GAN-MAT: Generative adversarial network-based microstructural profile covariance analysis toolbox.

Multimodal magnetic resonance imaging (MRI) provides complementary information for investigating brain structure and function; for example, an in vivo microstructure-sensitive proxy can be estimated using the ratio between T1- and T2-weighted structural MRI. However, acquiring multiple imaging modalities is challenging in patients with inattentive disorders. In this study, we proposed a comprehensive framework to provide multiple imaging features related to the brain microstructure using only T1-weighted MRI. Our toolbox consists of (i) synthesizing T2-weighted MRI from T1-weighted MRI using a conditional generative adversarial network; (ii) estimating microstructural features, including intracortical covariance and moment features of cortical layer-wise microstructural profiles; and (iii) generating a microstructural gradient, which is a low-dimensional representation of the intracortical microstructure profile. We trained and tested our toolbox using T1- and T2-weighted MRI scans of 1,104 healthy young adults obtained from the Human Connectome Project database. We found that the synthesized T2-weighted MRI was very similar to the actual image and that the synthesized data successfully reproduced the microstructural features. The toolbox was validated using an independent dataset containing healthy controls and patients with episodic migraine as well as the atypical developmental condition of autism spectrum disorder. Our toolbox may provide a new paradigm for analyzing multimodal structural MRI in the neuroscience community and is openly accessible at https://github.com/CAMIN-neuro/GAN-MAT.

Whole-brain structural connectome asymmetry in autism.

Autism spectrum disorder is a common neurodevelopmental condition that manifests as a disruption in sensory and social skills. Although it has been shown that the brain morphology of individuals with autism is asymmetric, how this differentially affects the structural connectome organization of each hemisphere remains under-investigated. We studied whole-brain structural connectivity-based brain asymmetry in individuals with autism using diffusion magnetic resonance imaging obtained from the Autism Brain Imaging Data Exchange initiative. By leveraging dimensionality reduction techniques, we constructed low-dimensional representations of structural connectivity and calculated their asymmetry index. Comparing the asymmetry index between individuals with autism and neurotypical controls, we found atypical structural connectome asymmetry in the sensory and default-mode regions, particularly showing weaker asymmetry towards the right hemisphere in autism. Network communication provided topological underpinnings by demonstrating that the inferior temporal cortex and limbic and frontoparietal regions showed reduced global network communication efficiency and decreased send-receive network navigation in the inferior temporal and lateral visual cortices in individuals with autism. Finally, supervised machine learning revealed that structural connectome asymmetry could be used as a measure for predicting communication-related autistic symptoms and nonverbal intelligence. Our findings provide insights into macroscale structural connectome alterations in autism and their topological underpinnings.

Structural connectome alterations between individuals with autism and neurotypical controls using feature representation learning.

Autism spectrum disorder is one of the most common neurodevelopmental conditions associated with sensory and social communication impairments. Previous neuroimaging studies reported that atypical nodal- or network-level functional brain organization in individuals with autism was associated with autistic behaviors. Although dimensionality reduction techniques have the potential to uncover new biomarkers, the analysis of whole-brain structural connectome abnormalities in a low-dimensional latent space is underinvestigated. In this study, we utilized autoencoder-based feature representation learning for diffusion magnetic resonance imaging-based structural connectivity in 80 individuals with autism and 61 neurotypical controls that passed strict quality controls. We generated low-dimensional latent features using the autoencoder model for each group and adopted an integrated gradient approach to assess the contribution of the input data for predicting latent features during the encoding process. Subsequently, we compared the integrated gradient values between individuals with autism and neurotypical controls and observed differences within the transmodal regions and between the sensory and limbic systems. Finally, we identified significant associations between integrated gradient values and communication abilities in individuals with autism. Our findings provide insights into the whole-brain structural connectome in autism and may help identify potential biomarkers for autistic connectopathy.

Connectome-wide structure-function coupling models implicate polysynaptic alterations in autism.

