Characteristics of diabetic and non-diabetic carpal tunnel syndrome in terms of clinical, electrophysiological, and Sonographic features: a cross-sectional study.

BACKGROUND: Although diabetes is considered a major risk factor for carpal tunnel syndrome (CTS), the characteristics of diabetic CTS have not been fully understood. OBJECTIVE: This study is aimed at evaluation of the clinical, electrophysiological, and ultrasonographic findings of non-diabetic and diabetic CTS. METHODS: This retrospective, cross-sectional study included patients diagnosed with CTS. Patient age, sex, involved side, body mass index, clinical and electrophysiological findings, and median nerve cross-sectional area (CSA) were identified. Diabetes was identified through patient or guardian interviews, medical records, and medication history. Linear and binary logistic regression models were established to confirm the associations between the electrophysiological findings, median nerve CSA, and clinical outcomes. Covariates, such as age, sex, body mass index, diabetes, symptom duration, and thenar muscle weakness were adjusted. RESULTS: Out of the 920 hands, 126 and 794 belonged to the diabetic and non-diabetic CTS groups, respectively. The patients were significantly older in the diabetic CTS group (P < 0.001). The rate of thenar weakness in the diabetic CTS group was also significantly higher than that in the non-diabetic CTS group (P = 0.009). The diabetic CTS group had a more severe electrodiagnostic grade (P = 0.001). The prolonged onset latency of the compound motor nerve action potential (CMAP) and median nerve CSA were well associated with the degree of clinical symptoms. Increased median nerve CSA was significantly associated with prolonged CMAP onset latency (ß = 0.64; P = 0.012), prolonged transcarpal latency (ß = 0.95; P = 0.044), and decreased CMAP amplitude (ß = -0.17; P = 0.002) in the non-diabetic CTS group. CONCLUSION: Diabetic CTS had more profound electrophysiological abnormalities. Distal motor latency and median nerve CSA were not only associated with each other, but also with clinical symptoms. Further studies are needed to investigate the pathophysiological mechanisms underlying diabetic CTS.

Deep multiview learning to identify imaging-driven subtypes in mild cognitive impairment.

BACKGROUND: In Alzheimer's Diseases (AD) research, multimodal imaging analysis can unveil complementary information from multiple imaging modalities and further our understanding of the disease. One application is to discover disease subtypes using unsupervised clustering. However, existing clustering methods are often applied to input features directly, and could suffer from the curse of dimensionality with high-dimensional multimodal data. The purpose of our study is to identify multimodal imaging-driven subtypes in Mild Cognitive Impairment (MCI) participants using a multiview learning framework based on Deep Generalized Canonical Correlation Analysis (DGCCA), to learn shared latent representation with low dimensions from 3 neuroimaging modalities. RESULTS: DGCCA applies non-linear transformation to input views using neural networks and is able to learn correlated embeddings with low dimensions that capture more variance than its linear counterpart, generalized CCA (GCCA). We designed experiments to compare DGCCA embeddings with single modality features and GCCA embeddings by generating 2 subtypes from each feature set using unsupervised clustering. In our validation studies, we found that amyloid PET imaging has the most discriminative features compared with structural MRI and FDG PET which DGCCA learns from but not GCCA. DGCCA subtypes show differential measures in 5 cognitive assessments, 6 brain volume measures, and conversion to AD patterns. In addition, DGCCA MCI subtypes confirmed AD genetic markers with strong signals that existing late MCI group did not identify. CONCLUSION: Overall, DGCCA is able to learn effective low dimensional embeddings from multimodal data by learning non-linear projections. MCI subtypes generated from DGCCA embeddings are different from existing early and late MCI groups and show most similarity with those identified by amyloid PET features. In our validation studies, DGCCA subtypes show distinct patterns in cognitive measures, brain volumes, and are able to identify AD genetic markers. These findings indicate the promise of the imaging-driven subtypes and their power in revealing disease structures beyond early and late stage MCI.

Detection of C8/T1 radiculopathy by measuring the root motor conduction time.

