Low-calorie diet-induced weight loss is associated with altered brain connectivity and food desire in obesity.

OBJECTIVE: The main objective of this study is to better understand the effects of diet-induced weight loss on brain connectivity in response to changes in glucose levels in individuals with obesity. METHODS: A total of 25 individuals with obesity, among whom 9 had a diagnosis of type 2 diabetes, underwent functional magnetic resonance imaging (fMRI) scans before and after an 8-week low-calorie diet. We used a two-step hypereuglycemia clamp approach to mimic the changes in glucose levels observed in the postprandial period in combination with task-mediated fMRI intrinsic connectivity distribution (ICD) analysis. RESULTS: After the diet, participants lost an average of 3.3% body weight. Diet-induced weight loss led to a decrease in leptin levels, an increase in hunger and food intake, and greater brain connectivity in the parahippocampus, right hippocampus, and temporal cortex (limbic-temporal network). Group differences (with vs. without type 2 diabetes) were noted in several brain networks. Connectivity in the limbic-temporal and frontal-parietal brain clusters inversely correlated with hunger. CONCLUSIONS: A short-term low-calorie diet led to a multifaceted body response in patients with obesity, with an increase in connectivity in the limbic-temporal network (emotion and memory) and hormone and eating behavior changes that may be important for recovering the weight lost.

Multimodal identification of the mouse brain using simultaneous Ca 2+ imaging and fMRI.

Individual differences in neuroimaging are of interest to clinical and cognitive neuroscientists based on their potential for guiding the personalized treatment of various heterogeneous neurological conditions and diseases. Despite many advantages, the workhorse in this arena, BOLD (blood-oxygen-level-dependent) functional magnetic resonance imaging (fMRI) suffers from low spatiotemporal resolution and specificity as well as a propensity for noise and spurious signal corruption. To better understand individual differences in BOLD-fMRI data, we can use animal models where fMRI, alongside complementary but more invasive contrasts, can be accessed. Here, we apply simultaneous wide-field fluorescence calcium imaging and BOLD-fMRI in mice to interrogate individual differences using a connectome-based identification framework adopted from the human fMRI literature. This approach yields high spatiotemporal resolution cell-type specific signals (here, from glia, excitatory, as well as inhibitory interneurons) from the whole cortex. We found mouse multimodal connectome-based identification to be successful and explored various features of these data.

Screen time, sleep, brain structural neurobiology, and sequential associations with child and adolescent psychopathology: Insights from the ABCD study.

Background and Aims: The precise roles of screen media activity (SMA) and sleep problems in relation to child/adolescent psychopathology remain ambiguous. We investigated temporal relationships among sleep problems, SMA, and psychopathology and potential involvement of thalamus-prefrontal-cortex (PFC)-brainstem structural covariation. Methods: This study utilized data from the Adolescent Brain Cognitive Development study (n = 4,641 ages 9-12) at baseline, Year1, and Year2 follow-up. Cross-Lagged Panel Models (CLPMs) investigated reciprocal predictive relationships between sleep duration/problems, SMA, and psychopathology symptoms. A potential mediating role of baseline Thalamus-PFC-brainstem covariation on SMA-externalizing relationships was examined. Results: Participants were divided into discovery (n = 2,359, 1,054 girls) and replication (n = 2,282, 997 girls) sets. CLPMs showed 1) bidirectional associations between sleep duration and SMA in late childhood, with higher frequency SMA predicting shorter sleep duration (ß = -0.10 [95%CI: -0.16, -0.03], p = 0.004) and vice versa (ß = -0.11 [95%CI: -0.18, -0.05], p < 0.001); 2) externalizing symptoms at age 10-11 predicting sleep problems (ß = 0.11 [95%CI: 0.04, 0.19], p = 0.002), SMA (ß = 0.07 [95%CI: 0.01, 0.13], p = 0.014), and internalizing symptoms (ß = 0.09 [95%CI: 0.05, 0.13], p < 0.001) at age 11-12; and 3) externalizing behavior at age 10-11 partially mediating the relationship between baseline thalamus-PFC-brainstem covariation and SMA at age 11-12 (indirect effect = 0.032 [95%CI: 0.003, 0.067], p-value = 0.030). Findings were replicable. Conclusion: We found bi-directional SMA-sleep-duration associations in late childhood. Externalizing symptoms preceded future SMA and sleep disturbances and partially mediated relationships between structural brain covariation and SMA. The findings emphasize the need for understanding individual differences and developing and implementing integrated strategies addressing both sleep concerns and screen time to mitigate potential impacts on psychopathology.

