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Low-dimensional representations are increasingly used to study meaningful organizational principles within the human brain. Most notably, the sensorimotor-association axis consistently explains the most variance in the human connectome as its so-called principal gradient, suggesting that it represents a fundamental organizational principle. While recent work indicates these low dimensional representations are relatively robust, they are limited by modeling only certain aspects of the functional connectivity structure. To date, the majority of studies have restricted these approaches to the strongest connections in the brain, treating weaker or negative connections as noise despite evidence of meaningful structure among them. The present work examines connectivity gradients of the human connectome across a full range of connectivity strengths and explores the implications for outcomes of individual differences, identifying potential dependencies on thresholds and opportunities to improve prediction tasks. Interestingly, the sensorimotor-association axis emerged as the principal gradient of the human connectome across the entire range of connectivity levels. Moreover, the principal gradient of connections at intermediate strengths encoded individual differences, better followed individual-specific anatomical features, and was also more predictive of intelligence. Taken together, our results add to evidence of the sensorimotor-association axis as a fundamental principle of the brain's functional organization, since it is evident even in the connectivity structure of more lenient connectivity thresholds. These more loosely coupled connections further appear to contain valuable and potentially important information that could be used to improve our understanding of individual differences, diagnosis, and the prediction of treatment outcomes., Competing Interests: Declaration of Competing Interest None., (Copyright © 2023. Published by Elsevier Inc.)

The Brain Imaging Data Structure (BIDS) is a specification accompanied by a software ecosystem that was designed to create reproducible and automated workflows for processing neuroimaging data. BIDS Apps flexibly build workflows based on the metadata detected in a dataset. However, even BIDS valid metadata can include incorrect values or omissions that result in inconsistent processing across sessions. Additionally, in large-scale, heterogeneous neuroimaging datasets, hidden variability in metadata is difficult to detect and classify. To address these challenges, we created a Python-based software package titled "Curation of BIDS" (CuBIDS), which provides an intuitive workflow that helps users validate and manage the curation of their neuroimaging datasets. CuBIDS includes a robust implementation of BIDS validation that scales to large samples and incorporates DataLad--a version control software package for data--as an optional dependency to ensure reproducibility and provenance tracking throughout the entire curation process. CuBIDS provides tools to help users perform quality control on their images' metadata and identify unique combinations of imaging parameters. Users can then execute BIDS Apps on a subset of participants that represent the full range of acquisition parameters that are present, accelerating pipeline testing on large datasets., Competing Interests: Declaration of Competing Interest The authors of Curation of BIDS (CuBIDS) declare that they have no competing or conflicting interests., (Copyright © 2022. Published by Elsevier Inc.)

While classically linked to memory, the hippocampus is also a feeding behavior modulator due to its multiple interconnected pathways with other brain regions and expression of receptors for metabolic hormones. Here we tested whether variations in insulin sensitivity would be correlated with differential brain activation following exposure to palatable food cues, as well as with variations in implicit food memory in a cohort of healthy adolescents, some of whom were born small for gestational age (SGA). Homeostatic Model Assessment of Insulin Resistance (HOMA-IR) was positively correlated with activation in the cuneus, and negatively correlated with activation in the middle frontal lobe, superior frontal gyrus and precuneus when presented with palatable food images versus non-food images in healthy adolescents. Additionally, HOMA-IR and insulinemia were higher in participants with impaired food memory. SGA individuals had higher snack caloric density and greater chance for impaired food memory. There was also an interaction between the HOMA-IR and birth weight ratio influencing external eating behavior. We suggest that diminished insulin sensitivity correlates with activation in visual attention areas and inactivation in inhibitory control areas in healthy adolescents. Insulin resistance also associated with less consistency in implicit memory for a consumed meal, which may suggest lower ability to establish a dietary pattern, and can contribute to obesity. Differences in feeding behavior in SGA individuals were associated with insulin sensitivity and hippocampal alterations, suggesting that cognition and hormonal regulation are important components involved in their food intake modifications throughout life., (Copyright © 2021 Elsevier Ltd. All rights reserved.)