BACKGROUND: Root motor conduction time (RMCT) can noninvasively evaluate the status of the proximal root segment. However, its clinical application remains limited, and wider studies regarding its use are scarce. We aimed to investigate the association between C8/T1 level radiculopathy and RMCT. METHODS: This was a retrospective cross-sectional study. Subjects were extracted from a general hospital's spine clinic database. A total of 48 C8/T1 root lesions from 37 patients were included, and 48 C8/T1 root levels from control subjects were matched for age, sex, and height. RMCT was measured in the abductor pollicis brevis muscle and the assessment of any delays owing to C8/T1 radiculopathy. RESULTS: The RMCT of the C8/T1 radiculopathy group was 1.7 ± 0.6 ms, which was significantly longer than that in the control group (1.2 ± 0.8 ms; p = 0.001). The delayed RMCT was independently associated with radiculopathy (adjusted odds ratio, 1.15; 95% confidence interval, 1.06-1.27; p = 0.011) after adjusting for the peripheral motor conduction time, amplitude of median compound motor nerve action potential, and shortest F-wave latency. The area under the Receiver Operating Characteristic curve for diagnosing C8/T1 radiculopathy using RMCT was 0.72 (0.61-0.82). The RMCT was significantly correlated with symptom duration (coefficient = 0.58; p < 0.001) but was not associated with the degree of arm pain. CONCLUSION: Our findings illustrate the clinical applicability of the RMCT by demonstrating its utility in diagnosing radiculopathy at certain spinal levels.

Wafer-Scale and Low-Temperature Growth of 1T-WS2 Film for Efficient and Stable Hydrogen Evolution Reaction.

The metallic 1T phase of WS2 (1T-WS2 ), which boosts the charge transfer between the electron source and active edge sites, can be used as an efficient electrocatalyst for the hydrogen evolution reaction (HER). As the semiconductor 2H phase of WS2 (2H-WS2 ) is inherently stable, methods for synthesizing 1T-WS2 are limited and complicated. Herein, a uniform wafer-scale 1T-WS2 film is prepared using a plasma-enhanced chemical vapor deposition (PE-CVD) system. The growth temperature is maintained at 150 °C enabling the direct synthesis of 1T-WS2 films on both rigid dielectric and flexible polymer substrates. Both the crystallinity and number of layers of the as-grown 1T-WS2 are verified by various spectroscopic and microscopic analyses. A distorted 1T structure with a 2a0 × a0 superlattice is observed using scanning transmission electron microscopy. An electrochemical analysis of the 1T-WS2 film demonstrates its similar catalytic activity and high durability as compared to those of previously reported untreated and planar 1T-WS2 films synthesized with CVD and hydrothermal methods. The 1T-WS2 does not transform to stable 2H-WS2 , even after a 700 h exposure to harsh catalytic conditions and 1000 cycles of HERs. This synthetic strategy can provide a facile method to synthesize uniform 1T-phase 2D materials for electrocatalysis applications.

Rational Design of Ultrathin Gas Barrier Layer via Reconstruction of Hexagonal Boron Nitride Nanoflakes to Enhance the Chemical Stability of Proton Exchange Membrane Fuel Cells.

Hexagonal boron nitride (hBN) has great potential as a promising gas barrier layer in proton exchange membrane fuel cells (PEMFCs) as it shows high proton conductivity as well as excellent gas-blocking capability. However, structural defects and mechanical damage during the transfer of the hBN layer and membrane swelling have limited the application of hBN sheets to PEMFCs. Here, an ultrathin gas barrier layer is successfully fabricated on a proton exchange membrane via reconstruction of mechanically exfoliated hBN nanoflakes using a direct spin-coating process. The hBN-coated layer effectively suppresses the gas crossover and inhibits the formation of reactive oxygen radicals in the electrodes without reducing the proton conductivity of the membrane. It is also demonstrated that the structural advantages of hBN-coated gas barrier layers promise high performance of a unit cell even after a open-circuit voltage (OCV) hold test for 100 h. Furthermore, through in-depth postmortem analyses, a time-dependent degradation mechanism of membrane electrode assembly under the OCV condition is rationally proposed.

