Neuropathological Diagnosis Research Articles

Clinical and Neuropathological Evaluations of the New Brunswick Neurological Syndrome of Unknown Cause.

In 2019, an alleged "mystery" neurological illness emerged in New Brunswick, Canada. Despite extensive media attention, no case description has been published to date. To report on 25 patients with a diagnosis of New Brunswick neurological syndrome of unknown cause (NSUC) who subsequently received a second, independent clinical reassessment or neuropathological examination between 2020 and 2025. This cross-sectional study of cases derived from a cohort of patients (n = 222) who had received an NSUC diagnosis. Four movement disorder neurologists and 2 behavioral neurologists carried out clinical evaluations at 2 hospitals in New Brunswick and Ontario, Canada. Neuropathological diagnoses were obtained in Ontario by a neuropathologist and a second reviewer, both blinded to the case histories. Eligible patients were offered a second opinion; 4 families of deceased patients provided consent for reporting autopsies and waivers of consent were obtained for 7. NSUC as described in the case definition circulated by Public Health New Brunswick in 2021. Findings from the independent clinical evaluations and diagnoses obtained through neuropathological examination. Among 105 eligible patients, 14 patients (aged 20-55 years; 8 female, 6 male) received clinical evaluations, and 11 patients (aged 56-82 years; 5 female, 6 male) had neuropathological diagnoses. Well-known conditions were identified in all 25 cases, including common neurodegenerative diseases, functional neurological disorder, traumatic brain injury, and metastatic cancer. Based on the 11 autopsy cases, a new disease was extremely unlikely, with a probability less than .001. When applying the 95% confidence interval for the true probability of no new disease, the data revealed a high probability between 87% and 100%. There was no evidence supporting a diagnosis of NSUC in this cohort. The data inclusive of independent examinations and neuropathology strongly supported the presence of several neurodegenerative and non-neurodegenerative conditions. Unfounded concerns that a potentially fatal mystery disease, possibly induced by an environmental toxin, is causing the patients' neurological symptoms has been amplified in traditional and social media. Second, independent clinical evaluations are needed for any patient given a diagnosis of NSUC.

High rates of diagnostic discordance and co-pathology: Insights into PSP from the NACC dataset.

Clinical features associated with accurate clinico-pathologic correlation within cognitive-predominant progressive supranuclear palsy (PSP) presentations have not been extensively studied. We identified features affecting PSP diagnosis within the National Alzheimer's Coordinating Center (NACC) dataset, a cognitive/behavioral research database. Autopsied NACC participants with clinical or neuropathologic PSP diagnoses were categorized by diagnostic modality (Clinical-only, Neuropathology-only, both). Group differences in clinical, demographic, neuropsychologic, and neuropathologic features were assessed. Only 38.8% of neuropathologically identified PSP had clinical PSP diagnosis while 63.5% of clinically identified PSP had PSP pathology. Neuropathology-only cases had fewer motor symptoms and better processing speed/executive functioning than the Clinical-Neuropathology group. Nearly 70% demonstrated co-neuropathologies including Alzheimer-type, Lewy body, and corticobasal degeneration pathologies. Within NACC, discordant clinical and neuropathologic PSP diagnoses highlight the heterogeneity of PSP presentation, possibly driven by reliance on "classic" ante mortem symptoms. Further studies should assess the impact of co-pathology on clinical neurodegenerative disease presentations. A high rate of clinico-pathologic PSP diagnostic discordance is seen within NACC. Cases without clinical PSP diagnosis had fewer classic motor and cognitive symptoms. Isolated PSP neuropathology is uncommon, with nearly 70% showing co-neuropathology.

Clinical Evolution of Neuropsychiatric Symptoms in Alzheimer's Disease and Dementia With Lewy Bodies in a Post-Mortem Cohort.