Autism spectrum disorder (ASD) is one of the most common neurodevelopmental diagnoses. Although incompletely understood, structural and functional network alterations are increasingly recognized to be at the core of the condition. We utilized multimodal imaging and connectivity modeling to study structure-function coupling in ASD and probed mono- and polysynaptic mechanisms on structurally-governed network function. We examined multimodal magnetic resonance imaging data in 80 ASD and 61 neurotypical controls from the Autism Brain Imaging Data Exchange (ABIDE) II initiative. We predicted intrinsic functional connectivity from structural connectivity data in each participant using a Riemannian optimization procedure that varies the times that simulated signals can unfold along tractography-derived personalized connectomes. In both ASD and neurotypical controls, we observed improved structure-function prediction at longer diffusion time scales, indicating better modeling of brain function when polysynaptic mechanisms are accounted for. Prediction accuracy differences (∆prediction accuracy) were marked in transmodal association systems, such as the default mode network, in both neurotypical controls and ASD. Differences were, however, lower in ASD in a polysynaptic regime at higher simulated diffusion times. We compared regional differences in ∆prediction accuracy between both groups to assess the impact of polysynaptic communication on structure-function coupling. This analysis revealed that between-group differences in ∆prediction accuracy followed a sensory-to-transmodal cortical hierarchy, with an increased gap between controls and ASD in transmodal compared to sensory/motor systems. Multivariate associative techniques revealed that structure-function differences reflected inter-individual differences in autistic symptoms and verbal as well as non-verbal intelligence. Our network modeling approach sheds light on atypical structure-function coupling in autism, and suggests that polysynaptic network mechanisms are implicated in the condition and that these can help explain its wide range of associated symptoms.

Dynamic transitions of functional brain connectivity during a working memory task.

Working memory is a cognitive system that temporarily stores and manipulates information via attention. Although brain activity patterns related to working memory have been extensively studied, little is known about how the brain connectome organization dynamically changes while performing working memory tasks. Here, we systematically investigated dynamic changes in functional brain connectivity during a working memory task. We found that functional connectivity in the medial frontal and orbitofrontal cortices and the precuneus showed differences during the transitions between the rest and task. Our results provide topographic patterns of dynamic functional connectivity during the working memory tasks.Clinical Relevance- This study may provide macroscale topography related to working memory and foster establishing memory-related brain mechanisms, which could be applied to clinical neuroscience.

Atypical structural connectome asymmetry and associations with network communication in autism spectrum disorder.

Autism spectrum disorder is a common neurodevelopmental condition showing connectome disorganization in sensory and transmodal cortices. However, alterations in the inter-hemispheric asymmetry of structural connectome are remained to be investigated. Here, we studied structural connectome asymmetry in individuals with autism using dimensionality reduction techniques and assessed its topological underpinnings by associating with network communication measures. We found that the sensory and heteromodal association regions showed significant between-group differences in inter-hemispheric asymmetry between individuals with autism and neurotypical controls. In addition, the network communication ability was particularly altered between visual and limbic areas. Our findings provide insights for understanding structural connectome alteration in autism and its topological underpinnings.Clinical Relevance- This study provides insights into the understanding of atypical macroscale structural connectome organization in individuals with autism.

Immature Persimmon Suppresses Amyloid Beta (Aß) Mediated Cognitive Dysfunction via Tau Pathology in ICR Mice.

This study confirmed the ameliorating effect of immature persimmon (Diospyros kaki) ethanolic extract (IPEE) on neuronal cytotoxicity in amyloid beta (Aß)1-42-induced ICR mice. The administration of IPEE ameliorated the cognitive dysfunction in Aß1-42-induced mice by improving the spatial working memory, the short-term and long-term memory functions. IPEE protected the cerebral cholinergic system, such as the acetylcholine (ACh) level and acetylcholinesterase (AChE) activity, and antioxidant system, such as the superoxide dismutase (SOD), reduced glutathione (GSH) and malondialdehyde (MDA) contents. In addition, mitochondrial dysfunction against Aß1-42-induced toxicity was reduced by regulating the reactive oxygen species (ROS), mitochondrial membrane potential and ATP contents. In addition, IPEE regulated the expression levels of tau signaling, such as TNF-α, p-JNK, p-Akt, p-GSK3ß, p-tau, p-NF-κB, BAX and caspase 3. Finally, gallic acid, ellagic acid and quercetin 3-O-(6″-acetyl-glucoside) were identified as the physiological compounds of IPEE using ultra-performance liquid chromatography ion mobility separation quadrupole time-of-flight/tandem mass spectrometry (UPLC IMS Q-TOF/MS2).

Extra-basal ganglia iron content and non-motor symptoms in drug-naïve, early Parkinson's disease.

