Enhanced amygdala-cingulate connectivity associates with better mood in both healthy and depressive individuals after sleep deprivation.

Sleep loss robustly disrupts mood and emotion regulation in healthy individuals but can have a transient antidepressant effect in a subset of patients with depression. The neural mechanisms underlying this paradoxical effect remain unclear. Previous studies suggest that the amygdala and dorsal nexus (DN) play key roles in depressive mood regulation. Here, we used functional MRI to examine associations between amygdala- and DN-related resting-state connectivity alterations and mood changes after one night of total sleep deprivation (TSD) in both healthy adults and patients with major depressive disorder using strictly controlled in-laboratory studies. Behavioral data showed that TSD increased negative mood in healthy participants but reduced depressive symptoms in 43% of patients. Imaging data showed that TSD enhanced both amygdala- and DN-related connectivity in healthy participants. Moreover, enhanced amygdala connectivity to the anterior cingulate cortex (ACC) after TSD associated with better mood in healthy participants and antidepressant effects in depressed patients. These findings support the key role of the amygdala-cingulate circuit in mood regulation in both healthy and depressed populations and suggest that rapid antidepressant treatment may target the enhancement of amygdala-ACC connectivity.

Image harmonization: A review of statistical and deep learning methods for removing batch effects and evaluation metrics for effective harmonization.

Magnetic resonance imaging and computed tomography from multiple batches (e.g. sites, scanners, datasets, etc.) are increasingly used alongside complex downstream analyses to obtain new insights into the human brain. However, significant confounding due to batch-related technical variation, called batch effects, is present in this data; direct application of downstream analyses to the data may lead to biased results. Image harmonization methods seek to remove these batch effects and enable increased generalizability and reproducibility of downstream results. In this review, we describe and categorize current approaches in statistical and deep learning harmonization methods. We also describe current evaluation metrics used to assess harmonization methods and provide a standardized framework to evaluate newly-proposed methods for effective harmonization and preservation of biological information. Finally, we provide recommendations to end-users to advocate for more effective use of current methods and to methodologists to direct future efforts and accelerate development of the field.

Structural brain measures linked to clinical phenotypes in major depression replicate across clinical centres.

Abnormalities in brain structural measures, such as cortical thickness and subcortical volumes, are observed in patients with major depressive disorder (MDD) who also often show heterogeneous clinical features. This study seeks to identify the multivariate associations between structural phenotypes and specific clinical symptoms, a novel area of investigation. T1-weighted magnetic resonance imaging measures were obtained using 3 T scanners for 178 unmedicated depressed patients at four academic medical centres. Cortical thickness and subcortical volumes were determined for the depressed patients and patients' clinical presentation was characterized by 213 item-level clinical measures, which were grouped into several large, homogeneous categories by K-means clustering. The multivariate correlations between structural and cluster-level clinical-feature measures were examined using canonical correlation analysis (CCA) and confirmed with both 5-fold and leave-one-site-out cross-validation. Four broad types of clinical measures were detected based on clustering: an anxious misery composite (composed of item-level depression, anxiety, anhedonia, neuroticism and suicidality scores); positive personality traits (extraversion, openness, agreeableness and conscientiousness); reported history of physical/emotional trauma; and a reported history of sexual abuse. Responses on the item-level anxious misery measures were negatively associated with cortical thickness/subcortical volumes in the limbic system and frontal lobe; reported childhood history of physical/emotional trauma and sexual abuse measures were negatively correlated with entorhinal thickness and left hippocampal volume, respectively. In contrast, the positive traits measures were positively associated with hippocampal and amygdala volumes and cortical thickness of the highly-connected precuneus and cingulate cortex. Our findings suggest that structural brain measures may reflect neurobiological mechanisms underlying MDD features.

Childhood trauma history is linked to abnormal brain connectivity in major depression.