Constrained alternating minimization for parameter mapping (CAMP).

OBJECTIVE: To improve image quality in highly accelerated parameter mapping by incorporating a linear constraint that relates consecutive images. APPROACH: In multi-echo T1 or T2 mapping, scan time is often shortened by acquiring undersampled but complementary measures of k-space at each TE or TI. However, residual undersampling artifacts from the individual images can then degrade the quality of the final parameter maps. In this work, a new reconstruction method, dubbed Constrained Alternating Minimization for Parameter mapping (CAMP), is introduced. This method simultaneously extracts T2 or T1* maps in addition to an image for each TE or TI from accelerated datasets, leveraging the constraints of the decay to improve the reconstructed image quality. The model enforces exponential decay through a linear constraint, resulting in a biconvex objective function that lends itself to alternating minimization. The method was tested in four in vivo volunteer experiments and validated in phantom studies and healthy subjects, using T2 and T1 mapping, with accelerations of up to 12. MAIN RESULTS: CAMP is demonstrated for accelerated radial and Cartesian acquisitions in T2 and T1 mapping. The method is even applied to generate an entire T2 weighted image series from a single TSE dataset, despite the blockwise k-space sampling at each echo time. Experimental undersampled phantom and in vivo results processed with CAMP exhibit reduced artifacts without introducing bias. SIGNIFICANCE: For a wide array of applications, CAMP linearizes the model cost function without sacrificing model accuracy so that the well-conditioned and highly efficient reconstruction algorithm improves the image quality of accelerated parameter maps.

Practical utilization of nonlinear spatial encoding: Fast field mapping and FRONSAC-wave.

PURPOSE: To study the additional value of FRONSAC encoding in 2D and 3D wave sequences, implementing a simple strategy to trajectory mapping for FRONSAC encoding gradients. THEORY AND METHODS: The nonlinear gradient trajectory for each voxel was estimated by exploiting the sparsity of the point spread function in the frequency domain. Simulations and in-vivo experiments were used to analyze the performance of combinations of wave and FRONSAC encoding. RESULTS: Field mapping using the simplified approach produced similar image quality with much shorter calibration time than the comprehensive mapping schemes utilized in previous work. In-vivo human brain images showed that the addition of FRONSAC encoding could improve wave image quality, particularly at very high undersampling factors and in the context of limited wave amplitudes. These results were further supported by g-factor maps. CONCLUSION: Results show that FRONSAC can be used to improve image quality of wave at very high undersampling rates or in slew-limited acquisitions. Our study illustrates the potential of the proposed fast field mapping approach.

Whole-brain functional connectivity predicts groupwise and sex-specific tau PET in preclincal Alzheimer's disease.

Preclinical Alzheimer's disease, characterized by the initial accumulation of amyloid and tau pathologies without symptoms, presents a critical opportunity for early intervention. Yet, the interplay between these pathological markers and the functional connectome during this window remains understudied. We therefore set out to elucidate the relationship between the functional connectome and amyloid and tau, as assessed by PET imaging, in individuals with preclinical AD using connectome-based predictive modeling (CPM). We found that functional connectivity predicts tau PET, outperforming amyloid PET models. These models were predominantly governed by linear relationships between functional connectivity and tau. Tau models demonstrated a stronger correlation to global connectivity than underlying tau PET. Furthermore, we identify sex-based differences in the ability to predict regional tau, without any underlying differences in tau PET or global connectivity. Taken together, these results suggest tau is more closely coupled to functional connectivity than amyloid in preclinical disease, and that multimodal predictive modeling approaches stand to identify unique relationships that any one modality may be insufficient to discern.

Investigating brain dynamics and their association with cognitive control in opioid use disorder using naturalistic and drug cue paradigms.

