Citations

Background Deep brain stimulation (DBS) has been investigated for patients with drug-resistant epilepsy who are not candidates for resective surgery. Because different types of epilepsy involve distinct brain networks, numerous DBS targets have been explored, yet a comprehensive synthesis is lacking. Methods To provide a comprehensive overview of this expanding literature, we conducted a systematic review of studies for DBS in epilepsy, including case series, prospective and retrospective studies. We collected data on surgical targets, individual disease characteristics, outcomes and precise electrode placements. DBS electrode coordinates were gathered into a common template space and related to clinical outcomes. Results We included 124 studies, corresponding to 1210 patients and 20 distinct surgical targets. While the anterior (ANT) and centromedian (CM) nuclei of the thalamus remain the most studied, we also review less commonly used targets that show promise for specific forms of epilepsy and may warrant further investigation. Substantial variability in targeting strategies and electrode placement was observed within each of the target regions. Importantly, significant relationships between stimulation location and outcomes were identified for ANT-DBS and CM-DBS. For ANT-DBS, shorter distance to the mammillothalamic tract junction was associated with greater seizure reduction on both study-level and patient-level analyses (r=−0.55, p<0.001 and r=−0.51, p<0.001, respectively). For CM-DBS, localisation effects may be dependent on the form of epilepsy, with stimulation of the parvocellular CM being associated with better outcomes in generalised epilepsy. Conclusions Our results emphasise the importance of accurate targeting in DBS for epilepsy. Our database and atlas of DBS targets are made publicly available, potentially serving further meta-analytical work. PROSPERO registration number CRD420250649304.

Abstract Background The incidence of type 2 diabetes mellitus among young adults has been increasing over the past few decades. There are limited data on contemporary national cardiovascular mortality rates in young adults with diabetes mellitus (DM). Methods We queried the Centers for Disease Control and Prevention's Wide‐Ranging Online Data for Epidemiologic Research database for patients aged 15 to 44 years from 1999 to 2019 in the primary analysis. A secondary analysis included data from 2020 to 2022 to examine the effect of the COVID‐19 pandemic. We analysed patients in whom cardiovascular diseases (CVD) were listed as the main cause of death and DM as a contributing cause of death. In calculating age‐adjusted mortality rates (AAMR) per 100 000 individuals, we used CVD mortality in young adults with comorbid diabetes as the numerator and the overall population as the denominator. We determined temporal trends by estimating the average annual percent change (AAPC) using the Joinpoint regression program. Results Among 3 309 079 individuals aged 15–44 years who died, 30 978 deaths were due to CVD with comorbid DM listed as a contributing cause. The overall AAMR increased from 1.08 per 100 000 individuals in 1999 to 1.23 per 100 000 individuals in 2019, with an average APC of 0.75 (95% CI, 0.31–1.20). Compared with 1999, the percentage increase in CVD deaths in 2019 among young adults with comorbid DM was +6.2%, whereas a decrease of 19.9% was observed among those without comorbid DM. Males had a higher AAMR than females (1.60 vs. 0.97). Non‐Hispanic Black individuals and non‐Hispanic American Indian/Alaska Native individuals had the highest AAMRs of 2.99 and 2.89, respectively. The AAMR was higher in rural regions compared to urban areas (1.63 vs. 1.23). The overall AAMR increased markedly from 1.23 in 2019 to 1.60 in 2022, with a similar pattern observed across demographic subgroups. Conclusion Our study reveals a rising trend in CVD mortality in young adults with DM as a contributing cause. Males, non‐Hispanic Black individuals, and individuals from rural regions had higher AAMR than their counterparts. This warrants the development of specific healthcare policies aimed at these at‐risk populations. Lay summary Our study shows that cardiovascular disease (CVD) deaths are increasing in young adults with diabetes mellitus listed as a contributing cause.

