive disorders show reduced meta-awareness, thus questioning the validity of probe-assessed mind-wandering in older adults. In prior work, we employed response time variability as an objective, albeit indirect, marker of mind-wandering to identify patterns of functional connectivity that predicted mind-wandering. In the current study, we evaluated the association of this connectome-based, mind-wandering model with cerebral spinal fluid (CSF) p-tau/Aß 42 ratio in 289 older adults from the Alzheimer's Disease NeuroImaging Initiative (ADNI). Moreover, we examined if this model was similarly associated with individual differences in composite measures of global cognition, episodic memory, and executive functioning. Edges from the high response time variability model were significantly associated with CSF p-tau/Aß ratio. Furthermore, connectivity strength within edges associated with high response time variability was negatively associated with global cognition and episodic memory functioning. This study provides the first empirical support for a link between an objective neuromarker of mind-wandering and AD pathophysiology. Given the observed association between mind-wandering and cognitive functioning in older adults, interventions targeted at reducing mind-wandering, particularly before the onset of AD pathogenesis, may make a significant contribution to the prevention of AD-related cognitive decline.

Response time variability is considered an objective, albeit indirect, marker of mind-wandering. In this study, we applied a previously-derived connectome-based model of response time variability to resting-state data obtained from 289 older adults in the Alzheimer's Disease NeuroImaging Initiative. The network strength of the high response time variability model was correlated with a cerebrospinal fluid (CSF)-based ratiometric measure of amyloid and tau pathology. Additionally, our results demonstrated that the network strength in the high response time variability model was also linked with global cognition and episodic memory. This study provides the first empirical support for the association between a neuromarker of response time variability­an indirect marker of mind-wandering­and AD pathophysiology.

Adult auditory brain responses to nestling begging calls in seasonal songbirds: an fMRI study in non-parenting male and female starlings (Sturnus vulgaris).

The present study aims to investigate whether begging calls elicit specific auditory responses in non-parenting birds, whether these responses are influenced by the hormonal status of the bird, and whether they reflect biparental care for offspring in the European starling (Sturnus vulgaris). An fMRI experiment was conducted to expose non-parenting male and female European starlings to recordings of conspecific nestling begging calls during both artificially induced breeding and non-breeding seasons. This response was compared with their reaction to conspecific individual warbling song motifs and artificial pure tones, serving as social species-specific and artificial control stimuli, respectively. Our findings reveal that begging calls evoke a response in non-parenting male and female starlings, with significantly higher responsiveness observed in the right Field L and the Caudomedial Nidopallium (NCM), regardless of season or sex. Moreover, a significant seasonal variation in auditory brain responses was elicited in both sexes exclusively by begging calls, not by the applied control stimuli, within a ventral midsagittal region of NCM. This heightened response to begging calls, even in non-parenting birds, in the right primary auditory system (Field L), and the photoperiod induced hormonal neuromodulation of auditory responses to offspring's begging calls in the secondary auditory system (NCM), bears resemblance to mammalian responses to hunger calls. This suggests a convergent evolution aimed at facilitating swift adult responses to such calls crucial for offspring survival.

An In-depth Exploration of the Interplay between fMRI Methods and Theory in Cognitive Neuroscience.

Functional magnetic resonance imaging (fMRI) has been a cornerstone of cognitive neuroscience since its invention in the 1990s. The methods that we use for fMRI data analysis allow us to test different theories of the brain, thus different analyses can lead us to different conclusions about how the brain produces cognition. There has been a centuries-long debate about the nature of neural processing, with some theories arguing for functional specialization or localization (e.g., face and scene processing) while other theories suggest that cognition is implemented in distributed representations across many neurons and brain regions. Importantly, these theories have received support via different types of analyses; therefore, having students implement hands-on data analysis to explore the results of different fMRI analyses can allow them to take a firsthand approach to thinking about highly influential theories in cognitive neuroscience. Moreover, these explorations allow students to see that there are not clearcut "right" or "wrong" answers in cognitive neuroscience, rather we effectively instantiate assumptions within our analytical approaches that can lead us to different conclusions. Here, I provide Python code that uses freely available software and data to teach students how to analyze fMRI data using traditional activation analysis and machine-learning-based multivariate pattern analysis (MVPA). Altogether, these resources help teach students about the paramount importance of methodology in shaping our theories of the brain, and I believe they will be helpful for introductory undergraduate courses, graduate-level courses, and as a first analysis for people working in labs that use fMRI.

Genetic associations with neural reward responsivity to food cues in children.

Objective: To test associations of candidate obesity-related single nucleotide polymorphisms (SNPs) and obesity polygenic risk scores (PRS) with neural reward reactivity to food cues. Methods: After consuming a pre-load meal, 9-12-year-old children completed a functional magnetic resonance imaging (fMRI) paradigm with exposure to food and non-food commercials. Genetic exposures included FTO rs9939609, MC4R rs571312, and a pediatric-specific obesity PRS. A targeted region-of-interest (ROI) analysis for 7 bilateral reward regions and a whole-brain analysis were conducted. Independent associations between each genetic factor and reward responsivity to food cues in each ROI were evaluated using linear models. Results: Analyses included 151 children (M = 10.9 years). Each FTO rs9939609 obesity risk allele was related to a higher food-cue-related response in the right lateral hypothalamus after controlling for covariates including the current BMI Z-score (p < 0.01), however, the association did not remain significant after applying the multiple testing correction. MC4R rs571312 and the PRS were not related to heightened food-cue-related reward responsivity in any examined regions. The whole-brain analysis did not identify additional regions of food-cue-related response related to the examined genetic factors. Conclusion: Children genetically at risk for obesity, as indicated by the FTO genotype, may be predisposed to higher food-cue-related reward responsivity in the lateral hypothalamus in the sated state, which, in turn, could contribute to overconsumption. Clinical trial registration: https://clinicaltrials.gov/study/NCT03766191, identifier NCT03766191.

Neuroimaging and the Investigation of Drug-Drug Interactions Involving Psychedelics.

Psychedelic therapies are an emerging class of treatments in psychiatry with great potential, however relatively little is known about their interactions with other commonly used psychiatric medications. As psychedelic therapies become more widespread and move closer to the clinic, they likely will need to be integrated into existing treatment models which may include one or more traditional pharmacological therapies, meaning an awareness of potential drug-drug interactions will become vital. This commentary outlines some of the issues surrounding the study of drug-drug interactions of this type, provides a summary of some of the relevant key results to date, and charts a way forward which relies crucially on multimodal neuroimaging investigations. Studies in humans which combine Positron Emission Tomography (PET) and functional Magnetic Resonance Imaging (fMRI), plus ancillary measures, are likely to provide the most comprehensive assessment of drug-drug interactions involving psychedelics and the relevant effects at multiple levels of the drug response (molecular, functional, and clinical).