Citations

Abstract Background: As part of the CurePSP brain donation program, a questionnaire was developed to capture basic information from the next-of-kin or someone familiar with the brain donor with respect to symptoms and clinical course. The usefulness of information from the questionnaire has not been assessed. Objective: To assess the value of information from the brain donation questionnaire as it relates to differential diagnosis of parkinsonian disorders. Methods: We reviewed 150 questionnaires, including 50 patients, each with a neuropathologic diagnosis of Lewy body disease (LBD), multiple system atrophy (MSA), or progressive supranuclear palsy (PSP). The frequency of clinical features recorded in the questionnaires was compared for the three disorders, and machine learning algorithm, XGBoost, was applied to tabulated information from the questionnaires to identify features that had predictive value with respect to neuropathologic diagnosis. Results: The information from the questionnaires correlated with core clinical features for each disorder - hallucinations for LBD, autonomic dysfunction for MSA, and early falls for PSP. Age at onset and early falls were identified as the key variables that contributed most to prediction of neuropathology. Conclusion: While less optimal than prospectively collected information, a questionnaire can provide useful clinical information for clinicopathological correlative studies.

Abstract Neuropathological assessment at autopsy is the gold standard for diagnosing neurodegenerative disorders. We aimed to develop a pipeline for diagnosing Alzheimer's disease and other tauopathies, including corticobasal degeneration, globular glial tauopathy, Pick’s disease, and progressive supranuclear palsy. We used deep learning (DL)-based approach called clustering-constrained-attention multiple instance learning (CLAM) on whole slide images (WSIs) of tau immunohistochemistry in three brain regions from 120 patients. We also augmented gradient-weighted class activation mapping (Grad-CAM) to the model for visualizing cellular-level evidence in the model’s decisions. The model using the sections of cingulate and superior frontal gyri achieved the highest area under the curve (0.970±0.037) and diagnostic accuracy (0.873±0.087). Grad-CAM showed the highest attention in known pathognomonic tau lesions for each disease (e.g., Pick bodies for Pick’s disease). Our findings supported the feasibility of the DL-based approach for the classification task on WSIs, which encouraged further investigation, especially focusing on clinicopathological correlation studies.

Abstract Neuropathological assessment at autopsy is the gold standard for diagnosing neurodegenerative disorders. We aimed to develop a pipeline for diagnosing Alzheimer's disease and other tauopathies, including corticobasal degeneration, globular glial tauopathy, Pick’s disease, and progressive supranuclear palsy. We used deep learning (DL)-based approach called clustering-constrained-attention multiple instance learning (CLAM) on whole slide images (WSIs) of tau immunohistochemistry in three brain regions from 120 patients. We also augmented gradient-weighted class activation mapping (Grad-CAM) to the model for visualizing cellular-level evidence in the model’s decisions. The model using the sections of cingulate and superior frontal gyri achieved the highest area under the curve (0.970±0.037) and diagnostic accuracy (0.873±0.087). Grad-CAM showed the highest attention in known pathognomonic tau lesions for each disease (e.g., Pick bodies for Pick’s disease). Our findings supported the feasibility of the DL-based approach for the classification task on WSIs, which encouraged further investigation, especially focusing on clinicopathological correlation studies.

AbstractBackgroundMultiple system atrophy (MSA) is a progressive neurodegenerative disorder characterized by various combinations of autonomic failure, parkinsonism, and cerebellar syndromes. Although consensus criteria have been widely used to diagnose MSA, accurate clinical diagnosis remains challenging. Other neurodegenerative disorders, such as Lewy body disease, can mimic MSA.ObjectivesWe described clinical and neuropathologic findings of two patients with Creutzfeldt–Jakob disease (CJD) who had antemortem clinical diagnoses of MSA.MethodsThe brain bank for neurodegenerative disorders was queried for cases with a clinical diagnosis of MSA, but neuropathologic findings of CJD.ResultsCase 1 was a 55‐year‐old man with a 6‐month history of orthostatic hypotension, parkinsonism, cerebellar ataxia, bradyphrenia, and memory impairment. Case 2 was a 65‐year‐old man who had a 5‐year history of cerebellar ataxia, parkinsonism, and cognitive impairment, as well as a 7‐year history of dream enactment behavior. Neither case had characteristic α‐synuclein immunoreactive neuronal or glial inclusions typical of MSA. Instead, they had spongiform encephalopathy with neuronal loss and gliosis with prion protein‐immunoreactive kuru‐like plaques. Genetic analyses in case 1 had wild‐type PRNP, whereas case 2 revealed a 4‐octapeptide repeat insertion in PRNP.ConclusionsEven when clinical features suggest MSA, CJD should also be considered if the progression is rapid or the disease course is atypical, such as the absence of autonomic dysfunction for an extended period.