Abstract Background Respiration has traditionally been considered a source of artifact in functional MRI (fMRI) due to chest motion. However, the respiratory cycle also induces changes in local oxygen concentration, resulting in fluctuations in the fMRI signal. These breathing‐triggered signals (BTS) may reflect underlying oxygen metabolism. This preliminary study developed a method to extract BTS from resting‐state BOLD fMRI and examined differences between healthy and mild cognitive impairment (MCI) groups. Method MRI scans were conducted under IRB approval on a compact 3T MRI system. Participants included two groups of 18 healthy volunteers (42.6±16.0 years) and 12 MCI patients (70.9±7.3 years). Each MRI scan included T 1 ‐weighted anatomy imaging and multi‐echo resting‐state fMRI acquisitions. The preprocessing pipeline was implemented by AFNI and FreeSurfer. Figure 1 illustrates the workflow for extracting and averaging fMRI data based on respiratory or cardiac cycle windows. As depicted in Figure 2(c,d), the mean fMRI signals corresponding to the breathing or cardiac cycles were modeled using a low‐order Fourier series to assess their amplitudes. A linear mixed‐effects model was employed to examine the effects of group and breathing cycle period, using MATLAB's ‘fitlme’ function with the formula: BTS ∼ Period + Group. Result As shown in Figure 2(e,f), breathing‐triggered BOLD signals were prominent, while cardiac‐driven BOLD signals were rarely observed. Figure 2(g) shows that global BTSs in the healthy group exhibited a linear relationship with the breathing cycle period, persisting even after artifact correction (RETROICOR for respiration and cardiac cycles and RVT). Figure 3 highlights significant regional differences between groups, particularly in the hippocampus and thalamus ( p < 0.05, uncorrected), where BTS amplitudes were greater in the MCI group. Potentially, the detected BTS could provide some insights into the coupling between respiration and cerebral oxygen metabolism and potentially serve as an imaging biomarker to assess brain health. However, due to the limited number of samples, these findings should be interpreted with caution, and further analysis is needed. Conclusion This preliminary study suggests that breathing‐triggered signals in resting‐state BOLD fMRI have the potential to serve as a noninvasive biomarker for early detection and monitoring of neurodegenerative conditions such as MCI.

Abstract Background Progressive supranuclear palsy (PSP) is a devastating neurodegenerative disorder with rapid progression to death. Multiple efforts to develop a disease‐modifying therapy for PSP have yet to succeed. We previously reported the discovery of three potential therapeutic targets for PSP. These three genes, DDR2, KANK2, and STOM, are consistently upregulated in the brains of PSP patients and mouse models of tauopathy. Reducing their expression in a drosophila model significantly rescued the tau‐induced eye degeneration and tau seeding activity. To translate these targets into the clinic, we are developing antisense oligonucleotides (ASO) as potential therapies. Method We screened candidate ASOs in neuroglioma cell line H4. ASO hits were validated for their potency and safety. We also used iPSC‐derived neurons from PSP patients to test the potency and potential toxicity of our ASOs. To investigate target engagement, therapeutic efficacy and preliminary pharmacodynamic properties, we treated iPSC‐derived midbrain organoids with the ASO leads. Lastly, we will assess the off‐target effects of the ASOs by investigating the levels of mRNA transcripts that share similar sequences as our targets using RNAseq. Result Our in vitro screening identified ASO leads that significantly reduce the target gene expressions at mRNA and protein levels in a dose‐dependent manner. The ASO hits also exhibit low cellular toxicity in the H4 cell line. Upon treating iPSC neurons with ASO hits, we were able to validate the dose‐dependent reduction of target mRNAs. Cell viability analysis indicated low toxicity, suggesting that these ASOs are potent and safe. In midbrain organoid model, DDR2‐targeting ASO showed time‐dependent target engagement and were prioritized for further development. Conclusion We developed candidate therapeutics for PSP using ASOs that silence novel therapeutic targets discovered in our systems based cross‐species screening. We showed that our ASOs have low toxicity and confirmed target engagement at both mRNA and protein levels in vitro. The results suggest a favorable preclinical profile of our ASO leads that warrants additional investigations. Importantly, PSP shares congruent pathophysiological changes with other tauopathies, such as Alzheimer’s disease. These common perturbed pathways indicate that our ASOs can be potentially repurposed for multiple neurodegenerative tauopathies.

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 Alzheimer's disease (AD) affects all brain cells and has complex genomic and immunological alterations. Previous research discovered missense AD risk or protective variants in microglial genes ABI3 and PLCG2, respectively. Expression levels of these genes are altered in AD and can influence microglial function. This study aims to uncover protective, and risk microglial molecular signatures associated with these variants to determine their role in microglial subtypes and states in AD by single cell expression and functional studies. Method We generated microglia‐enriched snRNAseq data from donors harboring either AD protective PLCG2 or AD risk ABI3 missense mutations, or neither mutation. After standard snRNAseq QC, we performed differential expressed gene (DEG) analysis of all microglial cells between variant carriers and non‐carriers using MAST. We defined protective signature as genes that are both down in ABI3 and up in PLCG2 mutation‐carriers. In contrast, risk signature genes are up in ABI3 and down in PLCG2 mutation‐carriers. We investigated the conservation of protective and risk signatures across multiple datasets, including scRNAseq data from iPSC‐derived microglia cells carrying PLCG2 protective variant, snRNAseq data from AD‐resilient donors, and data from AD and other diagnostic groups across multiple brain regions sourced from external datasets. Result We obtained snRNAseq profiles of 35,000 microglia from AD variant carriers. Our DEG analysis among all microglia cells provided 227 microglial protective and 293 risk signature genes defined by these variants. Using integrated analysis of multiple internal and external datasets, we further narrowed down these signatures. We determined that these high‐confidence protective signature genes are downregulated in early AD, upregulated in late AD brains and positively‐correlated with protective variant load in in vitro models. In contrast, risk signature genes are upregulated in early AD, downregulated in late AD and resilient donors. Risk signature expression is decreased with protective PLCG2 variant load, and altered with Aβ treatment in in vitro models. Conclusion Our study uncovers microglia specific protective and risk signatures associated with AD using sn/scRNAseq datasets from multiple sources and models. These findings nominate novel immune targets and pathways with implications for microglial function in health and disease, and ultimately therapeutic potential.