Objectives: Opioid use disorder (OUD) impacts millions of people worldwide. The prevalence and debilitating effects of OUD present a pressing need to understand its neural mechanisms to provide more targeted interventions. Prior studies have linked altered functioning in large-scale brain networks with clinical symptoms and outcomes in OUD. However, these investigations often do not consider how brain responses change over time. Time-varying brain network engagement can convey clinically relevant information not captured by static brain measures. Methods: We investigated brain dynamic alterations in individuals with OUD by applying a new multivariate computational framework to movie-watching (i.e., naturalistic; N=76) and task-based (N=70) fMRI. We further probed the associations between cognitive control and brain dynamics during a separate drug cue paradigm in individuals with OUD. Results: Compared to healthy controls (N=97), individuals with OUD showed decreased variability in the engagement of recurring brain states during movie-watching. We also found that worse cognitive control was linked to decreased variability during the rest period when no opioid-related stimuli were present. Conclusions: These findings suggest that individuals with OUD may experience greater difficulty in effectively engaging brain networks in response to evolving internal or external demands. Such inflexibility may contribute to aberrant response inhibition and biased attention toward opioid-related stimuli, two hallmark characteristics of OUD. By incorporating temporal information, the current study introduces novel information about how brain dynamics are altered in individuals with OUD and their behavioral implications.

Encoding scheme design for gradient-free, nonlinear projection imaging using Bloch-Siegert RF spatial encoding in a low-field, open MRI system.

Eliminating conventional pulsed B0-gradient coils for magnetic resonance imaging (MRI) can significantly reduce the cost of and increase access to these devices. Phase shifts induced by the Bloch-Siegert shift effect have been proposed as a means for gradient-free, RF spatial encoding for low-field MR imaging. However, nonlinear phasor patterns like those generated from loop coils have not been systematically studied in the context of 2D spatial encoding. This work presents an optimization algorithm to select an efficient encoding trajectory among the nonlinear patterns achievable with a given hardware setup. Performance of encoding trajectories or projections was evaluated through simulated and experimental image reconstructions. Results show that the encodings schemes designed by this algorithm provide more efficient spatial encoding than comparison encoding sets, and the method produces images with the predicted spatial resolution and minimal artifacts. Overall, the work demonstrates the feasibility of performing 2D gradient-free, low-field imaging using the Bloch-Siegert shift which is an important step towards creating low-cost, point-of-care MR systems.

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

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

Low-Field, Low-Cost, Point-of-Care Magnetic Resonance Imaging.

Low-field magnetic resonance imaging (MRI) has recently experienced a renaissance that is largely attributable to the numerous technological advancements made in MRI, including optimized pulse sequences, parallel receive and compressed sensing, improved calibrations and reconstruction algorithms, and the adoption of machine learning for image postprocessing. This new attention on low-field MRI originates from a lack of accessibility to traditional MRI and the need for affordable imaging. Low-field MRI provides a viable option due to its lack of reliance on radio-frequency shielding rooms, expensive liquid helium, and cryogen quench pipes. Moreover, its relatively small size and weight allow for easy and affordable installation in most settings. Rather than replacing conventional MRI, low-field MRI will provide new opportunities for imaging both in developing and developed countries. This article discusses the history of low-field MRI, low-field MRI hardware and software, current devices on the market, advantages and disadvantages, and low-field MRI's global potential. Expected final online publication date for the Annual Review of Biomedical Engineering, Volume 26 is May 2024. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Impact of postnatal weight gain on brain white matter maturation in very preterm infants.