BACKGROUND: Although iron dyshomeostasis is associated with Parkinson's disease (PD) pathogenesis, the relationship between iron deposition and non-motor involvement in PD is not fully understood. In this study, we investigated basal ganglia and extra-basal ganglia system iron contents and their correlation with non-motor symptoms in drug-naïve, early-stage PD patients. METHODS: We enrolled 14 drug-naïve, early-stage PD patients and 12 age/sex-matched normal controls. All participants underwent brain magnetic resonance imaging to obtain the effective transverse relaxation rate (R2*) and quantitative susceptibility mapping (QSM). Deep brain structures, including the nucleus accumbens, caudate nucleus, putamen, globus pallidus, thalamus, hippocampus, and amygdala, were delineated using the FSL-FIRST; the substantia nigra, red nucleus, and dentate nucleus were segmented manually. Inter-group differences in R2* and QSM values, as well as their association with clinical parameters of PD, were investigated. RESULTS: Substantia nigra and putamen R2* values were significantly higher in PD patients than in normal controls, despite no significant difference in QSM values. Regarding the non-motor symptom scales, PD sleep scale score negatively correlated with R2* values in the red nucleus and right amygdala, Scales for Outcomes in Parkinson's disease-Autonomic scores were positively correlated with R2* values in the right amygdala and left hippocampus, and cardiovascular sub-score of Non-Motor Symptoms Scale for PD was positively associated with the QSM value in the left hippocampus. CONCLUSION: In this study, iron content in the extra-basal ganglia system was significantly correlated with non-motor symptoms, especially sleep problems and dysautonomia, even in early-stage PD.

Magnetic susceptibility imaging of human habenula at 3 T.

The habenula plays an important role in brain reward circuitry and psychiatric conditions. While much work has been done on the function and structure of the habenula in animal models, in vivo imaging studies of the human habenula have been relatively scarce due to its small size, deep brain location, and lack of clear biomarkers for its heterogeneous substructure. In this paper, we report high-resolution (0.5 × 0.5 × 0.8 mm3) MRI of the human habenula with quantitative susceptibility mapping (QSM) at 3 T. By analyzing 48 scan datasets collected from 21 healthy subjects, we found that magnetic susceptibility contrast is highly non-uniform within the habenula and across the subjects. In particular, we observed high prevalence of elevated susceptibility in the posterior subregion of the habenula. Correlation analysis between the susceptibility and the effective transverse relaxation rate (R2*) indicated that localized susceptibility enhancement in the habenula is more associated with increased paramagnetic (such as iron) rather than decreased diamagnetic (such as myelin) sources. Our results suggest that high-resolution QSM could make a potentially useful tool for substructure-resolved in vivo habenula imaging, and provide a groundwork for the future development of magnetic susceptibility as a quantitative biomarker for human habenula studies.

Feasibility of head-tilted brain scan to reduce susceptibility-induced signal loss in the prefrontal cortex in gradient echo-based imaging.

Susceptibility-induced static field (B0) inhomogeneity near the nasal cavity degrades high-field MRI image quality. Many studies have addressed this problem by hardware- or sequence-based methods to improve local B0 shimming or minimize the impact of inhomogeneity. Here, we investigate the feasibility of the head-tilted brain scan as an easily accessible way to reduce B0 inhomogeneity and associated gradient echo signal loss in the prefrontal cortex (PFC). We exploit the fact that the region of intense local B0 gradient can be steered away from the PFC by head reorientation with respect to the main magnetic field. We found that the required chin-up head tilting by a substantial angle (> 30°) can be readily achieved for a group of healthy subjects when their back was raised by about 10  cm. Eleven subjects were scanned at 3T, using a standard 20 channel head-neck coil, for whole-head B0 mapping and gradient-echo EPI-based functional MRI (fMRI) performing a reward-punishment task in normal and tilted head orientations. Additionally, multi-echo gradient echo and resting-state fMRI scans were performed on six subjects in both orientations. Head-tilted sessions, which lasted for at least 20 min, were well-tolerated by all subjects and demonstrated a marked reduction of localized signal loss in the gradient echo-based images and EPI images in the PFC compared to normal orientation scans. Imaging in tilted orientation reduced the group-averaged B0 standard deviation and peak B0 gradient in the orbital gyrus beyond what was possible with simulated 3rd order shimming. The behavioral performance in the head-tilted fMRI scans indicated that the subjects were able to perform a cognitive task with little difficulty, and the tilted fMRI scans successfully produced a robust whole-brain functional activation map consistent with the literature. Our study proposes that the back-raised, head-tilted imaging can benefit the shimming of the prefrontal brain regions while being compatible with moderate-length neuroimaging scans on healthy, cooperating subjects.