Patients with major depressive disorder (MDD) present with heterogeneous symptom profiles, while neurobiological mechanisms are still largely unknown. Brain network studies consistently report disruptions of resting-state networks (RSNs) in patients with MDD, including hypoconnectivity in the frontoparietal network (FPN), hyperconnectivity in the default mode network (DMN), and increased connection between the DMN and FPN. Using a large, multisite fMRI dataset (n = 189 patients with MDD, n = 39 controls), we investigated network connectivity differences within and between RSNs in patients with MDD and healthy controls. We found that MDD could be characterized by a network model with the following abnormalities relative to controls: (i) lower within-network connectivity in three task-positive RSNs [FPN, dorsal attention network (DAN), and cingulo-opercular network (CON)], (ii) higher within-network connectivity in two intrinsic networks [DMN and salience network (SAN)], and (iii) higher within-network connectivity in two sensory networks [sensorimotor network (SMN) and visual network (VIS)]. Furthermore, we found significant alterations in connectivity between a number of these networks. Among patients with MDD, a history of childhood trauma and current symptoms quantified by clinical assessments were associated with a multivariate pattern of seven different within- and between-network connectivities involving the DAN, FPN, CON, subcortical regions, ventral attention network (VAN), auditory network (AUD), VIS, and SMN. Overall, our study showed that traumatic childhood experiences and dimensional symptoms are linked to abnormal network architecture in MDD. Our results suggest that RSN connectivity may explain underlying neurobiological mechanisms of MDD symptoms and has the potential to serve as an effective diagnostic biomarker.

Benchmarking metrics for inferring functional connectivity from multi-channel EEG and MEG: A simulation study.

I present a systematic evaluation of different types of metrics, for inferring magnitude, amplitude, or phase synchronization from the electroencephalogram (EEG) and the magnetoencephalogram (MEG). I used a biophysical model, generating EEG/MEG-like signals, together with a system of two coupled self-sustained chaotic oscillators, containing clear transitions from phase to amplitude synchronization solely modulated by coupling strength. Specifically, I compared metrics according to five benchmarks for assessing different types of reliability factors, including immunity to spatial leakage, test-retest reliability, and sensitivity to noise, coupling strength, and synchronization transition. My results delineate the heterogeneous reliability of widely used connectivity metrics, including two magnitude synchronization metrics [coherence (Coh) and imaginary part of coherence (ImCoh)], two amplitude synchronization metrics [amplitude envelope correlation (AEC) and corrected amplitude envelope correlation (AECc)], and three phase synchronization metrics [phase coherence (PCoh), phase lag index (PLI), and weighted PLI (wPLI)]. First, the Coh, AEC, and PCoh were prone to create spurious connections caused by spatial leakage. Therefore, they are not recommended to be applied to real EEG/MEG data. The ImCoh, AECc, PLI, and wPLI were less affected by spatial leakage. The PLI and wPLI showed the highest immunity to spatial leakage. Second, the PLI and wPLI showed higher test-retest reliability and higher sensitivity to coupling strength and synchronization transition than the ImCoh and AECc. Third, the AECc was less noisy than the ImCoh, PLI, and wPLI. In sum, my work shows that the choice of connectivity metric should be determined after a comprehensive consideration of the aforementioned five reliability factors.

Direction of information flow in large-scale resting-state networks is frequency-dependent.

Normal brain function requires interactions between spatially separated, and functionally specialized, macroscopic regions, yet the directionality of these interactions in large-scale functional networks is unknown. Magnetoencephalography was used to determine the directionality of these interactions, where directionality was inferred from time series of beamformer-reconstructed estimates of neuronal activation, using a recently proposed measure of phase transfer entropy. We observed well-organized posterior-to-anterior patterns of information flow in the higher-frequency bands (alpha1, alpha2, and beta band), dominated by regions in the visual cortex and posterior default mode network. Opposite patterns of anterior-to-posterior flow were found in the theta band, involving mainly regions in the frontal lobe that were sending information to a more distributed network. Many strong information senders in the theta band were also frequent receivers in the alpha2 band, and vice versa. Our results provide evidence that large-scale resting-state patterns of information flow in the human brain form frequency-dependent reentry loops that are dominated by flow from parieto-occipital cortex to integrative frontal areas in the higher-frequency bands, which is mirrored by a theta band anterior-to-posterior flow.