Connectivity Analysis and Feature Classification in Attention Deficit Hyperactivity Disorder Sub-Types: A Task Functional Magnetic Resonance Imaging Study.

Attention deficit hyperactivity disorder (ADHD) is a pervasive neuropsychiatric disorder. Patients with different ADHD subtypes show different behaviors under different stimuli and thus might require differential approaches to treatment. This study explores connectivity differences between ADHD subtypes and attempts to classify these subtypes based on neuroimaging features. A total of 34 patients (13 ADHD-IA and 21 ADHD-C subtypes) underwent functional magnetic resonance imaging (fMRI) with six task paradigms. Connectivity differences between ADHD subtypes were assessed for the whole brain in each task paradigm. Connectivity measures of the identified regions were used as features for the support vector machine classifier to distinguish between ADHD subtypes. The effectiveness of connectivity measures of the regions were tested by predicting ADHD-related Diagnostic and Statistical Manual of Mental Disorders (DSM) scores. Significant connectivity differences between ADHD subtypes were identified mainly in the frontal, cingulate, and parietal cortices and partially in the temporal, occipital cortices and cerebellum. Classifier accuracy for distinguishing between ADHD subtypes was 91.18 % for both gambling punishment and emotion task paradigms. Linear prediction under the two task paradigms showed significant correlation with DSM hyperactive/impulsive score. Our study identified important brain regions from connectivity analysis based on an fMRI paradigm using gambling punishment and emotion task paradigms. The regions and associated connectivity measures could serve as features to distinguish between ADHD subtypes.

Nerve growth factor (NGF) regulates activity of nuclear factor of activated T-cells (NFAT) in neurons via the phosphatidylinositol 3-kinase (PI3K)-Akt-glycogen synthase kinase 3ß (GSK3ß) pathway.

The Ca(2+)/calcineurin-dependent transcription factor nuclear factor of activated T-cells (NFAT) plays an important role in regulating many neuronal functions, including excitability, axonal growth, synaptogenesis, and neuronal survival. NFAT can be activated by action potential firing or depolarization that leads to Ca(2+)/calcineurin-dependent dephosphorylation of NFAT and its translocation to the nucleus. Recent data suggest that NFAT and NFAT-dependent functions in neurons can also be potently regulated by NGF and other neurotrophins. However, the mechanisms of NFAT regulation by neurotrophins are not well understood. Here, we show that in dorsal root ganglion sensory neurons, NGF markedly facilitates NFAT-mediated gene expression induced by mild depolarization. The effects of NGF were not associated with changes in [Ca(2+)]i and were independent of phospholipase C activity. Instead, the facilitatory effect of NGF depended on activation of the PI3K/Akt pathway downstream of the TrkA receptor and on inhibition of glycogen synthase kinase 3ß (GSK3ß), a protein kinase known to phosphorylate NFAT and promote its nuclear export. Knockdown or knockout of NFATc3 eliminated this facilitatory effect. Simultaneous monitoring of EGFP-NFATc3 nuclear translocation and [Ca(2+)]i changes in dorsal root ganglion neurons indicated that NGF slowed the rate of NFATc3 nuclear export but did not affect its nuclear import rate. Collectively, our data suggest that NGF facilitates depolarization-induced NFAT activation by stimulating PI3K/Akt signaling, inactivating GSK3ß, and thereby slowing NFATc3 export from the nucleus. We propose that NFAT serves as an integrator of neurotrophin action and depolarization-driven calcium signaling to regulate neuronal gene expression.

Employing Informatics Strategies in Alzheimer's Disease Research: A Review from Genetics, Multiomics, and Biomarkers to Clinical Outcomes.