BACKGROUND AND PURPOSE: Very preterm infants (VPIs, <32 weeks gestational age at birth) are prone to long-term neurological deficits. While the effects of birth weight and postnatal growth on VPIs' neurological outcome are well established, the neurobiological mechanism behind these associations remains elusive. In this study, we utilized diffusion tensor imaging (DTI) to characterize how birth weight and postnatal weight gain influence VPIs' white matter (WM) maturation. METHODS: We included VPIs with complete birth and postnatal weight data in their health record, and DTI scan as part of their predischarge Magnetic Resonance Imaging (MRI). We conducted voxel-wise general linear model and tract-based regression analyses to explore the impact of birth weight and postnatal weight gain on WM maturation. RESULTS: We included 91 VPIs in our analysis. After controlling for gestational age at birth and time between birth and scan, higher birth weight Z-scores were associated with DTI markers of more mature WM tracts, most prominently in the corpus callosum and sagittal striatum. The postnatal weight Z-score changes over the first 4 weeks of life were also associated with increased maturity in these WM tracts, when controlling for gestational age at birth, birth weight Z-score, and time between birth and scan. CONCLUSIONS: In VPIs, birth weight and post-natal weight gain are associated with markers of brain WM maturation, particularly in the corpus callosum, which can be captured on discharge MRI. These neuroimaging metrics can serve as potential biomarkers for the early effects of nutritional interventions on VPIs' brain development.

Connectome-based machine learning models are vulnerable to subtle data manipulations.

Neuroimaging-based predictive models continue to improve in performance, yet a widely overlooked aspect of these models is "trustworthiness," or robustness to data manipulations. High trustworthiness is imperative for researchers to have confidence in their findings and interpretations. In this work, we used functional connectomes to explore how minor data manipulations influence machine learning predictions. These manipulations included a method to falsely enhance prediction performance and adversarial noise attacks designed to degrade performance. Although these data manipulations drastically changed model performance, the original and manipulated data were extremely similar (r = 0.99) and did not affect other downstream analysis. Essentially, connectome data could be inconspicuously modified to achieve any desired prediction performance. Overall, our enhancement attacks and evaluation of existing adversarial noise attacks in connectome-based models highlight the need for counter-measures that improve the trustworthiness to preserve the integrity of academic research and any potential translational applications.

Brain responses to nutrients are severely impaired and not reversed by weight loss in humans with obesity: a randomized crossover study.

Post-ingestive nutrient signals to the brain regulate eating behaviour in rodents, and impaired responses to these signals have been associated with pathological feeding behaviour and obesity. To study this in humans, we performed a single-blinded, randomized, controlled, crossover study in 30 humans with a healthy body weight (females N = 12, males N = 18) and 30 humans with obesity (females N = 18, males N = 12). We assessed the effect of intragastric glucose, lipid and water (noncaloric isovolumetric control) infusions on the primary endpoints cerebral neuronal activity and striatal dopamine release, as well as on the secondary endpoints plasma hormones and glucose, hunger scores and caloric intake. To study whether impaired responses in participants with obesity would be partially reversible with diet-induced weight loss, imaging was repeated after 10% diet-induced weight loss. We show that intragastric glucose and lipid infusions induce orosensory-independent and preference-independent, nutrient-specific cerebral neuronal activity and striatal dopamine release in lean participants. In contrast, participants with obesity have severely impaired brain responses to post-ingestive nutrients. Importantly, the impaired neuronal responses are not restored after diet-induced weight loss. Impaired neuronal responses to nutritional signals may contribute to overeating and obesity, and ongoing resistance to post-ingestive nutrient signals after significant weight loss may in part explain the high rate of weight regain after successful weight loss.

Clinical Promise of Brain-Phenotype Modeling: A Review.

Importance: Assessing the link between whole-brain activity and individual differences in cognition and behavior has the potential to offer insights into psychiatric disorder etiology and change the practice of psychiatry, from diagnostic clarification to intervention. To this end, recent application of predictive modeling to link brain activity to phenotype has generated significant excitement, but clinical applications have largely not been realized. This Review explores explanations for the as yet limited practical utility of brain-phenotype modeling and proposes a path forward to fulfill this clinical potential. Observations: Clinical applications of brain-phenotype models are proposed and will require coordinated collaboration across the relatively siloed fields of psychometrics and computational neuroscience. Such interdisciplinary work will maximize the reliability and validity of modeled phenotypic measures, ensuring that resulting brain-based models are interpretable and useful. The models, in turn, may shed additional light on the neurobiological systems into which each phenotypic measure taps, permitting further phenotype refinement. Conclusions and Relevance: Together, these observations reflect an opportunity: bridging the divide between phenotypic measure development and validation and measure end use for brain-phenotype modeling holds the promise that each may inform the other, yielding more precise and useful brain-phenotype models. Such models can in turn be used to reveal the macroscale neural bases of a given phenotype, advancing basic neuroscientific understanding and identifying circuits that can be targeted (eg, via closed-loop neurofeedback or brain stimulation) to slow, reverse, or even prevent functional impairment.