Statistical harmonization corrects site effects in functional connectivity measurements from multi-site fMRI data.

Acquiring resting-state functional magnetic resonance imaging (fMRI) datasets at multiple MRI scanners and clinical sites can improve statistical power and generalizability of results. However, multi-site neuroimaging studies have reported considerable nonbiological variability in fMRI measurements due to different scanner manufacturers and acquisition protocols. These undesirable sources of variability may limit power to detect effects of interest and may even result in erroneous findings. Until now, there has not been an approach that removes unwanted site effects. In this study, using a relatively large multi-site (4 sites) fMRI dataset, we investigated the impact of site effects on functional connectivity and network measures estimated by widely used connectivity metrics and brain parcellations. The protocols and image acquisition of the dataset used in this study had been homogenized using identical MRI phantom acquisitions from each of the neuroimaging sites; however, intersite acquisition effects were not completely eliminated. Indeed, in this study, we found that the magnitude of site effects depended on the choice of connectivity metric and brain atlas. Therefore, to further remove site effects, we applied ComBat, a harmonization technique previously shown to eliminate site effects in multi-site diffusion tensor imaging (DTI) and cortical thickness studies. In the current work, ComBat successfully removed site effects identified in connectivity and network measures and increased the power to detect age associations when using optimal combinations of connectivity metrics and brain atlases. Our proposed ComBat harmonization approach for fMRI-derived connectivity measures facilitates reliable and efficient analysis of retrospective and prospective multi-site fMRI neuroimaging studies.

Selective impairment of hippocampus and posterior hub areas in Alzheimer's disease: an MEG-based multiplex network study.

Although frequency-specific network analyses have shown that functional brain networks are altered in patients with Alzheimer's disease, the relationships between these frequency-specific network alterations remain largely unknown. Multiplex network analysis is a novel network approach to study complex systems consisting of subsystems with different types of connectivity patterns. In this study, we used magnetoencephalography to integrate five frequency-band specific brain networks in a multiplex framework. Previous structural and functional brain network studies have consistently shown that hub brain areas are selectively disrupted in Alzheimer's disease. Accordingly, we hypothesized that hub regions in the multiplex brain networks are selectively targeted in patients with Alzheimer's disease in comparison to healthy control subjects. Eyes-closed resting-state magnetoencephalography recordings from 27 patients with Alzheimer's disease (60.6 ± 5.4 years, 12 females) and 26 controls (61.8 ± 5.5 years, 14 females) were projected onto atlas-based regions of interest using beamforming. Subsequently, source-space time series for both 78 cortical and 12 subcortical regions were reconstructed in five frequency bands (delta, theta, alpha 1, alpha 2 and beta band). Multiplex brain networks were constructed by integrating frequency-specific magnetoencephalography networks. Functional connections between all pairs of regions of interests were quantified using a phase-based coupling metric, the phase lag index. Several multiplex hub and heterogeneity metrics were computed to capture both overall importance of each brain area and heterogeneity of the connectivity patterns across frequency-specific layers. Different nodal centrality metrics showed consistently that several hub regions, particularly left hippocampus, posterior parts of the default mode network and occipital regions, were vulnerable in patients with Alzheimer's disease compared to control subjects. Of note, these detected vulnerable hubs in Alzheimer's disease were absent in each individual frequency-specific network, thus showing the value of integrating the networks. The connectivity patterns of these vulnerable hub regions in the patients were heterogeneously distributed across layers. Perturbed cognitive function and abnormal cerebrospinal fluid amyloid-ß42 levels correlated positively with the vulnerability of the hub regions in patients with Alzheimer's disease. Our analysis therefore demonstrates that the magnetoencephalography-based multiplex brain networks contain important information that cannot be revealed by frequency-specific brain networks. Furthermore, this indicates that functional networks obtained in different frequency bands do not act as independent entities. Overall, our multiplex network study provides an effective framework to integrate the frequency-specific networks with different frequency patterns and reveal neuropathological mechanism of hub disruption in Alzheimer's disease.