Alzheimer's disease (AD) is a critical national concern, affecting 5.8 million people and costing more than 250 billion annually. However, there is no available cure. Thus, effective strategies are in urgent need to discover AD biomarkers for disease early detection and drug development. In this review, we study AD from a biomedical data scientist perspective to discuss the four fundamental components in AD research: genetics (G), molecular multiomics (M), multimodal imaging biomarkers (B), and clinical outcomes (O) (collectively referred to as the GMBO framework). We provide a comprehensive review of common statistical and informatics methodologies for each component within the GMBO framework, accompanied by the major findings from landmark AD studies. Our review highlights the potential of multimodal biobank data in addressing key challenges in AD, such as early diagnosis, disease heterogeneity, and therapeutic development. We identify major hurdles in AD research, including data scarcity and complexity, and advocate for enhanced collaboration, data harmonization, and advanced modeling techniques. This review aims to be an essential guide for understanding current biomedical data science strategies in AD research, emphasizing the need for integrated, multidisciplinary approaches to advance our understanding and management of AD.

Normal Values of Central, Peripheral, and Root Motor Conduction Times in a Healthy Korean Population.

PURPOSE: Central, peripheral, and root motor conduction times (CMCTs, PMCTs, and RMCTs, respectively) are valuable diagnostic tools for spinal cord and motor nerve root lesions. We investigated the normal values and the effects of age and height on each motor conduction time. METHODS: This study included 190 healthy Korean subjects who underwent magnetic stimulation of the cortex and spinous processes at the C7 and L1 levels. Recording muscles were abductor pollicis brevis and abductor digiti minimi in the unilateral upper limb and extensor digitorum brevis and abductor hallucis in the contralateral lower limb. F-wave and compound motor nerve action potentials were also recorded. Central motor conduction time was evaluated as the difference between cortical motor evoked potential onset latency and PMCT using calculation and spinal stimulation methods. Root motor conduction time was computed as the difference between spinal stimulated and calculated CMCTs. RESULTS: The average age and height of the participants were 41.21 ± 14.39 years and 164.64 ± 8.27 cm, respectively; 39.5% (75/190) patients were men. In the linear regression analyses, upper limb CMCTs showed a significant and weak positive relationship with height. Lower limb CMCTs demonstrated a significant and weak positive relationship with age and height. Peripheral motor conduction times were significantly and positively correlated with age and height. Root motor conduction times showed no significant relationship with age and height, except for abductor pollicis brevis-RMCT, which had a weak negative correlation with height. CONCLUSIONS: This study provides normal values of CMCTs, PMCTs, and RCMTs, which have potential clinical applications. When interpreting CMCTs, age and height should be considered.

Multiplexed serum biomarkers to discriminate nonviable and ectopic pregnancy.

OBJECTIVE: To evaluate combinations of candidate biomarkers to develop a multiplexed prediction model for identifying the viability and location of an early pregnancy. In this study, we assessed 24 biomarkers with multiple machine learning-based methodologies to assess if multiplexed biomarkers may improve the diagnosis of normal and abnormal early pregnancies. DESIGN: A nested case-control design evaluated the predictive ability and discrimination of biomarkers in patients at risk of early pregnancy failure in the first trimester to classify viability and location. SETTING: Three university hospitals. PATIENTS: A total of 218 individuals with pain and/or bleeding in early pregnancy: 75 had an ongoing intrauterine gestation; 68 had ectopic pregnancies (EPs); and 75 had miscarriages. INTERVENTIONS: Serum levels of 24 biomarkers were assessed in the same patients. Multiple machine learning-based methodologies to evaluate combinations of these top candidates to develop a multiplexed prediction model for the identification of a nonviable pregnancy (ongoing intrauterine pregnancy vs. miscarriage or EP) and an EP (EP vs. ongoing intrauterine pregnancy or miscarriage). MAIN OUTCOME MEASURES: The predicted classification using each model was compared with the actual diagnosis, and sensitivity, specificity, positive predictive value, negative predictive value, conclusive classification, and accuracy were calculated. RESULTS: Models using classification regression tree analysis using 3 (pregnancy-specific beta-1-glycoprotein 3 [PSG3], chorionic gonadotropin-alpha subunit, and pregnancy-associated plasma protein-A) biomarkers were able to predict a maximum sensitivity of 93.3% and a maximum specificity of 98.6%. The model with the highest accuracy was 97.4% (with 70.2% receiving classification). Models using an overlapping group of 3 (soluble fms-like tyrosine kinase-1, PSG3, and tissue factor pathway inhibitor 2) biomarkers achieved a maximum sensitivity of 98.5% and a maximum specificity of 95.3%. The model with the highest accuracy was 94.4% (with 65.6% receiving classification). When the models were used simultaneously, the conclusive classification increased to 72.7% with an accuracy of 95.9%. The predictive ability of the biomarkers in the random forest produced similar test characteristics when using 11 predictive biomarkers. CONCLUSION: We have demonstrated a pool of biomarkers from divergent biological pathways that can be used to classify individuals with potential early pregnancy loss. The biomarkers choriogonadotropin alpha, pregnancy-associated plasma protein-A, and PSG3 can be used to predict viability, and soluble fms-like tyrosine kinase-1, tissue factor pathway inhibitor 2, and PSG3 can be used to predict pregnancy location.

