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Eating behavior is highly heterogeneous across individuals, and thus, it 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. This study was conducted on 424 healthy adults. We generated low-dimensional representations of functional connectivity defined using the resting-state functional magnetic resonance imaging, and calculated 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 disinhibition and hunger traits; however, their body mass indices were comparable. The model interpretation technique of integrated gradients revealed that these distinctions were associated with the functional reorganization in higher-order associations and limbic networks and reward-related subcortical structures. The cognitive decoding analysis revealed that these systems are associated with reward- and emotion-related systems. We replicated our findings using an independent dataset, thereby suggesting generalizability. Our findings provide insights into the macroscopic brain organization of eating behavior-related subgroups independent of obesity.

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 (HG

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.

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

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.

Distinguishing the autism spectrum disorder (ASD) from typical control (TC) using resting-state functional magnetic resonance imaging (rs-fMRI) is very difficult because ASD has heterogenetic properties and induce small changes in the brain structure. Moreover, distinguishing ASD from TC using the data obtained from many sites is even more difficult because many factors might negatively affect the classification model leading to unstable results. This difficulty is especially true for existing rs-fMRI analysis methods such as functional connectivity analysis. Recent studies have shown better ASD classification performance using models constructed using recurrent neural network (RNN). However, a blinded application of RNN not considering the multi-site factors is sub-optimal. In this paper, we present an artificial neural network model inspired by the existing functional connectivity analysis modeling. Our model includes layers that play the role of spatial reduction, temporal feature extraction, and combining phenotypic data (inclusive of multisite data) for classifying ASD. We applied the cross validation framework to the multi-site rs-fMRI dataset to test the proposed model. Our best model showed an accuracy of 74.54%, which is superior to the existing functional connectivity analysis with an accuracy of 54.05%.

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.

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.