Horizontal visibility graph transfer entropy (HVG-TE): A novel metric to characterize directed connectivity in large-scale brain networks.

We propose a new measure, horizontal visibility graph transfer entropy (HVG-TE), to estimate the direction of information flow between pairs of time series. HVG-TE quantifies the transfer entropy between the degree sequences of horizontal visibility graphs derived from original time series. Twenty-one Rössler attractors unidirectionally coupled in the posterior-to-anterior direction were used to simulate 21-channel Electroencephalography (EEG) brain networks and validate the performance of the HVG-TE. We showed that the HVG-TE is robust to different levels of coupling strengths between the coupled Rössler attractors, a wide range of time delays, different sample sizes, the effects of noise and linear mixing, and the choice of reference for EEG data. We also applied HVG-TE to EEG data in 20 healthy controls and compared its performance to a recently introduces phase-based TE measure (PTE). We found that compared with PTE, HVG-TE consistently detected stronger posterior-to-anterior information flow patterns in the alpha-band (8-13Hz) EEG brain networks for three different references. Moreover, in contrast to PTE, HVG-TE does not require an assumption on the periodicity of input signals, therefore it can be more widely applicable, even for non-periodic signals. This study shows that the HVG-TE is a directed connectivity measure to characterise the direction of information flow in large-scale brain networks.

Hierarchical clustering in minimum spanning trees.

The identification of clusters or communities in complex networks is a reappearing problem. The minimum spanning tree (MST), the tree connecting all nodes with minimum total weight, is regarded as an important transport backbone of the original weighted graph. We hypothesize that the clustering of the MST reveals insight in the hierarchical structure of weighted graphs. However, existing theories and algorithms have difficulties to define and identify clusters in trees. Here, we first define clustering in trees and then propose a tree agglomerative hierarchical clustering (TAHC) method for the detection of clusters in MSTs. We then demonstrate that the TAHC method can detect clusters in artificial trees, and also in MSTs of weighted social networks, for which the clusters are in agreement with the previously reported clusters of the original weighted networks. Our results therefore not only indicate that clusters can be found in MSTs, but also that the MSTs contain information about the underlying clusters of the original weighted network.

Sotagliflozin versus dapagliflozin to improve outcome of patients with diabetes and worsening heart failure: a cost per outcome analysis.

Background and aim: Dapagliflozin inhibits the sodium-glucose cotransporter protein 2 (SGLT-2), while sotagliflozin, belonging to a new class of dual-acting SGLT-1/SGLT-2 inhibitors, has garnered considerable attention due to its efficacy and safety. Both Dapagliflozin and sotagliflozin play a significant role in treating worsening heart failure in diabetes/nondiabetes patients with heart failure. Therefore, this article was to analyze and compare the cost per outcome of both drugs in preventing one event in patients diagnosed with diabetes-related heart failure. Method: The Cost Needed to Treat (CNT) was employed to calculate the cost of preventing one event, and the Number Needed to Treat (NNT) represents the anticipated number of patients requiring the intervention treatment to prevent a single adverse event, or the anticipated number of patients needing multiple treatments to achieve a beneficial outcome. The efficacy and safety data were obtained from the results of two published clinical trials, DAPA-HF and SOLOIST-WHF. Due to the temporal difference in the drugs' releases, we temporarily analyzed the price of dapagliflozin to calculate the price of sotagliflozin within the same timeframe. The secondary analyses aimed to assess the stability of the CNT study and minimize differences between the results of the RCT control and trial groups, employing one-way sensitivity analyses. Result: The final results revealed an annualized Number Needed to Treat (aNNT) of 4 (95% CI 3-7) for preventing one event with sotagliflozin, as opposed to 23 (95% CI 16-55) for dapagliflozin. We calculated dapagliflozin's cost per prevented event (CNT) to be $109,043 (95% CI $75,856-$260,755). The price of sotagliflozin was set below $27,260, providing a favorable advantage. Sensitivity analysis suggests that sotagliflozin may hold a cost advantage. Conclusion: In this study, sotagliflozin was observed to exhibit a price advantage over dapagliflozin in preventing one events, cardiovascular mortality, or all-cause mortality in patients with diabetes.