Mitochondria and plasma membrane Ca2+-ATPase control presynaptic Ca2+ clearance in capsaicin-sensitive rat sensory neurons.

The central processes of primary nociceptors form synaptic connections with the second-order nociceptive neurons located in the dorsal horn of the spinal cord. These synapses gate the flow of nociceptive information from the periphery to the CNS, and plasticity at these synapses contributes to centrally mediated hyperalgesia and allodynia. Although exocytosis and synaptic plasticity are controlled by Ca(2+) at the release sites, the mechanisms underlying presynaptic Ca(2+) signalling at the nociceptive synapses are not well characterized. We examined the presynaptic mechanisms regulating Ca(2+) clearance following electrical stimulation in capsaicin-sensitive nociceptors using a dorsal root ganglion (DRG)/spinal cord neuron co-culture system. Cytosolic Ca(2+) concentration ([Ca(2+)]i) recovery following electrical stimulation was well approximated by a monoexponential function with a ∼2 s. Inhibition of sarco-endoplasmic reticulum Ca(2+)-ATPase did not affect presynaptic [Ca(2+)]i recovery, and blocking plasmalemmal Na(+)/Ca(2+) exchange produced only a small reduction in the rate of [Ca(2+)]i recovery (∼12%) that was independent of intracellular K(+). However, [Ca(2+)]i recovery in presynaptic boutons strongly depended on the plasma membrane Ca(2+)-ATPase (PMCA) and mitochondria that accounted for ∼47 and 40%, respectively, of presynaptic Ca(2+) clearance. Measurements using a mitochondria-targeted Ca(2+) indicator, mtPericam, demonstrated that presynaptic mitochondria accumulated Ca(2+) in response to electrical stimulation. Quantitative analysis revealed that the mitochondrial Ca(2+) uptake is highly sensitive to presynaptic [Ca(2+)]i elevations, and occurs at [Ca(2+)]i levels as low as ∼200-300 nm. Using RT-PCR, we detected expression of several putative mitochondrial Ca(2+) transporters in DRG, such as MCU, Letm1 and NCLX. Collectively, this work identifies PMCA and mitochondria as the major regulators of presynaptic Ca(2+) signalling at the first sensory synapse, and underlines the high sensitivity of the mitochondrial Ca(2+) uniporter in neurons to cytosolic Ca(2+).

Distinct activation properties of the nuclear factor of activated T-cells (NFAT) isoforms NFATc3 and NFATc4 in neurons.

