Citations

Abstract Background Some data suggest that neuroinflammation may be associated with pro‐inflammatory states in the body. Systemic, proinflammatory cytokines (TNFα, IL‐1, IL‐6, etc.) are produced by a variety of comorbidities. Neuroinflammation has been shown to be associated with the accumulation of amyloid in the brain (Becher, Heneka). We hypothesized that systemic cytokine production could be associated with neuroinflammation and thereby be related to brain amyloid accumulation. This study aims to evaluate the relationship between neuroinflammation, amyloid accumulation and systemic cytokines. Method 90 participants from the Mayo Clinic Study of Aging (MCSA) received a fasting cytokine panel over a 5‐year span where IL‐1, IL‐6, IL‐10, TNFa levels were measured. Participants were divided into four groups: cognitively unimpaired with normal levels of amyloid (CU A‐, n = 25), cognitively unimpaired with elevated amyloid (CU A+, n = 25), and mild cognitively impaired (MCI) with amyloid positive (MCI A+, n = 25), and Alzheimer's disease (AD) with elevated amyloid (AD A+, n = 15). To compare regional standardized uptake value ratio (SUVr) from neuroinflammation PET based on amyloid status (A+ vs A‐) and the four cytokines, Spearman's rank correlation and Fishers r‐to‐z transformations were used to assess pairwise group differences. Result Using Spearman's rank correlation and Fishers r‐to‐z transformations, no strong relationship was found between systemic inflammation and neuroinflammation. While comparing CUA‐ vs CUA+ in Figure 1, three regions (supplementary motor area, frontal superior medial lobe, and middle cingulate cortex) showed a significant difference ( p < 0.05). However, Figure 2 showed an overarching trend bias towards significance across most regions in IL10, IL6, and TNFa. Conclusion Although significant relationships between systemic inflammation, brain amyloid and neuroinflammation were limited, the data show an overarching trend bias in broad brain regions, suggesting that further analyses are needed, possibly with larger sample sizes, to assess the relationship of regional neuroinflammation and amyloid to systemic inflammation. Future analyses could also regress out factors like sex, age, and genotype to further proinflammatory cytokine research.

Abstract Background Neuropathologic evaluation is essential for diagnosing and treating dementia, but definitive diagnoses is only available after post‐mortem brain autopsy. Positron emission tomography (PET) offers a potential in vivo proxy for predicting neuropathology. This study proposes a semi‐supervised learning (SSL) model using a single FDG‐PET image to predict underlying neuropathology. Method The SSL model leverages a self‐organized graph structure based on a patient similarity network (PSN) created from 1,495 FDG‐PET images across the aging and neurodegenerative spectrum. Each patient is represented by a node, with edges indicating image similarity derived from principal component analysis features and cosine similarity weights. The PSN structure connects 20 nearest neighbors. Neuropathologic labels were available for 204 autopsied patients. The Poisson Learning Method was employed to propagate labels from these annotated nodes to unlabeled nodes, guided by the PSN structure. FDG‐PET images were collected from clinical and research participants at the Mayo Clinic and processed using an MRI‐free pipeline. Neuropathologic labels were assigned based on widely accepted diagnostic criteria. Leave‐one‐out cross‐validation (LOOCV) was used to assess model performance. Results The confusion matrix demonstrated high accuracy in predicting Alzheimer's disease, frontotemporal lobar degeneration with tau inclusions, and Lewy body disease pathology. A Sankey diagram and label distribution analysis revealed strong correlations between clinical and predicted neuropathologic labels. Conclusion This study highlights the potential of a graph‐based SSL pipeline to predict neuropathological changes using FDG‐PET imaging data, despite limited pathology labels. The findings underscore the value of SSL in bridging the gap between abundant neuroimaging data and scarce neuropathologic labels, contributing to improved dementia diagnosis and understanding.

