Identifying subgroups of eating behavior traits unrelated to obesity using functional connectivity and feature representation learning.

Eating behavior is highly heterogeneous across individuals and cannot be fully explained using only the degree of obesity. We utilized unsupervised machine learning and functional connectivity measures to explore the heterogeneity of eating behaviors measured by a self-assessment instrument using 424 healthy adults (mean ± standard deviation [SD] age = 47.07 ± 18.89 years; 67% female). We generated low-dimensional representations of functional connectivity using resting-state functional magnetic resonance imaging and estimated latent features using the feature representation capabilities of an autoencoder by nonlinearly compressing the functional connectivity information. The clustering approaches applied to latent features identified three distinct subgroups. The subgroups exhibited different levels of hunger traits, while their body mass indices were comparable. The results were replicated in an independent dataset consisting of 212 participants (mean ± SD age = 38.97 ± 19.80 years; 35% female). The model interpretation technique of integrated gradients revealed that the between-group differences in the integrated gradient maps were associated with functional reorganization in heteromodal association and limbic cortices and reward-related subcortical structures such as the accumbens, amygdala, and caudate. The cognitive decoding analysis revealed that these systems are associated with reward- and emotion-related systems. Our findings provide insights into the macroscopic brain organization of eating behavior-related subgroups independent of obesity.

Diagnosis-informed connectivity subtyping discovers subgroups of autism with reproducible symptom profiles.

Clinical heterogeneity has been one of the main barriers to develop effective biomarkers and therapeutic strategies in autism spectrum disorder (ASD). Recognizing this challenge, much effort has been made in recent neuroimaging studies to find biologically more homogeneous subgroups (called 'neurosubtypes') in autism. However, most approaches have rarely evaluated how much the employed features in subtyping represent the core anomalies of ASD, obscuring its utility in actual clinical diagnosis. To address this, we combined two data-driven methods, 'connectome-based gradient' and 'functional random forest', collectively allowing to discover reproducible neurosubtypes based on resting-state functional connectivity profiles that are specific to ASD. Indeed, the former technique provides the features (as input for subtyping) that effectively summarize whole-brain connectome variations in both normal and ASD conditions, while the latter leverages a supervised random forest algorithm to inform diagnostic labels to clustering, which makes neurosubtyping driven by the features of ASD core anomalies. Applying this framework to the open-sharing Autism Brain Imaging Data Exchange repository data (discovery, n = 103/108 for ASD/typically developing [TD]; replication, n = 44/42 for ASD/TD), we found three dominant subtypes of functional gradients in ASD and three subtypes in TD. The subtypes in ASD revealed distinct connectome profiles in multiple brain areas, which are associated with different Neurosynth-derived cognitive functions previously implicated in autism studies. Moreover, these subtypes showed different symptom severity, which degree co-varies with the extent of functional gradient changes observed across the groups. The subtypes in the discovery and replication datasets showed similar symptom profiles in social interaction and communication domains, confirming a largely reproducible brain-behavior relationship. Finally, the connectome gradients in ASD subtypes present both common and distinct patterns compared to those in TD, reflecting their potential overlap and divergence in terms of developmental mechanisms involved in the manifestation of large-scale functional networks. Our study demonstrated a potential of the diagnosis-informed subtyping approach in developing a clinically useful brain-based classification system for future ASD research.

Whole-brain functional connectivity correlates of obesity phenotypes.

Dysregulated neural mechanisms in reward and somatosensory circuits result in an increased appetitive drive for and reduced inhibitory control of eating, which in turn causes obesity. Despite many studies investigating the brain mechanisms of obesity, the role of macroscale whole-brain functional connectivity remains poorly understood. Here, we identified a neuroimaging-based functional connectivity pattern associated with obesity phenotypes by using functional connectivity analysis combined with machine learning in a large-scale (n ~ 2,400) dataset spanning four independent cohorts. We found that brain regions containing the reward circuit positively associated with obesity phenotypes, while brain regions for sensory processing showed negative associations. Our study introduces a novel perspective for understanding how the whole-brain functional connectivity correlates with obesity phenotypes. Furthermore, we demonstrated the generalizability of our findings by correlating the functional connectivity pattern with obesity phenotypes in three independent datasets containing subjects of multiple ages and ethnicities. Our findings suggest that obesity phenotypes can be understood in terms of macroscale whole-brain functional connectivity and have important implications for the obesity neuroimaging community.

