A deeper understanding of neurobiological basis of disease is key to mental health research and clinical practice. This could lead to more targeted treatments, improved survival rates, and better ...outcomes. This is the challenge now undertaken by scientists around the globe who study the human brain in health and disease to identify brain systems involved in clinical syndromes. Several well-powered magnetic resonance imaging (MRI) studies have established the existence of differences in brain structure in several groups of patients compared to healthy individuals.
Recent technological advances in network analyses allowing examination of whole-brain connectivity are moving the field forward, providing evidence that major psychiatric disorders arise from perturbations in a complex network of highly connected, anatomically distributed neural systems rather than dysfunctions of circumscribed brain regions.
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Objective
Functional neuroimaging studies have found differential neural activation patterns during anticipation‐related paradigms in participants with eating disorders (EDs) compared to controls. ...However, publications reported conflicting results on the directionality and location of the abnormal activations. There is an urgent need to integrate our existing knowledge of anticipation, both rewarding and aversive, to elucidate these differences.
Method
We conducted an activation likelihood estimation (ALE) meta‐analysis to quantitatively review functional neuroimaging studies that evaluated differences between brain correlates of anticipation in participants with and without disordered eating. PubMed, Web of Sciences, PsycINFO, Medline and EMBASE were searched for studies published up to November 2022. Exploratory sub‐analyses to check for differences between reward and non‐reward anticipation among all anticipation paradigms.
Results
Twenty‐one references met the inclusion criteria for meta‐analysis. The meta‐analysis across anticipation all tasks identified a significant hyperactivation cluster in the right putamen in participants with disordered eating (n = 17 experiments) and a significant hypoactivation cluster in the left inferior parietal lobule (n = 13 experiments), in participants with disordered eating compared to controls.
Conclusions
These findings and sub‐analyses of reward‐ and non‐reward‐related cues suggest potential pathophysiological mechanisms underlying anticipatory responses to rewarding and aversive cues in ED.
Key points
The ALE meta‐analysis showed altered anticipatory brain responses in participants with disordered eating compared to controls.
One cluster of significant hyperactivation was identified in the right putamen; another cluster of hypoactivation was found in the left inferior parietal lobule.
Anticipatory brain responses to rewarding and aversive cues are altered in participants with disordered eating.
Machine learning is increasingly being applied to neuroimaging data. However, most machine learning algorithms have not been designed to accommodate neuroimaging data, which typically has many more ...data points than subjects, in addition to multicollinearity and low signal-to-noise. Consequently, the relative efficacy of different machine learning regression algorithms for different types of neuroimaging data are not known. Here, we sought to quantify the performance of a variety of machine learning algorithms for use with neuroimaging data with various sample sizes, feature set sizes, and predictor effect sizes. The contribution of additional machine learning techniques – embedded feature selection and bootstrap aggregation (bagging) – to model performance was also quantified. Five machine learning regression methods – Gaussian Process Regression, Multiple Kernel Learning, Kernel Ridge Regression, the Elastic Net and Random Forest, were examined with both real and simulated MRI data, and in comparison to standard multiple regression. The different machine learning regression algorithms produced varying results, which depended on sample size, feature set size, and predictor effect size. When the effect size was large, the Elastic Net, Kernel Ridge Regression and Gaussian Process Regression performed well at most sample sizes and feature set sizes. However, when the effect size was small, only the Elastic Net made accurate predictions, but this was limited to analyses with sample sizes greater than 400. Random Forest also produced a moderate performance for small effect sizes, but could do so across all sample sizes. Machine learning techniques also improved prediction accuracy for multiple regression. These data provide empirical evidence for the differential performance of various machines on neuroimaging data, which are dependent on number of sample size, features and effect size.
•The choice of machine learning algorithm influenced prediction accuracy.•Sample size was important: prediction accuracy generally increased once N ≥ 400.•The Elastic Net performed well at a range of effect sizes, relative to other methods.•Random Forest performed well at small effect sizes.•Gaussian Process Regression performed well at large effect sizes.