Almost all patients with neurodegenerative dementias experience neuropsychiatric symptoms (NPS) but the timing and clinical course is highly variable. In a prospective cohort study in Western Norway, patients with a new diagnosis of mild dementia were assessed annually in the Neuropsychiatric Inventory (NPI) for up to 9years until death. Patients with post-mortem neuropathological diagnoses of Alzheimer's disease (pAD) (n=37), Lewy body disease (pLBD) (n=14) or meeting criteria for both AD and LBD (mixed AD+LBD) (n=11) were included in this study. Neuropathological assessment was performed according to standardised protocols and blind to clinical information. In mixed effects logistic regression, longitudinal change in NPS was explored across neuropathological diagnoses and substrates. Additionally, the odds of NPS early and late in disease was evaluated in logistic regression. Early onset hallucinations were significantly more common in pLBD than pAD (OR 0.069 [95% CI 0.012-0.397], p=0.003) or mixed AD+LBD (OR 0.09 [95% CI 0.010-0.771], p=0.028) and there was a greater increase in the odds of hallucinations over time in pAD and AD+LBD than pLBD such that there was was no difference in the prevalence of late-onset hallucinations between pLBD, pAD or AD+LBD. Hallucinations early in disease were associated with higher LBD α-synuclein stages and neocortical LBD, in addition and sparser amyloid distribution. Higher density of amyloid plaques, tau tangles, cerebrovascular disease and increasing additional co-pathologies were associated with increasing odds of hallucinations over time. LBD, without significant comorbid AD pathology, is associated with hallucinations early in the course of disease while multiple other pathologies may be implicated in aetiology of late-onset hallucinations. Hallucinations increase in AD+LBD as disease progresses, a trajectory more closely aligned with AD than LBD.

Cognitive and neuropsychiatric profiles distinguish atypical parkinsonian syndromes.

Atypical parkinsonian syndromes are distinguished from Parkinson's disease by additional neurological signs and characteristic underlying neuropathology. However, they can be diagnostically challenging, rapidly progressive, and are often diagnosed late in disease course. Their different demographic features and prognoses are well studied, but the accompanying cognitive and psychiatric features may also facilitate diagnosis. Progressive supranuclear palsy (PSP) and corticobasal syndrome (CBS) may cause cognitive and behavioural manifestations that overlap with frontotemporal dementia, including non-fluent aphasia, apathy and impulsivity. Clinical diagnostic criteria have limited sensitivity, with pathologically confirmed PSP often having presented an initial clinical syndrome other than PSP-Richardson's syndrome. Here we integrate cross-sectional multi-centre baseline data from the PROSPECT and Oxford Discovery cohorts. This allowed us to compare cognitive and psychiatric features across a total of 1138 people with PSP, CBS, multiple-system atrophy (MSA), and idiopathic Parkinson's disease (PD). Data from the different cohorts were harmonised and compared using multiple linear regression. There were five key results. 1. Different syndromes showed distinctive cognitive profiles, using readily applicable 'bedside' screening tools. Frontal executive dysfunction was most evident in PSP, visuospatial deficits in CBS, with milder deficits in memory and executive function in MSA, as compared with PD. 2. The most prevalent neuropsychiatric features were depression and anxiety in CBS, apathy in PSP, with sleep disturbances common in PD. As expected, apathy correlated positively with impulsivity across all disorders. Neuropsychiatric features were generally better at discriminating between atypical parkinsonian syndromes than were the cognitive domains. 3. Both cognitive function and motor severity declined with disease duration, and motor function predicted cognition in PSP, CBS and PD but not in MSA, suggesting that in MSA cognitive and motor dysfunction are decoupled. 4. Plasma neurofilament light chain (NFL) levels, measured in a subset of patients, correlated with cognitive deficits in PSP, but not motor deficits. 5. Cognitive deficits contributed to the impairment in activities of daily living after controlling for motor severity, with every two points on the MoCA worsening the Schwab and England score by one point. In anticipation of future neuroprotective therapies, we present a classifier to improve diagnostic accuracy for atypical parkinsonian syndromes in vivo. Longitudinal cohort studies with resources for neuropathological gold-standard diagnosis remain important to validate better diagnostic tools for people with PSP, CBD, MSA and atypical parkinsonism.

Cognitive Trajectory in Concordant Clinical and Neuropathological Diagnosis of Alzheimer’s Disease Compared to Clinical Mimics - A Retrospective Analysis of NACC Data (2005–2023) (P2-3.002)

Cognitive Trajectory in Concordant Clinical and Neuropathological Diagnosis of Alzheimer’s Disease Compared to Clinical Mimics - A Retrospective Analysis of NACC Data (2005–2023) (P2-3.002)

Human skeletal muscle mitochondrial pathways are impacted by a neuropathologic diagnosis of Alzheimer's disease.

Human skeletal muscle mitochondrial pathways are impacted by a neuropathologic diagnosis of Alzheimer's disease.