Community Diversity of Fungi Carried by Four Common Woodpeckers in Heilongjiang Province, China.

Woodpeckers exhibit selectivity when choosing tree cavities for nest development in forest ecosystems, and fungi play a significant and important role in this ecological process. Therefore, there is a complex and intricate relationship between the various behaviors of woodpeckers and the occurrence of fungal species. Research into the complex bond between fungi and woodpeckers was undertaken to provide more information about this remarkable ecological relationship. Through the process of line transect sampling, woodpecker traces were searched for, and mist nets were set up to capture them. A total of 21 woodpeckers belonging to four species were captured. High-throughput sequencing of the ITS region was performed on fungal-conserved samples to enable an in-depth analysis of the fungal communities linked to the woodpeckers' nests. Members of Ascomycota were the most abundant in the samples, accounting for 91.96% of the total, demonstrating the importance of this group in the forest ecosystem of this station. The statistical results indicate significant differences in the fungal diversity carried by woodpeckers among the different groups. Species of Cladosporium were found to be the most prevalent of all the detected fungal genera, accounting for 49.3%. The top 15 most abundant genera were Cladosporium, Trichoderma, Beauveria, Epicococcum, Hypoxylon, Penicillium, Nigrospora, Aspergillus, Oidiodendron, Cercospora, Talaromyces, Phialemo-nium, Petriella, Cordyceps, and Sistotrema. The standard Bray-Curtis statistical technique was used in a hierarchical clustering analysis to compute inter-sample distances, allowing for the identification of patterns and correlations within the dataset. We discovered that in the grouped samples from woodpeckers, there were differences in the diversity of fungal communities carried by four woodpecker species, but the less dominant fungal species were still similar. The findings highlight the need to consider these diverse ecological linkages in woodpecker research and conservation efforts.

Deep Trans-Omic Network Fusion for Molecular Mechanism of Alzheimer's Disease.

Background: There are various molecular hypotheses regarding Alzheimer's disease (AD) like amyloid deposition, tau propagation, neuroinflammation, and synaptic dysfunction. However, detailed molecular mechanism underlying AD remains elusive. In addition, genetic contribution of these molecular hypothesis is not yet established despite the high heritability of AD. Objective: The study aims to enable the discovery of functionally connected multi-omic features through novel integration of multi-omic data and prior functional interactions. Methods: We propose a new deep learning model MoFNet with improved interpretability to investigate the AD molecular mechanism and its upstream genetic contributors. MoFNet integrates multi-omic data with prior functional interactions between SNPs, genes, and proteins, and for the first time models the dynamic information flow from DNA to RNA and proteins. Results: When evaluated using the ROS/MAP cohort, MoFNet outperformed other competing methods in prediction performance. It identified SNPs, genes, and proteins with significantly more prior functional interactions, resulting in three multi-omic subnetworks. SNP-gene pairs identified by MoFNet were mostly eQTLs specific to frontal cortex tissue where gene/protein data was collected. These molecular subnetworks are enriched in innate immune system, clearance of misfolded proteins, and neurotransmitter release respectively. We validated most findings in an independent dataset. One multi-omic subnetwork consists exclusively of core members of SNARE complex, a key mediator of synaptic vesicle fusion and neurotransmitter transportation. Conclusions: Our results suggest that MoFNet is effective in improving classification accuracy and in identifying multi-omic markers for AD with improved interpretability. Multi-omic subnetworks identified by MoFNet provided insights of AD molecular mechanism with improved details.

Spatial transcriptomic patterns underlying amyloid-ß and tau pathology are associated with cognitive dysfunction in Alzheimer's disease.