Abstract Background Dementia with Lewy Bodies (DLB) has been overlooked or misdiagnosed as Alzheimer’s disease (AD) dementia due to its frequent concomitant AD pathology with Lewy Body Disease (LBD) pathology. Although autopsy confirmation has been the gold standard for the precise diagnosis of DLB, time‐course 123 I‐FP‐CIT SPECT (DaT‐SPECT) findings in autopsy‐confirmed DLB are not well characterized. We previously reported that the DaTQUANT z‐score of the more affected side of the putamen has the best discriminatory power in detecting LBD pathology and nigrostriatal degeneration in dementia patients and demonstrated z‐score of −1 as the cutoff value. We sought to examine how DaT‐SPECT findings change over time in autopsy confirmed LBD patients in preparation for its use in LBD targeted clinical trials. Method Eight autopsy confirmed neocortical or limbic LBD patients who underwent two or more DaT‐SPECT were included. Nigrostriatal dopamine transporter (DaT) binding and age‐adjusted z‐scores were analyzed using the DaTQUANT 2.0 software (GE Healthcare). Result Six patients (75%) were male. The patients had 2.5±0.7 longitudinal DaT‐SPECT scans. Clinical diagnosis at initial scan was: DLB (75%), mild cognitive impairment (MCI) with REM sleep behavior disorder (RBD) (13%), and isolated RBD (iRBD) (13%). Clinical diagnosis at last scan was: DLB (88%) and iRBD (12%). All patients developed dementia during life. Mean age at initial scan/last scan/autopsy were 71.1±7.0/73.4±6.9/75.1±7.3 years old. Time from initial and last scan to autopsy were 4.0±1.9 and 1.8±1.9 years respectively. Seven patients (88%) had neocortical LBD and one (12%) had limbic LBD. Concomitant AD pathology was observed in four patients (two males (33%), two females (100%)). All patients had DaTQUANT putamen z‐score <‐1.0 at their initial scan including in MCI+RBD (‐1.76) and iRBD (‐1.53) patients. In the DLB patients at their initial scans, five (83%) had DaTQUANT putamen z‐score <‐2.0 and one (17%) had z‐score of ‐1.55. All patients (100%) showed DaTQUANT putamen z‐score <‐2.0 at their last scans. Conclusion DaTQUANT using cut‐off putamen z‐score of ‐1 was associated with underlying LBD pathology including at their iRBD and MCI stages. Nigrostriatal DaT binding tends to decline over time with DaTQUANT z‐score <‐2 at their last scans.

Abstract INTRODUCTION Brief digital cognitive measures may be a valuable and scalable alternative to traditional tests for improving early detection and disease monitoring in patients with early‐onset Alzheimer's disease (EOAD). METHODS We examined the convergent, diagnostic, and neuroanatomical validity of Tablet‐based Cognitive Assessment Tool (TabCAT) tests in the multisite Longitudinal Early‐Onset Alzheimer's Disease Study. Four hundred seventy‐five participants from 16 sites (83 cognitively unimpaired participants, 146 individuals diagnosed with mild cognitive impairment [MCI], and 246 with mild dementia) underwent TabCAT testing, including two executive/attentional tasks (Match and Flanker) and two visuospatial tasks (Line Length and Line Orientation). RESULTS TabCAT tests showed good convergent validity with traditional pen‐and‐paper tests. Match was the most accurate TabCAT test in differentiating among clinical groups (controls vs. MCI vs. dementia) and predicting amyloid positron emission tomography (PET) positivity in clinically impaired participants. TabCAT tests were associated with regional tau PET patterns. DISCUSSION TabCAT digital measures have promise for frontline identification of patients with EOAD. Highlights Four hundred seventy‐five participants from 16 US sites underwent tablet‐based cognitive assessment. Digital tests assessed executive function and visuospatial abilities. Digital tests correlated with traditional pen‐and‐paper cognitive tests. Match (test of executive function) differentiated clinical groups. Digital tests were associated with amyloid positron emission tomography (PET) status and regional tau PET patterns.

PURPOSE Recent studies reveal that 5%-18% of children with cancer harbor pathogenic variants in known cancer-predisposing genes. However, DNA damage repair (DDR) genes, which are frequently somatically altered in pediatric tumors, have not been systematically examined as a source of novel cancer-predisposing signals. METHODS To address this gap, we interrogated 189 DDR genes for presence of germline predisposing variants (PV) among 5,993 childhood cancer cases and 14,477 adult noncancer controls (discovery cohort). PV were determined using a tiered approach incorporating ClinVar annotations, InterVar classification, and in silico tools (REVEL, CADD, and MetaSVM). Using logistic and firth regression, we identified genes with PV statistically enriched in the germline of children with tumors and replicated findings among 1,497 additional childhood cancer cases across three independent cohorts. RESULTS Analysis across all cases with cancer revealed enrichment of TP53 PV. Cancer-specific analyses confirmed known associations including germline TP53 PV in adrenocortical carcinoma, high-grade glioma (HGG), and medulloblastoma (MB), PMS2 in HGG and non-Hodgkin lymphoma (NHL), MLH1 in HGG, BRCA2 in NHL, and BARD1 in neuroblastoma. In addition, four novel associations were uncovered, including BRCA1 in ependymoma, SPIDR in HGG, SMC5 in MB, and SMARCAL1 in osteosarcoma (OS). Importantly, the SMARCAL1 :OS association was significant in the discovery (6/230, 2.6%, false discovery rate [FDR] logistic = 0.0189) as well as all three replication cohorts (Childhood Cancer Survivor Study: 8/275, 2.9%; P Fisher < .0001; Cancer Predisposition Syndrome-German Childhood Cancer Registry: 4/135, 3%, P Fisher = .002; Individualized Therapy for Relapsed Malignancies in Childhood: 4/217, 1.8%, P Fisher = .012). The remaining wild-type SMARCAL1 allele was deleted in three of four OS tumors with available data. CONCLUSION Our study confirms the relevance DDR genetic variation in pediatric cancer risk and establishes SMARCAL1 as a novel OS predisposing gene, providing insights into tumor biology and creating opportunities to optimize care for patients with this challenging tumor.