Quantification of perforant path fibers for early detection of Alzheimer's disease

AbstractINTRODUCTIONThe entorhinal cortex (ERC) and perforant path (PP) fibers are critical structures in the pathology of Alzheimer's disease (AD). This study aims to explore these regions using high‐field magnetic resonance imaging (MRI), with the goal of identifying reliable biomarkers based on histopathological observations.METHODSTwenty post mortem brain specimens were scanned with 11.7T MRI, including diffusion tensor imaging and tractography, and were cut for subsequent histological examinations. The entorhinal cortical thickness and number of PP fibers derived from MRI were compared across neuropathological and premortem clinical diagnoses of AD.RESULTSThe entorhinal cortical thickness and number of PP fibers decreased along with severities of neurofibrillary tangles in the ERC. Meanwhile, a reduction in the number of PP fibers, but not the entorhinal cortical thickness, was observed during the preclinical stage of AD.CONCLUSIONSDegeneration of PP fibers was observed in early AD and progressed along with neuropathological changes.Highlights Twenty post mortem brain tissues were scanned with 11.7T MRI. Degeneration of PP fibers was observed at 250 µm isotropic resolution. PP fiber indices were linked with severities of NFTs. The number of PP fibers was decreased in preclinical AD.

Stable EEG Spatiospectral Patterns Estimated in Individuals by Group Information Guided NMF.

Electroencephalographic (EEG) oscillations occur across a wide range of spatial and spectral scales, and analysis of neural rhythmic variability have attracted recent attention as markers of development, intelligence, cognitive states and neural disorders. Nonnegative matrix factorization (NMF) has been successfully applied to multi-subject electroencephalography (EEG) spectral analysis. However, existing group NMF methods have not explicitly optimized the individual-level EEG components derived from group-level components. To preserve EEG characteristics at the individual level while establishing correspondence of patterns across participants, we present a novel framework for obtaining subject-specific EEG components, which we term group-information guided NMF (GIGNMF). In this framework, group information captured by standard NMF at the group level is utilized as guidance to compute individual subject-specific components through a multi-objective optimization strategy. Specifically, we propose a three-stage framework: first, group-level consensus EEG patterns are derived using standard group NMF tools; second, an optimal procedure is implemented to determine the number of components; and finally, the group-level EEG patterns serve as references in a new one-unit NMF employing a multi-objective optimization solver. We test the performance of the algorithm on both synthetic signals and real EEG recordings obtained from Alzheimer's disease data. Our results highlight the feasibility of using GIGNMF to identify EEG spatiotemporal patterns and present novel individual electrophysiological characteristics that enhance our understanding of cognitive function and contribute to clinical neuropathological diagnosis.

A Radiologist's Guide to IDH-Wildtype Glioblastoma for Efficient Communication With Clinicians: Part I-Essential Information on Preoperative and Immediate Postoperative Imaging.

The paradigm of isocitrate dehydrogenase (IDH)-wildtype glioblastoma is rapidly evolving, reflecting clinical, pathological, and imaging advancements. Thus, it remains challenging for radiologists, even those who are dedicated to neuro-oncology imaging, to keep pace with this rapidly progressing field and provide useful and updated information to clinicians. Based on current knowledge, radiologists can play a significant role in managing patients with IDH-wildtype glioblastoma by providing accurate preoperative diagnosis as well as preoperative and postoperative treatment planning including accurate delineation of the residual tumor. Through active communication with clinicians, extending far beyond the confines of the radiology reading room, radiologists can impact clinical decision making. This Part 1 review provides an overview about the neuropathological diagnosis of glioblastoma to understand the past, present, and upcoming revisions of the World Health Organization classification. The imaging findings that are noteworthy for radiologists while communicating with clinicians on preoperative and immediate postoperative imaging of IDH-wildtype glioblastomas will be summarized.

Current Advancements in Digital Neuropathology and Machine Learning for the Study of Neurodegenerative Diseases.

Current Advancements in Digital Neuropathology and Machine Learning for the Study of Neurodegenerative Diseases.

Novel, standardized sample collection from the brain-nose interface.

Novel, standardized sample collection from the brain-nose interface.

Investigating neuropathological correlates of hyperactive and psychotic symptoms in dementia: a systematic review.