Abstract Background Alzheimer's disease (AD) affects all brain cells and has complex genomic and immunological alterations. Previous research discovered missense AD risk or protective variants in microglial genes ABI3 and PLCG2 , respectively. Expression levels of these genes are altered in AD and can influence microglial function. This study aims to uncover protective, and risk microglial molecular signatures associated with these variants to determine their role in microglial subtypes and states in AD by single cell expression and functional studies. Method We generated microglia‐enriched snRNAseq data from donors harboring either AD protective PLCG2 or AD risk ABI3 missense mutations, or neither mutation. After standard snRNAseq QC, we performed differential expressed gene (DEG) analysis of all microglial cells between variant carriers and non‐carriers using MAST. We defined protective signature as genes that are both down in ABI3 and up in PLCG2 mutation‐carriers. In contrast, risk signature genes are up in ABI3 and down in PLCG2 mutation‐carriers. We investigated the conservation of protective and risk signatures across multiple datasets, including scRNAseq data from iPSC‐derived microglia cells carrying PLCG2 protective variant, snRNAseq data from AD‐resilient donors, and data from AD and other diagnostic groups across multiple brain regions sourced from external datasets. Result We obtained snRNAseq profiles of 35,000 microglia from AD variant carriers. Our DEG analysis among all microglia cells provided 227 microglial protective and 293 risk signature genes defined by these variants. Using integrated analysis of multiple internal and external datasets, we further narrowed down these signatures. We determined that these high‐confidence protective signature genes are downregulated in early AD, upregulated in late AD brains and positively‐correlated with protective variant load in in vitro models. In contrast, risk signature genes are upregulated in early AD, downregulated in late AD and resilient donors. Risk signature expression is decreased with protective PLCG2 variant load, and altered with Aβ treatment in in vitro models. Conclusion Our study uncovers microglia specific protective and risk signatures associated with AD using sn/scRNAseq datasets from multiple sources and models. These findings nominate novel immune targets and pathways with implications for microglial function in health and disease, and ultimately therapeutic potential.

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.

Background: Splanchnic venous thrombosis (SVT) is a potentially severe and frequently underdiagnosed complication following splenectomy in patients with hematologic diseases. Despite its clinical importance, no standardized, evidence-based guidelines currently exist to inform perioperative prevention, diagnosis, or treatment in this population. Materials and Methods: A systematic review and meta-analysis were conducted following PRISMA guidelines to estimate the incidence and risk factors for SVT after splenectomy in patients with hematologic diseases. Twenty-eight observational studies comprising 5025 patients were included. Risk factor data were analyzed using random-effects models. In parallel, an international Delphi consensus process was performed involving 35 experts (hematologists and surgeons) from 14 countries. Experts were assigned to five domain-specific groups and developed clinical statements on prophylaxis, diagnosis, and management of SVT. Consensus was defined as ≥80% agreement, and recommendations were graded using the GRADE framework. Results: The pooled incidence of SVT after splenectomy was 11.4%, with higher rates in studies using routine imaging (20.7%) and those conducted at single centers (15.1%). Splenic size and weight were significant risk factors, while age, BMI, and platelet count were not. Disease-specific risk was highest in patients with myeloproliferative neoplasms, lymphoma, and thalassemia. The Delphi process generated 25 consensus statements, 14 of which were classified as strong recommendations. Key guidance included perioperative vaccination, individualized thromboprophylaxis with BMI-adjusted low molecular weight heparin, selective Doppler-based screening in high-risk patients, and stepwise anticoagulation regimens based on thrombus burden and clinical evolution. Conclusion: This study presents the first international consensus-based guidelines for managing SVT after splenectomy in patients with hematologic disease. Developed through systematic evidence synthesis and expert consensus, these recommendations provide a practical framework rather than definitive standards of care and support individualized prevention, detection, and treatment strategies in this high-risk population.