Repairing the in situ hybridization missing data in the hippocampus region by using a 3D residual U-Net model.

The hippocampus is a critical brain region. Transcriptome data provides valuable insights into the structure and function of the hippocampus at the gene level. However, transcriptome data is often incomplete. To address this issue, we use the convolutional neural network model to repair the missing voxels in the hippocampus region, based on Allen institute coronal slices in situ hybridization (ISH) dataset. Moreover, we analyze the gene expression correlation between coronal and sagittal dataset in the hippocampus region. The results demonstrated that the trend of gene expression correlation between the coronal and sagittal datasets remained consistent following the repair of missing data in the coronal ISH dataset. In the last, we use repaired ISH dataset to identify novel genes specific to hippocampal subregions. Our findings demonstrate the accuracy and effectiveness of using deep learning method to repair ISH missing data. After being repaired, ISH has the potential to improve our comprehension of the hippocampus's structure and function.

Global spatiotemporal synchronizing structures of spontaneous neural activities in different cell types.

Increasing evidence has revealed the large-scale nonstationary synchronizations as traveling waves in spontaneous neural activity. However, the interplay of various cell types in fine-tuning these spatiotemporal patters remains unclear. Here, we performed comprehensive exploration of spatiotemporal synchronizing structures across different cell types, states (awake, anesthesia, motion) and developmental axis in male mice. We found traveling waves in glutamatergic neurons exhibited greater variety than those in GABAergic neurons. Moreover, the synchronizing structures of GABAergic neurons converged toward those of glutamatergic neurons during development, but the evolution of waves exhibited varying timelines for different sub-type interneurons. Functional connectivity arises from both standing and traveling waves, and negative connections can be elucidated by the spatial propagation of waves. In addition, some traveling waves were correlated with the spatial distribution of gene expression. Our findings offer further insights into the neural underpinnings of traveling waves, functional connectivity, and resting-state networks, with cell-type specificity and developmental perspectives.

Multi-Scale-Denoising Residual Convolutional Network for Retinal Disease Classification Using OCT.

Macular pathologies can cause significant vision loss. Optical coherence tomography (OCT) images of the retina can assist ophthalmologists in diagnosing macular diseases. Traditional deep learning networks for retinal disease classification cannot extract discriminative features under strong noise conditions in OCT images. To address this issue, we propose a multi-scale-denoising residual convolutional network (MS-DRCN) for classifying retinal diseases. Specifically, the MS-DRCN includes a soft-denoising block (SDB), a multi-scale context block (MCB), and a feature fusion block (FFB). The SDB can determine the threshold for soft thresholding automatically, which removes speckle noise features efficiently. The MCB is designed to capture multi-scale context information and strengthen extracted features. The FFB is dedicated to integrating high-resolution and low-resolution features to precisely identify variable lesion areas. Our approach achieved classification accuracies of 96.4% and 96.5% on the OCT2017 and OCT-C4 public datasets, respectively, outperforming other classification methods. To evaluate the robustness of our method, we introduced Gaussian noise and speckle noise with varying PSNRs into the test set of the OCT2017 dataset. The results of our anti-noise experiments demonstrate that our approach exhibits superior robustness compared with other methods, yielding accuracy improvements ranging from 0.6% to 2.9% when compared with ResNet under various PSNR noise conditions.

BAI-Net: Individualized Anatomical Cerebral Cartography Using Graph Neural Network.

Brain atlas is an important tool in the diagnosis and treatment of neurological disorders. However, due to large variations in the organizational principles of individual brains, many challenges remain in clinical applications. Brain atlas individualization network (BAI-Net) is an algorithm that subdivides individual cerebral cortex into segregated areas using brain morphology and connectomes. The presented method integrates group priors derived from a population atlas, adjusts areal probabilities using the context of connectivity fingerprints derived from the fiber-tract embedding of tractography, and provides reliable and explainable individualized brain areas across multiple sessions and scanners. We demonstrate that BAI-Net outperforms the conventional iterative clustering approach by capturing significantly heritable topographic variations in individualized cartographies. The topographic variability of BAI-Net cartographies has shown strong associations with individual variability in brain morphology, connectivity as well as higher relationship on individual cognitive behaviors and genetics. This study provides an explainable framework for individualized brain cartography that may be useful in the precise localization of neuromodulation and treatments on individual brains.

Tract-specific white matter microstructural alterations in subjects with schizophrenia and unaffected first-degree relatives.