Behavioral and Psychological Symptoms of Dementia (BPSD) are common neuropsychiatric manifestations that complicate the clinical course of dementia and impact caregiving. Among these, the Hyperactivity-Impulsivity-Irritiability-Disinhibition-Aggression-Agitation (HIDA) and Psychosis (P) domains are particularly challenging to manage. Despite their prevalence, their underlying mechanisms and neuropathological correlates, remain poorly understood. This systematic review aims to elucidate the neuropathological basis of the HIDA and psychosis domains, exploring whether distinct proteinopathies and neural circuit dysfunctions are associated with these symptoms. The review follows PRISMA guidelines, with a systematic search conducted across MEDLINE, CENTRAL, and EMBASE databases. Inclusion criteria involved studies exploring the neuropathology of the HIDA and psychosis domains in individuals with dementia. Records were screened using PICO software, and data quality was assessed using the Newcastle-Ottawa Scale (NOS) and CARE guidelines. A narrative synthesis was conducted due to heterogeneity in the data. From 846 records identified, 37 studies met inclusion criteria. Of the 18,823 cases analyzed, the most common diagnoses were Alzheimer's Disease (83.44%), Dementia with Lewy Bodies (5.37%), and Frontotemporal Dementia (13.40%). HIDA-P symptoms were distributed across all clinical diagnoses, with agitation (14.00%), delusions (11.60%), disinhibition (7.61%), and hallucinations (6.83%) being the most frequently reported behaviors. The primary neuropathological diagnosis was Alzheimer's Disease Neuropathologic Change (ADNC), present predominantly in intermediate to severe forms. The neuropathological analysis revealed the co-occurrence of multiple proteinopathies, particularly TAUopathy, TDP-43 pathology, and Lewy-related pathology (LRP), with the latter, in association with ADNC, reported in 15 studies. HIDA-P symptoms were linked with overlapping involvement of different neural circuits, particularly the amygdala and the broader limbic system. Evidence suggests that TAUopathy and multiple proteinopathies in key brain regions, such as amygdala, are central to the development of these symptoms. In contrast, the contribution of beta-amyloid and vascular damage appears marginal in the genesis of HIDA and psychotic symptoms. No behavioral symptom is pathognomonic of a specific proteinopathy; rather, the topography and severity of lesions plays a more decisive role than their single molecular composition. INPLASY2024100082.

Validating the Accuracy of Parkinson's Disease Clinical Diagnosis: A UK Brain Bank Case-Control Study.

Despite diagnostic criteria refinements, Parkinson's disease (PD) clinical diagnosis still suffers from a not satisfying accuracy, with the post-mortem examination as the gold standard for diagnosis. Seminal clinicopathological series highlighted that a relevant number of patients alive-diagnosed with idiopathic PD have an alternative post-mortem diagnosis. We evaluated the diagnostic accuracy of PD comparing the in-vivo clinical diagnosis with the post-mortem diagnosis performed through the pathological examination in 2 groups. In this retrospective case-control study, patients and healthy subjects who consented to the post-mortem pathological diagnosis at the UK Brain Bank were consecutively enrolled from the UK Brain Bank. Medical records were reviewed to classify participants and performance metrics were further calculated using neuropathological diagnosis as the gold standard. Four thousand five hundred seventy one subjects were eligible for the study. The clinical diagnosis group was: 1,048 Parkinson's patients and 1,242 healthy subjects. Pathology diagnosis group were: 996 Parkinson's patients and 1,288 subjects with no post-mortem abnormality. For the group of clinical diagnosis, PD diagnosis showed: sensitivity of 99%, specificity of 86%, accuracy of 90.96%, F1-Score 0.89, and a receiver operating characteristics area under the curve (ROC AUC) 0.925 (SE ± 0.006) [95% confidence interval [CI]: 0.913, 0.937], 𝑝<0.001. In this group, the most frequent pathology diagnosis among clinically misdiagnosed PD (false positive) patients was dementia with Lewy bodies (19.4%). Conversely, the most frequent clinical diagnosis among PD missed clinical diagnosis (false negative) patients was Alzheimer's disease (18.5%). Our findings confirm a still significant diagnostic error and emphasize the need for more fine and homogeneous criteria to classify idiopathic Parkinson's patients correctly. ANN NEUROL 2025.

Early Motor Signs in Pathologically Verified Alzheimer's Disease and Lewy Body Disease.