Neuroimaging data have been widely used to understand the neural bases of human behaviors. However, most studies were either based on a few predefined regions of interest or only able to reveal ...limited vital regions, hence not providing an overarching description of the relationship between neuroimaging and behaviors. Here, we proposed a voxel‐based pattern regression that not only could investigate the overall brain‐associated variance (BAV) for a given behavioral measure but could also evaluate the shared neural bases between different behaviors across multiple neuroimaging data. The proposed method demonstrated consistently high reliability and accuracy through comprehensive simulations. We further implemented this approach on real data of adolescents (IMAGEN project, n = 2089) and adults (HCP project, n = 808) to investigate brain‐based variances of multiple behavioral measures, for instance, cognitive behaviors, substance use, and psychiatric disorders. Notably, intelligence‐related scores showed similar high BAVs with the gray matter volume across both datasets. Further, our approach allows us to reveal the latent brain‐based correlation across multiple behavioral measures, which are challenging to obtain otherwise. For instance, we observed a shared brain architecture underlying depression and externalizing problems in adolescents, while the symptom comorbidity may only emerge later in adults. Overall, our approach will provide an important statistical tool for understanding human behaviors using neuroimaging data.
We proposed an MRI‐based strategy that could investigate the overall brain‐associated variance (BAV) for a given behavioral measure and evaluate the shared neural bases (i.e., neuroimaging correlation) between different traits. Using large adolescent and adult neuroimaging datasets, we estimated different levels of BAVs and latent brain‐based correlations across multiple phenotypes.
•Developed a model-based reproducibility index for large-scale high-throughput MRI-based studies.•Provided an analytical tool to evaluate the sample size necessary for achieving a desirable ...model-based reproducibility.•Model-based reproducibility >0.99 was observed for a few large sample size analyses.•Both sample size and study-specific experimental factors play important roles in model-based reproducibility assessment.
Magnetic Resonance Imaging (MRI) technology has been increasingly used in neuroscience studies. Reproducibility of statistically significant findings generated by MRI-based studies, especially association studies (phenotype vs. MRI metric) and task-induced brain activation, has been recently heavily debated. However, most currently available reproducibility measures depend on thresholds for the test statistics and cannot be use to evaluate overall study reproducibility. It is also crucial to elucidate the relationship between overall study reproducibility and sample size in an experimental design. In this study, we proposed a model-based reproducibility index to quantify reproducibility which could be used in large-scale high-throughput MRI-based studies including both association studies and task-induced brain activation. We performed the model-based reproducibility assessments for a few association studies and task-induced brain activation by using several recent large sMRI/fMRI databases. For large sample size association studies between brain structure/function features and some basic physiological phenotypes (i.e. Sex, BMI), we demonstrated that the model-based reproducibility of these studies is more than 0.99. For MID task activation, similar results could be observed. Furthermore, we proposed a model-based analytical tool to evaluate minimal sample size for the purpose of achieving a desirable model-based reproducibility. Additionally, we evaluated the model-based reproducibility of gray matter volume (GMV) changes for UK Biobank (UKB) vs. Parkinson Progression Marker Initiative (PPMI) and UK Biobank (UKB) vs. Human Connectome Project (HCP). We demonstrated that both sample size and study-specific experimental factors play important roles in the model-based reproducibility assessments for different experiments. In summary, a systematic assessment of reproducibility is fundamental and important in the current large-scale high-throughput MRI-based studies.
Mental disorders represent an increasing personal and financial burden and yet treatment development has stagnated in recent decades. Current disease classifications do not reflect psychobiological ...mechanisms of psychopathology, nor the complex interplay of genetic and environmental factors, likely contributing to this stagnation. Ten years ago, the longitudinal IMAGEN study was designed to comprehensively incorporate neuroimaging, genetics, and environmental factors to investigate the neural basis of reinforcement-related behavior in normal adolescent development and psychopathology. In this article, we describe how insights into the psychobiological mechanisms of clinically relevant symptoms obtained by innovative integrative methodologies applied in IMAGEN have informed our current and future research aims. These aims include the identification of symptom groups that are based on shared psychobiological mechanisms and the development of markers that predict disease course and treatment response in clinical groups. These improvements in precision medicine will be achieved, in part, by employing novel methodological tools that refine the biological systems we target. We will also implement our approach in low- and medium-income countries to understand how distinct environmental, socioeconomic, and cultural conditions influence the development of psychopathology. Together, IMAGEN and related initiatives strive to reduce the burden of mental disorders by developing precision medicine approaches globally.