New large neuroimaging studies, such as the Adolescent Brain Cognitive Development study (ABCD) and Human Connectome Project (HCP) Development studies are adopting a new T1-weighted imaging sequence with prospective motion correction (PMC) in favor of the more traditional 3-Dimensional Magnetization-Prepared Rapid Gradient-Echo Imaging (MPRAGE) sequence. Here, we used a developmental dataset (ages 5-21, N = 348) from the Healthy Brain Network (HBN) Initiative to directly compare two widely used MRI structural sequences: one based on the Human Connectome Project (MPRAGE) and another based on the ABCD study (MPRAGE+PMC). We aimed to determine if the morphometric measurements obtained from both protocols are equivalent or if one sequence has a clear advantage over the other. The sequences were also compared through quality control measurements. Inter- and intra-sequence reliability were assessed with another set of participants (N = 71) from HBN that performed two MPRAGE and two MPRAGE+PMC sequences within the same imaging session, with one MPRAGE (MPRAGE1) and MPRAGE+PMC (MPRAGE+PMC1) pair at the beginning of the session and another pair (MPRAGE2 and MPRAGE+PMC2) at the end of the session. Intraclass correlation coefficients (ICC) scores for morphometric measurements such as volume and cortical thickness showed that intra-sequence reliability is the highest with the two MPRAGE+PMC sequences and lowest with the two MPRAGE sequences. Regarding inter-sequence reliability, ICC scores were higher for the MPRAGE1 - MPRAGE+PMC1 pair at the beginning of the session than the MPRAGE1 - MPRAGE2 pair, possibly due to the higher motion artifacts in the MPRAGE2 run. Results also indicated that the MPRAGE+PMC sequence is robust, but not impervious, to high head motion. For quality control metrics, the traditional MPRAGE yielded better results than MPRAGE+PMC in 5 of the 8 measurements. In conclusion, morphometric measurements evaluated here showed high inter-sequence reliability between the MPRAGE and MPRAGE+PMC sequences, especially in images with low head motion. We suggest that studies targeting hyperkinetic populations use the MPRAGE+PMC sequence, given its robustness to head motion and higher reliability scores. However, neuroimaging researchers studying non-hyperkinetic participants can choose either MPRAGE or MPRAGE+PMC sequences, but should carefully consider the apparent tradeoff between relatively increased reliability, but reduced quality control metrics when using the MPRAGE+PMC sequence., Competing Interests: Declaration of Competing Interest All authors declare that they do not have any conflicts of interest., (Copyright © 2020. Published by Elsevier Inc.)

Multilayer network models have been proposed as an effective means of capturing the dynamic configuration of distributed neural circuits and quantitatively describing how communities vary over time. Beyond general insights into brain function, a growing number of studies have begun to employ these methods for the study of individual differences. However, test-retest reliabilities for multilayer network measures have yet to be fully quantified or optimized, potentially limiting their utility for individual difference studies. Here, we systematically evaluated the impact of multilayer community detection algorithms, selection of network parameters, scan duration, and task condition on test-retest reliabilities of multilayer network measures (i.e., flexibility, integration, and recruitment). A key finding was that the default method used for community detection by the popular generalized Louvain algorithm can generate erroneous results. Although available, an updated algorithm addressing this issue is yet to be broadly adopted in the neuroimaging literature. Beyond the algorithm, the present work identified parameter selection as a key determinant of test-retest reliability; however, optimization of these parameters and expected reliabilities appeared to be dataset-specific. Once parameters were optimized, consistent with findings from the static functional connectivity literature, scan duration was a much stronger determinant of reliability than scan condition. When the parameters were optimized and scan duration was sufficient, both passive (i.e., resting state, Inscapes, and movie) and active (i.e., flanker) tasks were reliable, although reliability in the movie watching condition was significantly higher than in the other three tasks. The minimal data requirement for achieving reliable measures for the movie watching condition was 20 min, and 30 min for the other three tasks. Our results caution the field against the use of default parameters without optimization based on the specific datasets to be employed - a process likely to be limited for most due to the lack of test-retest samples to enable parameter optimization., (Copyright © 2020. Published by Elsevier Inc.)