The Ca(2+)/calcineurin-dependent transcription factor NFAT (nuclear factor of activated T-cells) is implicated in regulating dendritic and axonal development, synaptogenesis, and neuronal survival. Despite the increasing appreciation for the importance of NFAT-dependent transcription in the nervous system, the regulation and function of specific NFAT isoforms in neurons are poorly understood. Here, we compare the activation of NFATc3 and NFATc4 in hippocampal and dorsal root ganglion neurons following electrically evoked elevations of intracellular Ca(2+) concentration ([Ca(2+)](i)). We find that NFATc3 undergoes rapid dephosphorylation and nuclear translocation that are essentially complete within 20 min, although NFATc4 remains phosphorylated and localized to the cytosol, only exhibiting nuclear localization following prolonged (1-3 h) depolarization. Knocking down NFATc3, but not NFATc4, strongly diminished NFAT-mediated transcription induced by mild depolarization in neurons. By analyzing NFATc3/NFATc4 chimeras, we find that the region containing the serine-rich region-1 (SRR1) mildly affects initial NFAT translocation, although the region containing the serine-proline repeats is critical for determining the magnitude of NFAT activation and nuclear localization upon depolarization. Knockdown of glycogen synthase kinase 3ß (GSK3ß) significantly increased the depolarization-induced nuclear localization of NFATc4. In contrast, inhibition of p38 or mammalian target of rapamycin (mTOR) kinases had no significant effect on nuclear import of NFATc4. Thus, electrically evoked [Ca(2+)](i) elevation in neurons rapidly and strongly activates NFATc3, whereas activation of NFATc4 requires a coincident increase in [Ca(2+)](i) and suppression of GSK3ß, with differences in the serine-proline-containing region giving rise to these distinct activation properties of NFATc3 and NFATc4.

Chemical tuning of electrochemical properties of Pt-skin surfaces for highly active oxygen reduction reactions.

Pt-skin surfaces were successfully fabricated by the chemical deposition of additional Pt on corrugated Pt-Ni nanoparticles with Pt-skeleton surfaces. Compared to the Pt-skin formed by heat annealing, the chemically-tuned Pt-skin had a higher Pt coordination number and surface crystallinity, which resulted in superior ORR activity and durability.

Clinical effects of novel susceptibility genes for beta-amyloid: a gene-based association study in the Korean population.

Amyloid-beta (Aß) is a pathological hallmark of Alzheimer's disease (AD). We aimed to identify genes related to Aß uptake in the Korean population and investigate the effects of these novel genes on clinical outcomes, including neurodegeneration and cognitive impairments. We recruited a total of 759 Korean participants who underwent neuropsychological tests, brain magnetic resonance imaging, 18F-flutemetamol positron emission tomography, and microarray genotyping data. We performed gene-based association analysis, and also performed expression quantitative trait loci and network analysis. In genome-wide association studies, no single nucleotide polymorphism (SNP) passed the genome-wide significance threshold. In gene-based association analysis, six genes (LCMT1, SCRN2, LRRC46, MRPL10, SP6, and OSBPL7) were significantly associated with Aß standardised uptake value ratio in the brain. The three most significant SNPs (rs4787307, rs9903904, and rs11079797) on these genes are associated with the regulation of the LCMT1, OSBPL7, and SCRN2 genes, respectively. These SNPs are involved in decreasing hippocampal volume and cognitive scores by mediating Aß uptake. The 19 enriched gene sets identified by pathway analysis included axon and chemokine activity. Our findings suggest novel susceptibility genes associated with the uptake of Aß, which in turn leads to worse clinical outcomes. Our findings might lead to the discovery of new AD treatment targets.

Early-stage chronic venous disorder as a cause of leg pain overlooked for lumbar spinal disease.

Leg pain can be caused by both lumbar spinal disease and chronic venous disorder (CVD) of leg veins, but their clinical differences have not been thoroughly investigated. This study aimed to determine the incidence of CVD among patients visiting a spine center for leg pain. A total of 196 cases underwent ultrasound examination with a diagnosis rate were 85.7% (168 cases). CVD-diagnosed cases were divided into two groups based on the severity of lumbar spinal disease. The Clinical grades, symptom areas, and symptom types were compared. The differences in symptom improvements with vasoactive medication were also assessed. The most common symptom area was calf then the foot in CVD, while calf then thigh in lumbar spinal disease. Tingling-paresthesia was the most common symptom type for both, with pain and cramping similarly common in CVD and pain more common than cramping in lumbar spinal disease. Considering that the majority of CVD cases (78.6%) had minor cutaneous changes and almost half of cases (41.7%) had refluxes only in tributaries, significant differences in symptom improvement in CVD-dominant group suggested that early-stage venous reflux is a symptomatic disease and a possible cause of leg pain and other symptoms.