Magnetic resonance imaging using a nonuniform Bo (NuBo) field-cycling magnet.

Magnetic resonance imaging (MRI) is a powerful noninvasive diagnostic tool with superior soft tissue contrast. However, access to MRI is limited since current systems depend on homogeneous, high field strength main magnets (B0-fields), with strong switchable gradients which are expensive to install and maintain. In this work we propose a new approach to MRI where imaging is performed in an inhomogeneous field using radiofrequency spatial encoding, thereby eliminating the need for uniform B0-fields and conventional cylindrical gradient coils. The proposed technology uses an innovative data acquisition and reconstruction approach by integrating developments in field cycling, parallel imaging and non-Fourier based algebraic reconstruction. The scanner uses field cycling to image in an inhomogeneous B0-field; in this way magnetization is maximized during the high field polarization phase, and B0 inhomogeneity effects are minimized by using a low field during image acquisition. In addition to presenting the concept, this work provides experimental verification of a long-lived spin echo signal, spatially varying resolution, as well as both simulated and experimental 2D images. Our initial design creates an open MR system that can be installed in a patient examination table for body imaging (e.g., breast or liver) or built into a wall for weighted-spine imaging. The proposed system introduces a new class of inexpensive, open, silent MRIs that could be housed in doctor's offices much like ultrasound is today, making MRI more widely accessible.

Association of Body Mass Index and Waist Circumference With Imaging Metrics of Brain Integrity and Functional Connectivity in Children Aged 9 to 10 Years in the US, 2016-2018.

Importance: Aside from widely known cardiovascular implications, higher weight in children may have negative associations with brain microstructure and neurodevelopment. Objective: To evaluate the association of body mass index (BMI) and waist circumference with imaging metrics that approximate brain health. Design, Setting, and Participants: This cross-sectional study used data from the Adolescent Brain Cognitive Development (ABCD) study to examine the association of BMI and waist circumference with multimodal neuroimaging metrics of brain health in cross-sectional and longitudinal analyses over 2 years. From 2016 to 2018, the multicenter ABCD study recruited more than 11 000 demographically representative children aged 9 to 10 years in the US. Children without any history of neurodevelopmental or psychiatric disorders were included in this study, and a subsample of children who completed 2-year follow-up (34%) was included for longitudinal analysis. Exposures: Children's weight, height, waist circumference, age, sex, race and ethnicity, socioeconomic status, handedness, puberty status, and magnetic resonance imaging scanner device were retrieved and included in the analysis. Main Outcomes and Measures: Association of preadolescents' BMI z scores and waist circumference with neuroimaging indicators of brain health: cortical morphometry, resting-state functional connectivity, and white matter microstructure and cytostructure. Results: A total of 4576 children (2208 [48.3%] female) at a mean (SD) age of 10.0 years (7.6 months) were included in the baseline cross-sectional analysis. There were 609 (13.3%) Black, 925 (20.2%) Hispanic, and 2565 (56.1%) White participants. Of those, 1567 had complete 2-year clinical and imaging information at a mean (SD) age of 12.0 years (7.7 months). In cross-sectional analyses at both time points, higher BMI and waist circumference were associated with lower microstructural integrity and neurite density, most pronounced in the corpus callosum (fractional anisotropy for BMI and waist circumference at baseline and second year: P < .001; neurite density for BMI at baseline: P < .001; neurite density for waist circumference at baseline: P = .09; neurite density for BMI at second year: P = .002; neurite density for waist circumference at second year: P = .05), reduced functional connectivity in reward- and control-related networks (eg, within the salience network for BMI and waist circumference at baseline and second year: P < .002), and thinner brain cortex (eg, for the right rostral middle frontal for BMI and waist circumference at baseline and second year: P < .001). In longitudinal analysis, higher baseline BMI was most strongly associated with decelerated interval development of the prefrontal cortex (left rostral middle frontal: P = .003) and microstructure and cytostructure of the corpus callosum (fractional anisotropy: P = .01; neurite density: P = .02). Conclusions and Relevance: In this cross-sectional study, higher BMI and waist circumference among children aged 9 to 10 years were associated with imaging metrics of poorer brain structure and connectivity as well as hindered interval development. Future follow-up data from the ABCD study can reveal long-term neurocognitive implications of excess childhood weight. Imaging metrics that had the strongest association with BMI and waist circumference in this population-level analysis may serve as target biomarkers of brain integrity in future treatment trials of childhood obesity.