The neuropathologies of Alzheimer's disease (AD) and Lewy body disease (LBD) commonly co-occur. Parkinsonism is the hallmark feature in LBD but it can be difficult to predict the presence of these co-pathologies early in the course of clinical disease. Timely diagnosis has crucial implications, especially with the advent of disease-modifying therapies. We sought to define early motor features that predict the ultimate neuropathological diagnoses of normal, AD, AD with concurrent LB pathology, and pure LB. We examined the associations between individuals' early motor features from their initial visit using the Unified Parkinson's Disease Rating Scale (UPDRS) Part III and their neuropathological diagnoses using the U.S. National Alzheimer's Coordinating Center (NACC) Database. We included data from participants with neuropathologically normal brains (n = 49), AD (n = 502), AD w/LB (n = 167), and pure LB (n = 51). Total UPDRS Part III scores were increasingly higher with purer LB pathology. Decreased facial expression at baseline differentiated those with AD w/LB pathology from those with AD. Participants having pure LB pathology more often had deficits in speech, facial expression, posture, gait, bradykinesia, and upper extremity rigidity relative to those with AD w/LB. Diminished facial expression significantly predicted the presence of LBs among those with concurrent AD pathology. Worse early speech, facial expression, posture, gait, bradykinesia, and upper extremity rigidity were suggestive of more pure LB pathology. These findings emphasize the utility of the neurological exam in the clinical assessment of persons with cognitive complaints as it can guide management.

Diagnostic impact of DNA methylation classification in adult and pediatric CNS tumors

Over the past decade, neuropathological diagnosis has undergone significant changes, integrating morphological features with molecular biomarkers. The molecular era has successfully refined neuropathological diagnostic accuracy; however, a substantial number of CNS tumor diagnoses remain challenging, particularly in children. DNA methylation classification has emerged as a powerful machine learning approach for clinical decision-making in CNS tumors. The aim of this study is to share our experience using DNA methylation classification in daily routine practice, illustrated through clinical cases. We employed a classification system to evaluate discrepancies between histo-molecular and DNA methylation diagnoses, with a specific focus on adult versus pediatric CNS tumors. In our study, we observed that 40% of cases fell into Class I, 47% into Class II, and 13% into Class III among the “matched cases” (≥ 0.84). In other words, DNA methylation classification confirmed morphological diagnoses in 63% of adult and 23% of pediatric cases. Refinement of diagnosis was particularly evident in the pediatric population (65% vs. 21% for the adult population, p = 0.006). Additionally, we discussed cases classified with low calibrated scores. In conclusion, our study confirms that DNA methylation classification provides significant added-value for CNS tumors diagnosis, particularly in pediatric cases.

Multivariate Analyses with Two-Step Dimension Reduction for an Association Study Between 11C-Pittsburgh Compound B and Magnetic Resonance Imaging in Alzheimer's Disease.

The neuropathological diagnosis of Alzheimer's disease (AD) relies on amyloid beta (Aβ) deposition in brain tissues. To study the relationship between Aβ deposition and brain structure, as determined using 11C-Pittsburgh compound B (PiB) and magnetic resonance imaging (MRI), respectively, we developed a regression model with PiB and MRI data as the predictor and response variables, respectively, and proposed a regression method for studying the association between them based on a supervised sparse multivariate analysis with dimension reduction based on a composite paired basis function. By applying this method to imaging data of 61 patients with AD (age: 55-85), the first component showed the strongest correlation with the composite score, owing to the supervised feature. The spatial pattern included the hippocampal and parahippocampal regions for MRI. The peak value was observed in the posterior cingulate and precuneus for PiB. The differences in PiB scores among the diagnosis groups 12 months after PiB imaging were significant between the normal and AD groups (p = 0.0284), but not between the normal and mild cognitive impairment (MCI) groups or the MCI and AD groups (p = 0.3508). Our method may facilitate the development of a dementia biomarker from brain imaging data. Scoring imaging data allows for visualization and the application of traditional analysis, facilitating clinical analysis for better interpretation of results.

Weakly supervised deep learning-based classification for histopathology of gliomas: a single center experience

Multiple artificial intelligence systems have been created to facilitate accurate and prompt histopathological diagnosis of tumors using hematoxylin-eosin-stained slides. We aimed to investigate whether weakly supervised deep learning can aid in glioma diagnosis. We analyzed 472 whole slide images (WSIs) from 226 patients in West China Hospital (WCH) and 1604 WSIs from 880 patients in The Cancer Genome Atlas (TCGA). We utilized the OpenSlide library to load WSIs, segmented them into small patches using the DeepZoom module, and then normalized the color using the Reinhard method. A weakly supervised deep learning model was developed using ResNet-50 combined with an attention mechanism. We investigated the performance of the model by calculating area under the curve (AUC) in a ten-fold cross-validation setting. Heatmap visualizations showed the prediction mechanism of the model. The results were promising, with high AUC values for differentiating grades of astrocytomas, oligodendrogliomas, all gliomas, and glioma types in the TCGA dataset (0.9419, 0.8659, 0.9904, and 0.9298, respectively), and in the WCH cohort (0.9048, 0.7423, 0.9510, and 0.7098, respectively). The model demonstrated a strong ability to infer IDH status in the TCGA dataset (AUC = 0.9488). The weakly supervised deep learning model proved to be an effective and reliable tool for neuropathological diagnosis, making it an attractive auxiliary tool.