Amyloid-ß (Aß) and tau proteins accumulate within distinct neuronal systems in Alzheimer's disease (AD). Although it is not clear why certain brain regions are more vulnerable to Aß and tau pathologies than others, gene expression may play a role. We study the association between brain-wide gene expression profiles and regional vulnerability to Aß (gene-to-Aß associations) and tau (gene-to-tau associations) pathologies by leveraging two large independent AD cohorts. We identify AD susceptibility genes and gene modules in a gene co-expression network with expression profiles specifically related to regional vulnerability to Aß and tau pathologies in AD. In addition, we identify distinct biochemical pathways associated with the gene-to-Aß and the gene-to-tau associations. These findings may explain the discordance between regional Aß and tau pathologies. Finally, we propose an analytic framework, linking the identified gene-to-pathology associations to cognitive dysfunction in AD at the individual level, suggesting potential clinical implication of the gene-to-pathology associations.

Functional connectomics in depression: insights into therapies.

Depression is a common mental disorder characterized by heterogeneous cognitive and behavioral symptoms. The emerging research paradigm of functional connectomics has provided a quantitative theoretical framework and analytic tools for parsing variations in the organization and function of brain networks in depression. In this review, we first discuss recent progress in depression-associated functional connectome variations. We then discuss treatment-specific brain network outcomes in depression and propose a hypothetical model highlighting the advantages and uniqueness of each treatment in relation to the modulation of specific brain network connectivity and symptoms of depression. Finally, we look to the future promise of combining multiple treatment types in clinical practice, using multisite datasets and multimodal neuroimaging approaches, and identifying biological depression subtypes.

Persistent Ventral Anterior Cingulate Cortex and Resolved Amygdala Hyper-responses to Negative Outcomes After Depression Remission: A Combined Cross-sectional and Longitudinal Study.

BACKGROUND: Major depressive disorder (MDD) is a highly prevalent mood disorder affecting more than 300 million people worldwide. Biased processing of negative information and neural hyper-responses to negative events are hallmarks of depression. This study combined cross-sectional and longitudinal experiments to explore both persistent and resolved neural hyper-responses to negative outcomes from risky decision making in patients with current MDD (cMDD) and remitted MDD (rMDD). METHODS: A total of 264 subjects participated in the cross-sectional study, including 117 patients with medication-naïve, first-episode current depression; 45 patients with rMDD with only 1 episode of depression; and 102 healthy control subjects. Participants completed a modified balloon analog risk task during functional magnetic resonance imaging. In the longitudinal arm of the study, 42 patients with cMDD were followed and 26 patients with rMDD were studied again after 8 weeks of antidepressant treatment. RESULTS: Patients with cMDD showed hyper-responses to loss outcomes in multiple limbic regions including the amygdala and ventral anterior cingulate cortex (vACC). Amygdala but not vACC hyperactivity correlated with depression scores in patients with cMDD. Furthermore, amygdala hyperactivity resolved while vACC hyperactivity persisted in patients with rMDD in both cross-sectional and longitudinal studies. CONCLUSIONS: These findings provide consistent evidence supporting differential patterns of amygdala and vACC hyper-responses to negative outcomes during depression remission. Amygdala hyperactivity may be a symptomatic and state-dependent marker of depressive neural responses, while vACC hyperactivity may reflect a persistent and state-independent effect of depression on brain function. These findings offer new insights into the neural underpinnings of depression remission and prevention of depression recurrence.

BrainAGE Estimation: Influence of Field Strength, Voxel Size, Race, and Ethnicity.

The BrainAGE method is used to estimate biological brain age using structural neuroimaging. However, the stability of the model across different scan parameters and races/ethnicities has not been thoroughly investigated. Estimated brain age was compared within- and across- MRI field strength and across voxel sizes. Estimated brain age gap (BAG) was compared across demographically matched groups of different self-reported races and ethnicities in ADNI and IMAS cohorts. Longitudinal ComBat was used to correct for potential scanner effects. The brain age method was stable within field strength, but less stable across different field strengths. The method was stable across voxel sizes. There was a significant difference in BAG between races, but not ethnicities. Correction procedures are suggested to eliminate variation across scanner field strength while maintaining accurate brain age estimation. Further studies are warranted to determine the factors contributing to racial differences in BAG.