White matter tracts alterations have been reported in schizophrenia (SZ), but whether such abnormalities are associated with the effects of the disorder itself and/or genetic vulnerability remains unclear. Moreover, the specific patterns of different parts of these altered tracts have been less well studied. Thus, diffusion-weighted images were acquired from 38 healthy controls (HCs), 48 schizophrenia patients, and 33 unaffected first-degree relatives of SZs (FDRs). Diffusion properties of the 25 major tracts automatically extracted with probabilistic tractography were calculated and compared among groups. Regarding the peripheral regions of the tracts, significantly higher diffusivity values in the left superior longitudinal fasciculus (SLF) and the left anterior thalamic radiation (ATR) were observed in SZs than in HCs and unaffected FDRs. However, there were no significant differences between HCs and FDRs in these two tracts. While no main effects of group with respect to the core regions of the 25 tracts survived multiple comparisons correction, FDRs had significantly higher diffusivity values in the left medial lemniscus and lower diffusivity values in the middle cerebellar peduncle than HCs and SZs. These findings enhance the understanding of the abnormal patterns in the peripheral and core regions of the tracts in SZs and those at high genetic risk for schizophrenia. Our results suggest that alterations in the peripheral regions of the left SLF and ATR are features of established illness rather than genetic predisposition, which may serve as critical neural substrates for the psychopathology of schizophrenia.

Neurogenic-dependent changes in hippocampal circuitry underlie the procognitive effect of exercise in aging mice.

We have shown that the improvement in hippocampal-based learning in aged mice following physical exercise observed is dependent on neurogenesis in the dentate gyrus (DG) and is regulated by changes in growth hormone levels. The changes in neurocircuitry, however, which may underlie this improvement, remain unclear. Using in vivo multimodal magnetic resonance imaging to track changes in aged mice exposed to exercise, we show the improved spatial learning is due to enhanced DG connectivity, particularly the strengthening of the DG-Cornu Ammonis 3 and the DG-medial entorhinal cortex connections in the dorsal hippocampus. Moreover, we provide evidence that these changes in circuitry are dependent on neurogenesis since they were abrogated by ablation of newborn neurons following exercise. These findings identify the specific changes in hippocampal circuitry that underlie the cognitive improvements resulting from physical activity and show that they are dependent on the activation of neurogenesis in aged animals.

New Trajectory of Clinical and Biomarker Changes in Sporadic Alzheimer's Disease.

Identifying dynamic changes in biomarkers and clinical profiles is essential for understanding the progression of Alzheimer's disease (AD). The relevant studies have primarily relied on patients with autosomal dominant AD; however, relevant studies in sporadic AD are poorly understood. Here, we analyzed longitudinal data from 665 participants (mean follow-up 4.90 ± 2.83 years). By aligning normal cognition (CN) baseline with a clinical diagnosis of mild cognitive impairment (MCI) or AD, we studied the progression of AD using a linear mixed model to estimate the clinical and biomarker changes from stable CN to MCI to AD. The results showed that the trajectory of hippocampal volume and fluorodeoxyglucose (FDG) was consistent with the clinical measures in that they did not follow a hypothetical sigmoid curve but rather showed a slow change in the initial stage and accelerated changes in the later stage from MCI conversion to AD. Dramatic hippocampal atrophy and the ADAS13 increase were, respectively, 2.5 and 1 years earlier than the MCI onset. Besides, cognitively normal people with elevated and normal amyloid showed no significant differences in clinical measures, hippocampal volume, or FDG. These results reveal that pre-MCI to pre-AD may be a better time window for future clinical trial design.

Chromatin Signature and Transcription Factor Binding Provide a Predictive Basis for Understanding Plant Gene Expression.

Chromatin accessibility and post-transcriptional histone modifications play important roles in gene expression regulation. However, little is known about the joint effect of multiple chromatin modifications on the gene expression level in plants, despite that the regulatory roles of individual histone marks such as H3K4me3 in gene expression have been well-documented. By using machine-learning methods, we systematically performed gene expression level prediction based on multiple chromatin modifications data in Arabidopsis and rice. We found that as few as four histone modifications were sufficient to yield good prediction performance, and H3K4me3 and H3K36me3 being the top two predictors with known functions related to transcriptional initiation and elongation, respectively. We demonstrated that the predictive powers differed between protein-coding and non-coding genes as well as between CpG-enriched and CpG-depleted genes. We also showed that the predictive model trained in one tissue or species could be applied to another tissue or species, suggesting shared underlying mechanisms. More interestingly, the gene expression levels of conserved orthologs are easier to predict than the species-specific genes. In addition, chromatin state of distal enhancers was moderately correlated to gene expression but was dispensable if given the chromatin features of the proximal regions of genes. We further extended the analysis to transcription factor (TF) binding data. Strikingly, the combinatorial effects of only a few TFs were roughly fit to gene expression levels in Arabidopsis. Overall, by using quantitative modeling, we provide a comprehensive and unbiased perspective on the epigenetic and TF-mediated regulation of gene expression in plants.