Dysarthric speech poses significant challenges for clinical assessment and diagnosis due to wide variability in the nature and severity of speech impairments associated with it. To address this, this study investigates the phonetic variations in different types of dysarthric speech using the deep learning model Phonet. Recordings of the word “Catastrophe” from 54 patients diagnosed with dysarthria were analyzed to extract posterior probabilities of consonants and vowels. The analysis revealed distinct posterior probability patterns for consonants and vowels across three types of dysarthria, indicating disorder-specific articulatory-based speech characteristics. Moreover, within each dysarthric group, variations in posterior probabilities aligned with the severity of rated articulation difficulty, reflecting nuanced intra-disease phonetic differences. These findings demonstrate Phonet’s ability to capture subtle speech variations and link them to both dysarthria type and severity. By quantifying these phonetic alterations, this study offers a novel and robust framework for objectively evaluating articulation difficulties in dysarthric speech. The results emphasize the clinical potential of Phonet as an advanced tool for improving the diagnosis and monitoring of dysarthria, paving the way for innovative applications in speech-based clinical assessments.

Abstract INTRODUCTION When cerebrospinal fluid (CSF) and positron emission tomography (PET) measurements for amyloid‐beta‐peptide (Aβ) related pathology are discordant, therapeutic decision‐making becomes uncertain. METHODS Using data from patients with mild cognitive impairment ( n = 541) from the Alzheimer's Disease Neuroimaging Initiative, we examined baseline characteristics and longitudinal clinical outcomes in persons grouped according to normal/abnormal Aβ via concurrent CSF and PET determinations using standard cutpoints. RESULTS Discordant groups for brain Aβ status (CSF+/PET− and CSF−/PET+) each represented about 5% of the mild cognitive impairment (MCI) population. Longitudinally, neither discordant group declined more than the CSF−/PET− group on either a memory measure or the Clinical Dementia Rating Sum of Boxes scores over a median 4 years of observation, while the CSF+/PET+ group exhibited worsening on both measures. DISCUSSION In contrast to the clinical decline observed in the CSF+/PET+ group, persons with MCI and CSF+/PET− or CSF−/PET+ brain amyloid patterns did not exhibit incipient decline. Highlights Discrepant abnormal cerebrospinal fluid (CSF) and positron emission tomography (PET) brain amyloid indicators are uncommon in mild cognitive impairment (MCI). CSF‐PET discrepant persons with MCI tend to have less abnormal values initially. CSF‐PET discrepant persons with MCI have a benign prognosis at 4 years.

OBJECTIVE Idiopathic normal pressure hydrocephalus (iNPH) is characterized by gait deterioration, cognitive disturbances, and urinary incontinence. Gait deficits pose safety hazards due to frequent falls, yet objective investigations of gait improvement following ventriculoperitoneal shunt (VPS) placement are limited. Therefore, the aim of this study was to assess objective improvement in gait parameters for patients with iNPH after VPS placement. METHODS All patients treated with a VPS for iNPH by the senior author at a single tertiary care center from December 2017 to January 2023 were retrospectively reviewed. Patients who underwent detailed motion analysis testing before surgery with > 1 follow-up assessment were included. Analyzed parameters included gait velocity, gait cadence, stride length, stride width, gait stability ratio, total support, and single support. Time-dependent linear mixed-effects models and generalized additive mixed-effects models (GAMMs) were used to model short-term and longitudinal gait parameter changes, respectively. RESULTS A total of 212 patients (137 male, median age was 75.2 years) who underwent VPS placement for iNPH were included, with a total of 747 gait analyses performed at a median last-gait analysis follow-up duration of 12.0 months. Statistically significant changes in all gait parameter values were seen between the preoperative time point and first postoperative follow-up (within 3 months) time point (paired Wilcoxon p < 0.001), with a statistically significant majority of patients experiencing improvement (binominal p < 0.0001). Longitudinal analyses using a GAMM showed increased gait velocity, single support, and stride length, with decreased stability ratio, step width, and total support at early time points, while a plateau was achieved around 6 months. CONCLUSIONS A detailed gait analysis demonstrated significant improvements in objective ambulation metrics, particularly in the gait velocity, stride length, and gait stability ratio, following VPS placement in patients with iNPH. Most patients maintained marked improvement from baseline to the last follow-up assessment, although a plateau of the benefit was experienced over longer follow-up.