Spatial transcriptomic patterns underlying regional vulnerability to amyloid-ß and tau pathologies and their relationships to cognitive dysfunction in Alzheimer's disease.

Amyloid-ß (Aß) and tau proteins accumulate within distinct neuronal systems in Alzheimer's disease (AD). Although it is not clear why certain brain regions are more vulnerable to Aß and tau pathologies than others, gene expression may play a role. We studied the association between brain-wide gene expression profiles and regional vulnerability to Aß (gene-to-Aß associations) and tau (gene-to-tau associations) pathologies leveraging two large independent cohorts (n = 715) of participants along the AD continuum. We identified several AD susceptibility genes and gene modules in a gene co-expression network with expression profiles related to regional vulnerability to Aß and tau pathologies in AD. In particular, we found that the positive APOE -to-tau association was only seen in the AD cohort, whereas patients with AD and frontotemporal dementia shared similar positive MAPT -to-tau association. Some AD candidate genes showed sex-dependent negative gene-to-Aß and gene-to-tau associations. In addition, we identified distinct biochemical pathways associated with the gene-to-Aß and the gene-to-tau associations. Finally, we proposed a novel analytic framework, linking the identified gene-to-pathology associations to cognitive dysfunction in AD at the individual level, suggesting potential clinical implication of the gene-to-pathology associations. Taken together, our study identified distinct gene expression profiles and biochemical pathways that may explain the discordance between regional Aß and tau pathologies, and filled the gap between gene-to-pathology associations and cognitive dysfunction in individual AD patients that may ultimately help identify novel personalized pathogenetic biomarkers and therapeutic targets. One Sentence Summary: We identified replicable cognition-related associations between regional gene expression profiles and selectively regional vulnerability to amyloid-ß and tau pathologies in AD.

Novel CYP1B1-RMDN2 Alzheimer's disease locus identified by genome-wide association analysis of cerebral tau deposition on PET.

Determining the genetic architecture of Alzheimer's disease (AD) pathologies can enhance mechanistic understanding and inform precision medicine strategies. Here, we performed a genome-wide association study of cortical tau quantified by positron emission tomography in 3,136 participants from 12 independent studies. The CYP1B1-RMDN2 locus was associated with tau deposition. The most significant signal was at rs2113389, which explained 4.3% of the variation in cortical tau, while APOE4 rs429358 accounted for 3.6%. rs2113389 was associated with higher tau and faster cognitive decline. Additive effects, but no interactions, were observed between rs2113389 and diagnosis, APOE4 , and Aß positivity. CYP1B1 expression was upregulated in AD. rs2113389 was associated with higher CYP1B1 expression and methylation levels. Mouse model studies provided additional functional evidence for a relationship between CYP1B1 and tau deposition but not Aß. These results may provide insight into the genetic basis of cerebral tau and novel pathways for therapeutic development in AD.

Characterization of the complete mitochondrial genome of the Lesser Spotted Woodpecker (Dryobates minor) and its phylogenetic position.

In this study, we sequenced and assembled the complete mitochondrial genome of Dryobates minor by next-generation sequencing. The mitochondrial genome of Dryobates minor is 16,847 bp in length and consists of 13 protein-coding genes (PCGS), two ribosomal RNA (rRNA) genes, 22 transfer RNA (tRNA) genes and 1 control region (CR). The CG content of the mitochondrial genome is 47.46%. Only one overlap among the 13 protein-coding genes was found: ND4L/ND4. Phylogenetic analysis based on a combined mitochondrial gene dataset indicated that the mitochondrial genome of Dryobates minor exhibited a close relationship with that of Picoides pubescens.