ObjectiveRecent evidence supports a link between increased TDP‐43 burden and the presence of an APOE4 gene allele in Alzheimer's disease (AD); however, it is difficult to conclude the direct effect of APOE on TDP‐43 pathology due to the presence of mixed AD pathologies. The goal of this study is to address how APOE isoforms impact TDP‐43 pathology and related neurodegeneration in the absence of typical AD pathologies.MethodsWe overexpressed human TDP‐43 via viral transduction in humanized APOE2, APOE3, APOE4 mice, and murine Apoe‐knockout (Apoe‐KO) mice. Behavior tests were performed across ages. Animals were harvested at 11 months of age and TDP‐43 overexpression‐related neurodegeneration and gliosis were assessed. To further address the human relevance, we analyzed the association of APOE with TDP‐43 pathology in 160 postmortem brains from autopsy‐confirmed amyotrophic lateral sclerosis (ALS) and frontotemporal lobar degeneration with motor neuron disease (FTLD‐MND) in the Mayo Clinic Brain Bank.ResultsWe found that TDP‐43 overexpression induced motor function deficits, neuronal loss, and gliosis in the motor cortex, especially in APOE2 mice, with much milder or absent effects in APOE3, APOE4, or Apoe‐KO mice. In the motor cortex of the ALS and FTLD‐MND postmortem human brains, we found that the APOE2 allele was associated with more severe TDP‐43‐positive dystrophic neurites.InterpretationOur data suggest a genotype‐specific effect of APOE on TDP‐43 proteinopathy and neurodegeneration in the absence of AD pathology, with the strongest association seen with APOE2. ANN NEUROL 2023;93:830–843

INTRODUCTION The cerebral cortex underlies our complex cognitive capabilities. Variations in human cortical surface area and thickness are associated with neurological, psychological, and behavioral traits and can be measured in vivo by magnetic resonance imaging (MRI). Studies in model organisms have identified genes that influence cortical structure, but little is known about common genetic variants that affect human cortical structure. RATIONALE To identify genetic variants associated with human cortical structure at both global and regional levels, we conducted a genome-wide association meta-analysis of brain MRI data from 51,665 individuals across 60 cohorts. We analyzed the surface area and average thickness of the whole cortex and 34 cortical regions with known functional specializations. RESULTS We identified 369 nominally genome-wide significant loci ( P < 5 × 10 −8 ) associated with cortical structure in a discovery sample of 33,992 participants of European ancestry. Of the 360 loci for which replication data were available, 241 loci influencing surface area and 66 influencing thickness remained significant after replication, with 237 loci passing multiple testing correction ( P < 8.3 × 10 −10 ; 187 influencing surface area and 50 influencing thickness). Common genetic variants explained 34% (SE = 3%) of the variation in total surface area and 26% (SE = 2%) in average thickness; surface area and thickness showed a negative genetic correlation ( r G = −0.32, SE = 0.05, P = 6.5 × 10 −12 ), which suggests that genetic influences have opposing effects on surface area and thickness. Bioinformatic analyses showed that total surface area is influenced by genetic variants that alter gene regulatory activity in neural progenitor cells during fetal development. By contrast, average thickness is influenced by active regulatory elements in adult brain samples, which may reflect processes that occur after mid-fetal development, such as myelination, branching, or pruning. When considered together, these results support the radial unit hypothesis that different developmental mechanisms promote surface area expansion and increases in thickness. To identify specific genetic influences on individual cortical regions, we controlled for global measures (total surface area or average thickness) in the regional analyses. After multiple testing correction, we identified 175 loci that influence regional surface area and 46 that influence regional thickness. Loci that affect regional surface area cluster near genes involved in the Wnt signaling pathway, which is known to influence areal identity. We observed significant positive genetic correlations and evidence of bidirectional causation of total surface area with both general cognitive functioning and educational attainment. We found additional positive genetic correlations between total surface area and Parkinson’s disease but did not find evidence of causation. Negative genetic correlations were evident between total surface area and insomnia, attention deficit hyperactivity disorder, depressive symptoms, major depressive disorder, and neuroticism. CONCLUSION This large-scale collaborative work enhances our understanding of the genetic architecture of the human cerebral cortex and its regional patterning. The highly polygenic architecture of the cortex suggests that distinct genes are involved in the development of specific cortical areas. Moreover, we find evidence that brain structure is a key phenotype along the causal pathway that leads from genetic variation to differences in general cognitive function. Identifying genetic influences on human cortical structure. ( A ) Measurement of cortical surface area and thickness from MRI. ( B ) Genomic locations of common genetic variants that influence global and regional cortical structure. ( C ) Our results support the radial unit hypothesis that the expansion of cortical surface area is driven by proliferating neural progenitor cells. ( D ) Cortical surface area shows genetic correlation with psychiatric and cognitive traits. Error bars indicate SE. IMAGE CREDITS: (A) K. COURTNEY; (C) M. R. GLASS