A common coordinate space enabling comparison across individuals is vital to understanding human brain organization and individual differences. By leveraging dimensionality reduction algorithms, high-dimensional fMRI data can be represented in a low-dimensional space to characterize individual features. Such a representative space encodes the functional architecture of individuals and enables the observation of functional changes across time. However, determining comparable functional features across individuals in resting-state fMRI in a way that simultaneously preserves individual-specific connectivity structure can be challenging. In this work we propose scalable joint embedding to simultaneously embed multiple individual brain connectomes within a common space that allows individual representations across datasets to be aligned. Using Human Connectome Project data, we evaluated the joint embedding approach by comparing it to the previously established orthonormal alignment model. Alignment using joint embedding substantially increased the similarity of functional representations across individuals while simultaneously capturing their distinct profiles, allowing individuals to be more discriminable from each other. Additionally, we demonstrated that the common space established using resting-state fMRI provides a better overlap of task-activation across participants. Finally, in a more challenging scenario - alignment across a lifespan cohort aged from 6 to 85 - joint embedding provided a better prediction of age (r2 = 0.65) than the prior alignment model. It facilitated the characterization of functional trajectories across lifespan. Overall, these analyses establish that joint embedding can simultaneously capture individual neural representations in a common connectivity space aligning functional data across participants and populations and preserve individual specificity., Competing Interests: Declaration of Competing Interest None., (Copyright © 2020. Published by Elsevier Inc.)

Background: The A3669G single nucleotide polymorphism (SNP) of the glucocorticoid receptor (GR) gene NR3C1 is associated with altered tissue sensitivity to glucocorticoids (GCs). GCs modulate the food reward circuitry and are implicated in increased intake of palatable foods, which can lead to the metabolic syndrome and obesity. We hypothesized that presence of the G variant of the A3669G SNP would affect preferences for palatable foods and alter metabolic, behavioural, and neural outcomes., Methods: One hundred thirty-one adolescents were genotyped for the A3669G polymorphism, underwent anthropometric assessment and nutritional evaluations, and completed behavioural measures. A subsample of 74 subjects was followed for 5 years and performed a brain functional magnetic resonance imaging (fMRI) paradigm to verify brain activity in response to food cues., Results: Sugar and total energy consumption were lower in A3669G G allele variant carriers. On follow-up, this group also had reduced serum insulin concentrations, increased insulin sensitivity, and lower anxiety scores. Because of our unbalanced sample sizes (31/37 participants non-G allele carriers/total), our imaging data analysis failed to find whole brain-corrected significant results in between-group t-tests., Conclusion: These results highlight that a genetic variation in the GR gene is associated, at the cellular level, with significant reduction in GC sensitivity, which, at cognitive and behavioural levels, translates to altered food intake and emotional stress response. This genetic variant might play a major role in decreasing risk for metabolic and psychiatric diseases., (Copyright © 2017 Elsevier Ltd. All rights reserved.)

Addition of fertilisers is a common practice in nursery production of conifer seedlings. The aim of this study was to evaluate whether ectomycorrhizal (ECM) fungi can be an alternative to the use of chemical fertilisers in the nursery production of Pinus pinaster. A greenhouse nursery experiment was conducted by inoculating seedlings obtained from seeds of P. pinaster plus trees with a range of compatible ECM fungi: (1) Thelephora terrestris, (2) Rhizopogon vulgaris, (3) a mixture of Pisolithus tinctorius and Scleroderma citrinum, and (4) a mixture of Suillus bovinus, Laccaria laccata and Lactarius deterrimus, using forest soil as substrate. Plant development was assessed at two levels of N-P-K fertiliser (0 or 600 mg/seedling). Inoculation with a mixture of mycelium from S. bovinus, L. laccata and L. deterrimus and with a mixture of spores of P. tinctorius and S. citrinum improved plant growth and nutrition, without the need of fertiliser. Results indicate that selected ECM fungi can be a beneficial biotechnological tool in nursery production of P. pinaster., (Copyright © 2010 Elsevier Ltd. All rights reserved.)

One of the most consistent electrophysiological deficits reported in the schizophrenia literature is the failure to inhibit, or properly gate, the neuronal response to the second stimulus of an identical pair (i.e., sensory gating). Although animal and invasive human studies have consistently implicated the auditory cortex, prefrontal cortex and hippocampus in mediating the sensory gating response, localized activation in these structures has not always been reported during non-invasive imaging modalities. In the current experiment, event-related FMRI and a variant of the traditional gating paradigm were utilized to examine how the gating network differentially responded to the processing of pairs of identical and non-identical tones. Two single-tone conditions were also presented so that they could be used to estimate the HRF for paired stimuli, reconstructed based on actual hemodynamic responses, to serve as a control non-gating condition. Results supported an emerging theory that the gating response for both paired-tone conditions was primarily mediated by auditory and prefrontal cortex, with potential contributions from the thalamus. Results also indicated that the left auditory cortex may play a preferential role in determining the stimuli that should be inhibited (gated) or receive further processing due to novelty of information. In contrast, there was no evidence of hippocampal involvement, suggesting that future work is needed to determine what role it may play in the gating response.