Why is everyone talking about brain state?

The rapid and coordinated propagation of neural activity across the brain provides the foundation for complex behavior and cognition. Technical advances across neuroscience subfields have advanced understanding of these dynamics, but points of convergence are often obscured by semantic differences, creating silos of subfield-specific findings. In this review we describe how a parsimonious conceptualization of brain state as the fundamental building block of whole-brain activity offers a common framework to relate findings across scales and species. We present examples of the diverse techniques commonly used to study brain states associated with physiology and higher-order cognitive processes, and discuss how integration across them will enable a more comprehensive and mechanistic characterization of the neural dynamics that are crucial to survival but are disrupted in disease.

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

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

Functional brain networks reflect spatial and temporal autocorrelation.

High-throughput experimental methods in neuroscience have led to an explosion of techniques for measuring complex interactions and multi-dimensional patterns. However, whether sophisticated measures of emergent phenomena can be traced back to simpler, low-dimensional statistics is largely unknown. To explore this question, we examined resting-state functional magnetic resonance imaging (rs-fMRI) data using complex topology measures from network neuroscience. Here we show that spatial and temporal autocorrelation are reliable statistics that explain numerous measures of network topology. Surrogate time series with subject-matched spatial and temporal autocorrelation capture nearly all reliable individual and regional variation in these topology measures. Network topology changes during aging are driven by spatial autocorrelation, and multiple serotonergic drugs causally induce the same topographic change in temporal autocorrelation. This reductionistic interpretation of widely used complexity measures may help link them to neurobiology.

Population level multimodal neuroimaging correlates of attention-deficit hyperactivity disorder among children.

Objectives: Leveraging a large population-level morphologic, microstructural, and functional neuroimaging dataset, we aimed to elucidate the underlying neurobiology of attention-deficit hyperactivity disorder (ADHD) in children. In addition, we evaluated the applicability of machine learning classifiers to predict ADHD diagnosis based on imaging and clinical information. Methods: From the Adolescents Behavior Cognitive Development (ABCD) database, we included 1,798 children with ADHD diagnosis and 6,007 without ADHD. In multivariate logistic regression adjusted for age and sex, we examined the association of ADHD with different neuroimaging metrics. The neuroimaging metrics included fractional anisotropy (FA), neurite density (ND), mean-(MD), radial-(RD), and axial diffusivity (AD) of white matter (WM) tracts, cortical region thickness and surface areas from T1-MPRAGE series, and functional network connectivity correlations from resting-state fMRI. Results: Children with ADHD showed markers of pervasive reduced microstructural integrity in white matter (WM) with diminished neural density and fiber-tracks volumes - most notable in the frontal and parietal lobes. In addition, ADHD diagnosis was associated with reduced cortical volume and surface area, especially in the temporal and frontal regions. In functional MRI studies, ADHD children had reduced connectivity among default-mode network and the central and dorsal attention networks, which are implicated in concentration and attention function. The best performing combination of feature selection and machine learning classifier could achieve a receiver operating characteristics area under curve of 0.613 (95% confidence interval = 0.580-0.645) to predict ADHD diagnosis in independent validation, using a combination of multimodal imaging metrics and clinical variables. Conclusion: Our study highlights the neurobiological implication of frontal lobe cortex and associate WM tracts in pathogenesis of childhood ADHD. We also demonstrated possible potentials and limitations of machine learning models to assist with ADHD diagnosis in a general population cohort based on multimodal neuroimaging metrics.