Chronic peer victimization has long-term impacts on mental health; however, the biological mediators of this adverse relationship are unknown. We sought to determine whether adolescent brain ...development is involved in mediating the effect of peer victimization on psychopathology. We included participants (n = 682) from the longitudinal IMAGEN study with both peer victimization and neuroimaging data. Latent profile analysis identified groups of adolescents with different experiential patterns of victimization. We then associated the victimization trajectories and brain volume changes with depression, generalized anxiety, and hyperactivity symptoms at age 19. Repeated measures ANOVA revealed time-by-victimization interactions on left putamen volume (F = 4.38, p = 0.037). Changes in left putamen volume were negatively associated with generalized anxiety (t = -2.32, p = 0.020). Notably, peer victimization was indirectly associated with generalized anxiety via decreases in putamen volume (95% CI = 0.004-0.109). This was also true for the left caudate (95% CI = 0.002-0.099). These data suggest that the experience of chronic peer victimization during adolescence might induce psychopathology-relevant deviations from normative brain development. Early peer victimization interventions could prevent such pathological changes.
Moment-to-moment reaction time variability on tasks of attention, often quantified by intra-individual response variability (IRV), provides a good indication of the degree to which an individual is ...vulnerable to lapses in sustained attention. Increased IRV is a hallmark of several disorders of attention, including Attention-Deficit/Hyperactivity Disorder (ADHD). Here, task-based fMRI was used to provide the first examination of how average brain activation and functional connectivity patterns in adolescents are related to individual differences in sustained attention as measured by IRV. We computed IRV in a large sample of adolescents (n = 758) across 'Go' trials of a Stop Signal Task (SST). A data-driven, multi-step analysis approach was used to identify networks associated with low IRV (i.e., good sustained attention) and high IRV (i.e., poorer sustained attention). Low IRV was associated with greater functional segregation (i.e., stronger negative connectivity) amongst an array of brain networks, particularly between cerebellum and motor, cerebellum and prefrontal, and occipital and motor networks. In contrast, high IRV was associated with stronger positive connectivity within the motor network bilaterally and between motor and parietal, prefrontal, and limbic networks. Consistent with these observations, a separate sample of adolescents exhibiting elevated ADHD symptoms had increased fMRI activation and stronger positive connectivity within the same motor network denoting poorer sustained attention, compared to a matched asymptomatic control sample. With respect to the functional connectivity signature of low IRV, there were no statistically significant differences in networks denoting good sustained attention between the ADHD symptom group and asymptomatic control group. We propose that sustained attentional processes are facilitated by an array of neural networks working together, and provide an empirical account of how the functional role of the cerebellum extends to cognition in adolescents. This work highlights the involvement of motor cortex in the integrity of sustained attention, and suggests that atypically strong connectivity within motor networks characterizes poor attentional capacity in both typically developing and ADHD symptomatic adolescents.
Replicating results (i.e. obtaining consistent results using a new independent dataset) is an essential part of good science. As replicability has consequences for theories derived from empirical ...studies, it is of utmost importance to better understand the underlying mechanisms influencing it. A popular tool for non-invasive neuroimaging studies is functional magnetic resonance imaging (fMRI). While the effect of underpowered studies is well documented, the empirical assessment of the interplay between sample size and replicability of results for task-based fMRI studies remains limited. In this work, we extend existing work on this assessment in two ways. Firstly, we use a large database of 1400 subjects performing four types of tasks from the IMAGEN project to subsample a series of independent samples of increasing size. Secondly, replicability is evaluated using a multi-dimensional framework consisting of 3 different measures: (un)conditional test-retest reliability, coherence and stability. We demonstrate not only a positive effect of sample size, but also a trade-off between spatial resolution and replicability. When replicability is assessed voxelwise or when observing small areas of activation, a larger sample size than typically used in fMRI is required to replicate results. On the other hand, when focussing on clusters of voxels, we observe a higher replicability. In addition, we observe variability in the size of clusters of activation between experimental paradigms or contrasts of parameter estimates within these.
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