ATPP: A Pipeline for Automatic Tractography-Based Brain Parcellation.

There is a longstanding effort to parcellate brain into areas based on micro-structural, macro-structural, or connectional features, forming various brain atlases. Among them, connectivity-based parcellation gains much emphasis, especially with the considerable progress of multimodal magnetic resonance imaging in the past two decades. The Brainnetome Atlas published recently is such an atlas that follows the framework of connectivity-based parcellation. However, in the construction of the atlas, the deluge of high resolution multimodal MRI data and time-consuming computation poses challenges and there is still short of publically available tools dedicated to parcellation. In this paper, we present an integrated open source pipeline (https://www.nitrc.org/projects/atpp), named Automatic Tractography-based Parcellation Pipeline (ATPP) to realize the framework of parcellation with automatic processing and massive parallel computing. ATPP is developed to have a powerful and flexible command line version, taking multiple regions of interest as input, as well as a user-friendly graphical user interface version for parcellating single region of interest. We demonstrate the two versions by parcellating two brain regions, left precentral gyrus and middle frontal gyrus, on two independent datasets. In addition, ATPP has been successfully utilized and fully validated in a variety of brain regions and the human Brainnetome Atlas, showing the capacity to greatly facilitate brain parcellation.

Distinct Changes in Functional Connectivity in Posteromedial Cortex Subregions during the Progress of Alzheimer's Disease.

Alzheimer's disease (AD) is a progressive neurodegenerative disorder which causes dementia, especially in the elderly. The posteromedial cortex (PMC), which consists of several subregions involved in distinct functions, is one of the critical regions associated with the progression and severity of AD. However, previous studies always ignored the heterogeneity of the PMC and focused on one stage of AD. Using resting-state functional magnetic resonance imaging, we studied the respective alterations of each subregion within the PMC along the progression of AD. Our data set consisted of 21 healthy controls, 18 patients with mild cognitive impairment (MCI), 17 patients with mild AD (mAD), and 18 patients with severe AD (sAD). We investigated the functional alterations of each subregion within the PMC in different stages of AD. We found that subregions within the PMC have differential vulnerability in AD. Disruptions in functional connectivity began in the transition area between the precuneus and the posterior cingulate cortex (PCC) and then extended to other subregions of the PMC. In addition, each of these subregions was associated with distinct alterations in the functional networks that we were able to relate to AD. Our research demonstrated functional changes within the PMC in the progression of AD and may elucidate potential biomarkers for clinical applications.

The Human Brainnetome Atlas: A New Brain Atlas Based on Connectional Architecture.

The human brain atlases that allow correlating brain anatomy with psychological and cognitive functions are in transition from ex vivo histology-based printed atlases to digital brain maps providing multimodal in vivo information. Many current human brain atlases cover only specific structures, lack fine-grained parcellations, and fail to provide functionally important connectivity information. Using noninvasive multimodal neuroimaging techniques, we designed a connectivity-based parcellation framework that identifies the subdivisions of the entire human brain, revealing the in vivo connectivity architecture. The resulting human Brainnetome Atlas, with 210 cortical and 36 subcortical subregions, provides a fine-grained, cross-validated atlas and contains information on both anatomical and functional connections. Additionally, we further mapped the delineated structures to mental processes by reference to the BrainMap database. It thus provides an objective and stable starting point from which to explore the complex relationships between structure, connectivity, and function, and eventually improves understanding of how the human brain works. The human Brainnetome Atlas will be made freely available for download at http://atlas.brainnetome.org, so that whole brain parcellations, connections, and functional data will be readily available for researchers to use in their investigations into healthy and pathological states.

Connectivity Profiles Reveal a Transition Subarea in the Parahippocampal Region That Integrates the Anterior Temporal-Posterior Medial Systems.