The effects of high-frequency repetitive transcranial magnetic stimulation on resting-state functional connectivity in obese adults.

AIMS: We conducted a 4-week randomized, sham-controlled, single-blind, parallel-group trial to examine the effect of repetitive transcranial magnetic stimulation (rTMS) delivered to the left dorsolateral prefrontal cortex (DLPFC) on functional brain connectivity and body weight in adults with obesity. MATERIALS AND METHODS: Of the 45 volunteers with obesity, aged between 18 and 70 years (body mass index [BMI] ≥25 kg/m2 according to the obesity criterion for an Asian population), 36 participants (54.1 ± 11.0 years, BMI 30.2 ± 3.5 kg/m2 , 77.8% female) completed the 4 weeks of follow-up, undergoing two resting state fMRI scans (20 in the real stimulation group and 16 in the sham stimulation group). A total of eight sessions of high-frequency rTMS targeting the left DLPFC were provided over a period of 4 weeks (5-second trains with 25-second inter-train intervals, 10 Hz, 110% motor threshold; 2000 pulses over 20 minutes). RESULTS: Participants in the real stimulation group showed significantly greater weight loss from baseline following the eight session of rTMS (-2.53 ± 2.41 kg vs 0.38 ± 1.13 kg, P < 0.01). For intrinsic brain connectivity comparisons, the between-ness centrality values within the right frontoparietal network tended to increase with rTMS, and a significant interaction effect was identified for time (pre vs post) × rTMS (real vs sham) in the right frontoparietal network (P = 0.031, FDR corrected). CONCLUSIONS: We observed that rTMS selectively increased resting state functional connectivity within the right frontoparietal network. Our findings suggest that high-frequency rTMS to the left DLPFC might strengthen the frontoparietal network that orchestrates top-down inhibitory control to reduce food intake.

A shifting role of thalamocortical connectivity in the emergence of cortical functional organization.

The cortical patterning principle has been a long-standing question in neuroscience, yet how this translates to macroscale functional specialization in the human brain remains largely unknown. Here we examine age-dependent differences in resting-state thalamocortical connectivity to investigate its role in the emergence of large-scale functional networks during early life, using a primarily cross-sectional but also longitudinal approach. We show that thalamocortical connectivity during infancy reflects an early differentiation of sensorimotor networks and genetically influenced axonal projection. This pattern changes in childhood, when connectivity is established with the salience network, while decoupling externally and internally oriented functional systems. A developmental simulation using generative network models corroborated these findings, demonstrating that thalamic connectivity contributes to developing key features of the mature brain, such as functional segregation and the sensory-association axis, especially across 12-18 years of age. Our study suggests that the thalamus plays an important role in functional specialization during development, with potential implications for studying conditions with compromised internal and external processing.

Waiting impulsivity in progressive supranuclear palsy-Richardson's syndrome.

Background: Waiting impulsivity in progressive supranuclear palsy-Richardson's syndrome (PSP-RS) is difficult to assess, and its regulation is known to involve nucleus accumbens (NAc) subregions. We investigated waiting impulsivity using the "jumping the gun" (JTG) sign, which is defined as premature initiation of clapping before the start signal in the three-clap test and compared clinical features of PSP-RS patients with and without the sign and analyzed neural connectivity and microstructural changes in NAc subregions. Materials and methods: A positive JTG sign was defined as the participant starting to clap before the start sign in the three-clap test. We classified participants into the JTG positive (JTG +) and JTG negative (JTG-) groups and compared their clinical features, microstructural changes, and connectivity between NAc subregions using diffusion tension imaging. The NAc was parcellated into core and shell subregions using data-driven connectivity-based methods. Results: Seventy-seven patients with PSP-RS were recruited, and the JTG + group had worse frontal lobe battery (FAB) scores, more frequent falls, and more occurrence of the applause sign than the JTG- group. A logistic regression analysis revealed that FAB scores were associated with a positive JTG sign. The mean fiber density between the right NAc core and right medial orbitofrontal gyrus was higher in the JTG + group than the JTG- group. Discussion: We show that the JTG sign is a surrogate marker of waiting impulsivity in PSP-RS patients. Our findings enrich the current literature by deepening our understanding of waiting impulsivity in PSP patients and introducing a novel method for its evaluation.