Abstract Eating disorders and substance use disorders frequently co‐occur. Twin studies reveal shared genetic variance between liabilities to eating disorders and substance use, with the strongest associations between symptoms of bulimia nervosa and problem alcohol use (genetic correlation [ r g ], twin‐based = 0.23‐0.53). We estimated the genetic correlation between eating disorder and substance use and disorder phenotypes using data from genome‐wide association studies (GWAS). Four eating disorder phenotypes (anorexia nervosa [AN], AN with binge eating, AN without binge eating, and a bulimia nervosa factor score), and eight substance‐use‐related phenotypes (drinks per week, alcohol use disorder [AUD], smoking initiation, current smoking, cigarettes per day, nicotine dependence, cannabis initiation, and cannabis use disorder) from eight studies were included. Significant genetic correlations were adjusted for variants associated with major depressive disorder and schizophrenia. Total study sample sizes per phenotype ranged from ~2400 to ~537 000 individuals. We used linkage disequilibrium score regression to calculate single nucleotide polymorphism‐based genetic correlations between eating disorder‐ and substance‐use‐related phenotypes. Significant positive genetic associations emerged between AUD and AN ( r g = 0.18; false discovery rate q = 0.0006), cannabis initiation and AN ( r g = 0.23; q < 0.0001), and cannabis initiation and AN with binge eating ( r g = 0.27; q = 0.0016). Conversely, significant negative genetic correlations were observed between three nondiagnostic smoking phenotypes (smoking initiation, current smoking, and cigarettes per day) and AN without binge eating ( r gs = −0.19 to −0.23; qs < 0.04). The genetic correlation between AUD and AN was no longer significant after co‐varying for major depressive disorder loci. The patterns of association between eating disorder‐ and substance‐use‐related phenotypes highlights the potentially complex and substance‐specific relationships among these behaviors.

AbstractEpidemiological, biological, and molecular data suggest links between endometriosis and endometrial cancer, with recent epidemiological studies providing evidence for an association between a previous diagnosis of endometriosis and risk of endometrial cancer. We used genetic data as an alternative approach to investigate shared biological etiology of these two diseases. Genetic correlation analysis of summary level statistics from genomewide association studies (GWAS) using LD Score regression revealed moderate but significant genetic correlation (rg = 0.23, P = 9.3 × 10−3), and SNP effect concordance analysis provided evidence for significant SNP pleiotropy (P = 6.0 × 10−3) and concordance in effect direction (P = 2.0 × 10−3) between the two diseases. Cross‐disease GWAS meta‐analysis highlighted 13 distinct loci associated at P ≤ 10−5 with both endometriosis and endometrial cancer, with one locus (SNP rs2475335) located within PTPRD associated at a genomewide significant level (P = 4.9 × 10−8, OR = 1.11, 95% CI = 1.07–1.15). PTPRD acts in the STAT3 pathway, which has been implicated in both endometriosis and endometrial cancer. This study demonstrates the value of cross‐disease genetic analysis to support epidemiological observations and to identify biological pathways of relevance to multiple diseases.