Extra-Basal Ganglia Brain Structures Are Related to Motor Reserve in Parkinson's Disease.

BACKGROUND: The "motor reserve" is an emerging concept based on the discrepancy between the severity of parkinsonism and dopaminergic degeneration; however, the related brain structures have not yet been elucidated. OBJECTIVE: We investigated brain structures relevant to the motor reserve in Parkinson's disease (PD) in this study. METHODS: Patients with drug-naïve, early PD were enrolled, who then underwent dopamine transporter (DAT) scan and diffusion tensor imaging (DTI). The severity of motor symptoms was evaluated with the Unified Parkinson's Disease Rating Scale score of bradykinesia and rigidity on the more affected side and dopaminergic degeneration of DAT uptake of the more affected putamen. Individual motor reserve estimate (MRE) was evaluated based on the discrepancy between the severity of motor symptoms and dopaminergic degeneration. Using DTI and the Brainnetome atlas, brain structures correlated with MRE were identified. RESULTS: We enrolled 193 patients with drug-naïve PD (mean disease duration of 15.6±13.2 months), and the MRE successfully predicted the increase of levodopa equivalent dose after two years. In the DTI analysis, fractional anisotropy values of medial, inferior frontal, and temporal lobes, limbic structures, nucleus accumbens, and thalamus were positively correlated with the MRE, while no brain structures were correlated with mean diffusivity. Additionally, degree centrality derived from the structural connectivity of the frontal and temporal lobes and limbic structures was positively correlated with the MRE. CONCLUSION: Our results show empirical evidence for MR in PD and brain structures relevant to MR, particularly, the extra-basal ganglia system including the limbic and frontal structures.

Identifying Alzheimer's genes via brain transcriptome mapping.

BACKGROUND: Alzheimer's disease (AD) is one of the most common neurodegenerative disorders characterized by progressive decline in cognitive function. Targeted genetic analyses, genome-wide association studies, and imaging genetic analyses have been performed to detect AD risk and protective genes and have successfully identified dozens of AD susceptibility loci. Recently, brain imaging transcriptomics analyses have also been conducted to investigate the relationship between neuroimaging traits and gene expression measures to identify interesting gene-traits associations. These imaging transcriptomic studies typically do not involve the disease outcome in the analysis, and thus the identified brain or transcriptomic markers may not be related or specific to the disease outcome. RESULTS: We propose an innovative two-stage approach to identify genes whose expression profiles are related to diagnosis phenotype via brain transcriptome mapping. Specifically, we first map the effects of a diagnosis phenotype onto imaging traits across the brain using a linear regression model. Then, the gene-diagnosis association is assessed by spatially correlating the brain transcriptome map with the diagnostic effect map on the brain-wide imaging traits. To demonstrate the promise of our approach, we apply it to the integrative analysis of the brain transcriptome data from the Allen Human Brain Atlas (AHBA) and the amyloid imaging data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort. Our method identifies 12 genes whose brain-wide transcriptome patterns are highly correlated with six different diagnostic effect maps on the amyloid imaging traits. These 12 genes include four confirmatory findings (i.e., AD genes reported in DisGeNET) and eight novel genes that have not be associated with AD in DisGeNET. CONCLUSION: We have proposed a novel disease-related brain transcriptomic mapping method to identify genes whose expression profiles spatially correlated with regional diagnostic effects on a studied brain trait. Our empirical study on the AHBA and ADNI data shows the promise of the approach, and the resulting AD gene discoveries provide valuable information for better understanding biological pathways from transcriptomic signatures to intermediate brain traits and to phenotypic disease outcomes.

Genomics transformer for diagnosing Parkinson's disease.