In-vivo data-driven parcellation of Heschl's gyrus using structural connectivity.

The human auditory cortex around Heschl's gyrus (HG) exhibits diverging patterns across individuals owing to the heterogeneity of its substructures. In this study, we investigated the subregions of the human auditory cortex using data-driven machine-learning techniques at the individual level and assessed their structural and functional profiles. We studied an openly accessible large dataset of the Human Connectome Project and identified the subregions of the HG in humans using data-driven clustering techniques with individually calculated imaging features of cortical folding and structural connectivity information obtained via diffusion magnetic resonance imaging tractography. We characterized the structural and functional profiles of each HG subregion according to the cortical morphology, microstructure, and functional connectivity at rest. We found three subregions. The first subregion (HG1) occupied the central portion of HG, the second subregion (HG2) occupied the medial-posterior-superior part of HG, and the third subregion (HG3) occupied the lateral-anterior-inferior part of HG. The HG3 exhibited strong structural and functional connectivity to the association and paralimbic areas, and the HG1 exhibited a higher myelin density and larger cortical thickness than other subregions. A functional gradient analysis revealed a gradual axis expanding from the HG2 to the HG3. Our findings clarify the individually varying structural and functional organization of human HG subregions and provide insights into the substructures of the human auditory cortex.

Inter-individual body mass variations relate to fractionated functional brain hierarchies.

Variations in body mass index (BMI) have been suggested to relate to atypical brain organization, yet connectome-level substrates of BMI and their neurobiological underpinnings remain unclear. Studying 325 healthy young adults, we examined associations between functional connectivity and inter-individual BMI variations. We utilized non-linear connectome manifold learning techniques to represent macroscale functional organization along continuous hierarchical axes that dissociate low level and higher order brain systems. We observed an increased differentiation between unimodal and heteromodal association networks in individuals with higher BMI, indicative of a disrupted modular architecture and hierarchy of the brain. Transcriptomic decoding and gene enrichment analyses identified genes previously implicated in genome-wide associations to BMI and specific cortical, striatal, and cerebellar cell types. These findings illustrate functional connectome substrates of BMI variations in healthy young adults and point to potential molecular associations.

The orbitofrontal cortex functionally links obesity and white matter hyperintensities.

Many studies have linked dysfunction in cognitive control-related brain regions with obesity and the burden of white matter hyperintensities (WMHs). This study aimed to explore how functional connectivity differences in the brain are associated with WMH burden and degree of obesity using resting-state functional magnetic resonance imaging (fMRI) in 182 participants. Functional connectivity measures were compared among four different groups: (1) low WMH burden, non-obese; (2) low WMH burden, obese; (3) high WMH burden, non-obese; and (4) high WMH burden, obese. At a large-scale network-level, no networks showed significant interaction effects, but the frontoparietal network showed a main effect of degree of obesity. At a finer node level, the orbitofrontal cortex showed interaction effects between periventricular WMH burden and degree of obesity. Higher functional connectivity was observed when the periventricular WMH burden and degree of obesity were both high. These results indicate that the functional connectivity of the orbitofrontal cortex is affected by the mutual interaction between the periventricular WMHs and degree of obesity. Our results suggest that this region links obesity with WMHs in terms of functional connectivity.

Multivariate association between brain function and eating disorders using sparse canonical correlation analysis.

Eating disorder is highly associated with obesity and it is related to brain dysfunction as well. Still, the functional substrates of the brain associated with behavioral traits of eating disorder are underexplored. Existing neuroimaging studies have explored the association between eating disorder and brain function without using all the information provided by the eating disorder related questionnaire but by adopting summary factors. Here, we aimed to investigate the multivariate association between brain function and eating disorder at fine-grained question-level information. Our study is a retrospective secondary analysis that re-analyzed resting-state functional magnetic resonance imaging of 284 participants from the enhanced Nathan Kline Institute-Rockland Sample database. Leveraging sparse canonical correlation analysis, we associated the functional connectivity of all brain regions and all questions in the eating disorder questionnaires. We found that executive- and inhibitory control-related frontoparietal networks showed positive associations with questions of restraint eating, while brain regions involved in the reward system showed negative associations. Notably, inhibitory control-related brain regions showed a positive association with the degree of obesity. Findings were well replicated in the independent validation dataset (n = 34). The results of this study might contribute to a better understanding of brain function with respect to eating disorder.