Traditional anatomical studies of the parahippocampal region (PHR) defined the lateral portion into two subregions, the perirhinal (PRC) and parahippocampal (PHC) cortices. Based on this organization, several models suggested that the PRC and the PHC play different roles in memory through connections with different memory-related brain networks. To identify the key components of the human PHR, we used a well accepted connection-based parcellation method on two independent datasets. Our parcellation divided the PRC and PHC into three subregions, specifically, the rostral PRC, caudal PRC (PRCc), and PHC. The connectivity profile for each subregion showed that the rostral PRC was connected to the anterior temporal (AT) system and the PHC was connected to the posterior medial (PM) system. The transition area (PRCc) integrated the AT-PM systems. These results suggest that the lateral PHR not only contains functionally segregated subregions, but also contains a functionally integrated subregion. SIGNIFICANCE STATEMENT: We redefined the cartography of the human parahippocampal region (PHR) and identified a transition subarea based on distinct anatomical and functional connectivity profiles. This well defined anatomical organization of the PHR is necessary for expanding our understanding and studying the functional relevance of its subregions in recognition memory. We found that the transition subregion [caudal perirhinal cortex (PRCc)] is a functionally integrated subregion that integrates the anterior temporal (AT)-posterior medial (PM) systems. In addition, we found that the core components of the AT and PM systems connect with the PHR in the rostral PRC and parahippocampal cortex (PHC), respectively, rather than connecting with the traditional, larger, and thus less concise PRC and PHC areas. This may lead to new insights into the human memory system and related neurodegenerative diseases.

Impaired Parahippocampus Connectivity in Mild Cognitive Impairment and Alzheimer's Disease.

BACKGROUND: The parahippocampal gyrus (PHG) is an important region of the limbic system that plays an important role in episodic memory. Elucidation of the PHG connectivity pattern will aid in the understanding of memory deficits in neurodegenerative diseases. OBJECTIVE: To investigate if disease severity associated altered PHG connectivity in Alzheimer's disease (AD) exists. METHODS: We evaluated resting-state functional magnetic resonance imaging data from 18 patients with amnestic mild cognitive impairment (MCI), 35 patients with AD, and 21 controls. The PHG connectivity pattern was examined by calculating Pearson's correlation coefficients between the bilateral PHG and whole brain. Group comparisons were performed after controlling for the effects of age and gender. The functional connectivity strength in each identified region was correlated with the MMSE score to evaluate the relationship between connectivity and cognitive ability. RESULTS: Several brain regions of the default mode network showed reduced PHG connectivity in the AD patients, and PHG connectivity was associated with disease severity in the MCI and AD subjects. More importantly, correlation analyses showed that there were positive correlations between the connectivity strengths of the left PHG-PCC/Pcu and left PHG-left MTG and the Mini-Mental State Examination, indicating that with disease progression from MCI to severe AD, damage to the functional connectivity of the PHG becomes increasingly severe. CONCLUSIONS: These results indicate that disease severity is associated with altered PHG connectivity, contributing to knowledge about the reduction in cognitive ability and impaired brain activity that occur in AD/MCI. These early changes in the functional connectivity of the PHG might provide some potential clues for identification of imaging markers for the early detection of MCI and AD.

Identifying functional subdivisions in the human brain using meta-analytic activation modeling-based parcellation.

Parcellation of the human brain into fine-grained units by grouping voxels into distinct clusters has been an effective approach for delineating specific brain regions and their subregions. Published neuroimaging studies employing coordinate-based meta-analyses have shown that the activation foci and their corresponding behavioral categories may contain useful information about the anatomical-functional organization of brain regions. Inspired by these developments, we proposed a new parcellation scheme called meta-analytic activation modeling-based parcellation (MAMP) that uses meta-analytically obtained information. The raw meta data, including the experiments and the reported activation coordinates related to a brain region of interest, were acquired from the Brainmap database. Using this data, we first obtained the "modeled activation" pattern by modeling the voxel-wise activation probability given spatial uncertainty for each experiment that featured at least one focus within the region of interest. Then, we processed these "modeled activation" patterns across the experiments with a K-means clustering algorithm to group the voxels into different subregions. In order to verify the reliability of the method, we employed our method to parcellate the amygdala and the left Brodmann area 44 (BA44). The parcellation results were quite consistent with previous cytoarchitectonic and in vivo neuroimaging findings. Therefore, the MAMP proposed in the current study could be a useful complement to other methods for uncovering the functional organization of the human brain.

Regional inflation of the thalamus and globus pallidus in diving players.

PURPOSE: Several recent studies have identified structural changes in the cerebral cortex that occur with extensive motor training. However, limited studies have been conducted to explore the structural changes of subcortical structures, which are thought to play important roles in motor functioning. This study aims to localize the shape differences of the subcortical structures between the diving players and the nonathlete group. METHODS: Twelve professional players with top-level diving skills and 12 age- and education-matched healthy controls without any training experience were scanned with a 3-T magnetic resonance imaging. Data were processed using FSL-FIRST, a model-based segmentation and registration tool. RESULTS: Compared with the nonathlete group, we found significant regional inflation in the bilateral thalamus and the left globus pallidus in diving players. CONCLUSION: Although we cannot rule out the effect of predispositions, the regional inflation of the thalamus and globus pallidus might reflect the experience-dependent plasticity because of extensive diving training.