Parkinson's disease (PD) is the second most common neurodegenerative disease and presents a complex etiology with genomic and environmental factors and no recognized cures. Genotype data, such as single nucleotide polymorphisms (SNPs), could be used as a prodromal factor for early detection of PD. However, the polygenic nature of PD presents a challenge as the complex relationships between SNPs towards disease development are difficult to model. Traditional assessment methods such as polygenic risk scores and machine learning approaches struggle to capture the complex interactions present in the genotype data, thus limiting their discriminative capabilities in diagnosis. On the other hand, deep learning models are better suited for this task. Nevertheless, they encounter difficulties of their own such as a lack of interpretability. To overcome these limitations, in this work, a novel transformer encoder-based model is introduced to classify PD patients from healthy controls based on their genotype. This method is designed to effectively model complex global feature interactions and enable increased interpretability through the learned attention scores. The proposed framework outperformed traditional machine learning models and multilayer perceptron (MLP) baseline models. Moreover, visualization of the learned SNP-SNP associations provides not only interpretability to the model but also valuable insights into the biochemical pathways underlying PD development, which are corroborated by pathway enrichment analysis. Our results suggest novel SNP interactions to be further studied in wet lab and clinical settings.

Identifying imaging genetic associations via regional morphometricity estimation.

Brain imaging genetics is an emerging research field aiming to reveal the genetic basis of brain traits captured by imaging data. Inspired by heritability analysis, the concept of morphometricity was recently introduced to assess trait association with whole brain morphology. In this study, we extend the concept of morphometricity from its original definition at the whole brain level to a more focal level based on a region of interest (ROI). We propose a novel framework to identify the SNP-ROI association via regional morphometricity estimation of each studied single nucleotide polymorphism (SNP). We perform an empirical study on the structural MRI and genotyping data from a landmark Alzheimer's disease (AD) biobank; and yield promising results. Our findings indicate that the AD-related SNPs have higher overall regional morphometricity estimates than the SNPs not yet related to AD. This observation suggests that the variance of AD SNPs can be explained more by regional morphometric features than non-AD SNPs, supporting the value of imaging traits as targets in studying AD genetics. Also, we identified 11 ROIs, where the AD/non-AD SNPs and significant/insignificant morphometricity estimation of the corresponding SNPs in these ROIs show strong dependency. Supplementary motor area (SMA) and dorsolateral prefrontal cortex (DPC) are enriched by these ROIs. Our results also demonstrate that using all the detailed voxel-level measures within the ROI to incorporate morphometric information outperforms using only a single average ROI measure, and thus provides improved power to detect imaging genetic associations.

Pallidal Structural Changes Related to Levodopa-induced Dyskinesia in Parkinson's Disease.

Background: Despite the clinical impact of levodopa-induced dyskinesia (LID) in Parkinson's disease (PD), the mechanism, especially the role of basal ganglia (BG), is not fully elucidated yet. We investigated the BG structural changes related to LID in PD using a surface-based shape analysis technique. Methods: We recruited patients with PD who developed LID within 3 years (LID group, 28 patients) and who did not develop it after 7 years (non-LID group, 35 patients) from levodopa treatment for the extreme case-control study. BG structure volumes were measured using volumetry analysis and the surface-based morphometry feature (i.e., Jacobian) from the subcortical surface vertices. We compared the volume and Jacobian of meshes in the regions between the two groups. We also performed a correlation analysis between local atrophy and the severity of LID. Additionally, we evaluated structural connectivity profiles from globus pallidus interna and externa (GPi and GPe) to other brain structures based on the group comparison. Results: The demographic and clinical data showed no significant difference except for disease duration, treatment duration, parkinsonism severity, and levodopa equivalent dose. The LID group had more local atrophies of vertices in the right GPi than the non-LID group, despite no difference in volumes. Furthermore, the LID group demonstrated significantly reduced structural connectivity between left GPi and thalamus. Conclusion: This is the first demonstration of distinct shape alterations of basal ganglia structures, especially GPi, related to LID in PD. Considering both direct and indirect BG pathways share the connection between GPi and thalamus, the BG pathway plays a crucial role in the development of LID.