FuNP (Fusion of Neuroimaging Preprocessing) Pipelines: A Fully Automated Preprocessing Software for Functional Magnetic Resonance Imaging.

The preprocessing of functional magnetic resonance imaging (fMRI) data is necessary to remove unwanted artifacts and transform the data into a standard format. There are several neuroimaging data processing tools that are widely used, such as SPM, AFNI, FSL, FreeSurfer, Workbench, and fMRIPrep. Different data preprocessing pipelines yield differing results, which might reduce the reproducibility of neuroimaging studies. Here, we developed a preprocessing pipeline for T1-weighted structural MRI and fMRI data by combining components of well-known software packages to fully incorporate recent developments in MRI preprocessing into a single coherent software package. The developed software, called FuNP (Fusion of Neuroimaging Preprocessing) pipelines, is fully automatic and provides both volume- and surface-based preprocessing pipelines with a user-friendly graphical interface. The reliability of the software was assessed by comparing resting-state networks (RSNs) obtained using FuNP with pre-defined RSNs using open research data (n = 90). The obtained RSNs were well-matched with the pre-defined RSNs, suggesting that the pipelines in FuNP are reliable. In addition, image quality metrics (IQMs) were calculated from the results of three different software packages (i.e., FuNP, FSL, and fMRIPrep) to compare the quality of the preprocessed data. We found that our FuNP outperformed other software in terms of temporal characteristics and artifacts removal. We validated our pipeline with independent local data (n = 28) in terms of IQMs. The IQMs of our local data were similar to those obtained from the open research data. The codes for FuNP are available online to help researchers.

Spatially guided functional correlation tensor: A new method to associate body mass index and white matter neuroimaging.

Obesity causes critical health problems including cardiovascular disease, diabetes, and stroke. Various neuroimaging methods including diffusion tensor imaging (DTI) are used to explore white matter (WM) alterations in obesity. The functional correlation tensor (FCT) is a method to simulate DTI in WM using resting-state functional magnetic resonance imaging (rs-fMRI). In this study, we enhanced the FCT with additional anatomical information from T1-weighted data in a regression framework. The goal was to 1) develop a spatially guided enhanced FCT (s-eFCT) and to 2) use it to identify imaging biomarkers for obesity. We computed fractional anisotropy (FA) and the mean diffusivity (MD) from the s-eFCT. The regional FA and MD values that can explain body mass index (BMI) well were chosen. The identified regional FA and MD values were used to predict BMI values. The correlation between real and predicted BMIs was 0.57. There was no significant correlation between real and predicted DTI using the MD. The BMI predicted using FA was used to classify participants into three obesity subgroups. The classification accuracy was 57.20%. In summary, we found potential imaging biomarkers of obesity based on the s-eFCT.

Synthesizing diffusion tensor imaging from functional MRI using fully convolutional networks.

PURPOSE: Medical image synthesis can simulate a target modality of interest based on existing modalities and has the potential to save scanning time while contributing to efficient data collection. This study proposed a three-dimensional (3D) deep learning architecture based on a fully convolutional network (FCN) to synthesize diffusion-tensor imaging (DTI) from resting-state functional magnetic resonance imaging (fMRI). METHODS: fMRI signals derived from white matter (WM) exist and can be used for assessing WM alterations. We constructed an initial functional correlation tensor image using the correlation patterns of adjacent fMRI voxels as one input to the FCN. We considered T1-weighted images as an additional input to provide an algorithm with the structural information needed to synthesize DTI. Our architecture was trained and tested using a large-scale open database dataset (training n = 648; testing n = 293). RESULTS: The average correlation value between synthesized and actual diffusion tensors for 38 WM regions was 0.808, which significantly improves upon an existing study (r = 0.480). We also validated our approach using two open databases. Our proposed method showed a higher correlation with the actual diffusion tensor than the conventional machine-learning method for many WM regions. CONCLUSIONS: Our method synthesized DTI images from fMRI images using a 3D FCN architecture. We hope to expand our method of synthesizing various other imaging modalities from a single image source.