Current Status and Future Perspective of Seoul National University Hospital-Dementia Brain Bank with Concordance of Clinical and Neuropathological Diagnosis.

This paper introduces the current status of Seoul National University Hospital Dementia Brain Bank (SNUH-DBB), focusing on the concordance rate between clinical diagnoses and postmortem neuropathological diagnoses. We detail SNUH-DBB operations, including protocols for specimen handling, induced pluripotent stem cells (iPSC) and cerebral organoids establishment from postmortem dural fibroblasts, and adult neural progenitor cell cultures. We assessed clinical-neuropathological diagnostic concordance rate. Between 2015 and September 2024, 162 brain specimens were collected via brain donation and autopsy. The median donor age was 73 years (1-94) with a male-to -female ratio of 2:1. The median postmortem interval was 9.5 hours (range: 2.5-65). Common neuropathological diagnoses included pure Lewy body disease (10.6%), Lewy body disease (LBD) with other brain diseases (10.6%), pure Alzheimer's disease-neuropathological change (ADNC) (6.0%), ADNC with other brain diseases (10.7%), vascular brain injury (15.2%), and primary age-related tauopathy (7.3%). APOE genotype distribution was following: ε3/ε3: 62.3%, ε2/ε3: 9.6%, ε2/ε4: 3.4%, ε3/ε4: 24.0%, and ε4/ε4: 0.7%. Concordance rates between pathological and clinical diagnoses were: ADNC/AD at 42.4%; LBD at 59.0%; PSP at 100%; ALS at 85.7%; Huntington's disease 100%. The varying concordance rates across different diseases emphasize the need for improved diagnostic criteria and biomarkers, particularly for AD and LBD. Tissues have been distributed to over 40 national studies. SNUH-DBB provides high-quality brain tissues and cell models for neuroscience research, operating under standardized procedures and international guidelines. It supports translational research in dementia and neurodegenerative diseases, potentially advancing diagnostic and therapeutic strategies.

Two novel variants in GRN: the relevance of CNV analysis and genetic screening in FTLD patients with a negative family history

BackgroundFrontotemporal lobar degeneration (FTLD) is one of the leading causes of early onset dementia. Pathogenic variants in GRN have been reported to cause 5–25% of familial and 5% of sporadic FTLD. Here, we present two novel, likely pathogenic variants in GRN.MethodsFour patients from four different families underwent whole exome sequencing (WES) with additional copy-number variance (CNV) analysis in a clinical setting. TMEM106B rs1990622 and rs3173615 SNPs and 3’UTR insertion were tested in one presymptomatic carrier. In three probands and one presymptomatic carrier, plasma progranulin (PGRN) levels were measured using a specific ELISA kit. In two probands, neuropathological diagnosis was established using current neuropathological criteria.ResultsThrough CNV analysis on WES data, we identified a partial deletion, NM_002087.2 (GRN):c.1179 + 104_1536delinsCTGA, p.(?), in three patients with primary progressive aphasia and/or corticobasal syndrome. Haplotype analysis revealed a shared haplotype block, suggesting that the deletion represents a founder mutation. Additionally, we found a novel, missense variant, NM_002087.2 (GRN):c.23 T > A, p.(Val8Glu), in one proband with a negative family history. The proband’s unaffected parent—in their 80 s—carried the same variant, yet was homozygous for the TMEM106B risk haplotype. The pathogenicity of both GRN variants was supported by typical neuropathological features and reduced PGRN levels.ConclusionWe recommend a thorough genetic screening, including CNV analysis, for both familial and apparent sporadic FTLD patients. Furthermore, the presymptomatic carrier homozygous for the TMEM106B risk haplotype exemplifies the presence of other protective factors that modify disease onset and urges caution in genetic counselling based on the TMEM106B haplotype.

Deep learning reveals pathology-confirmed neuroimaging signatures in Alzheimer's, vascular and Lewy body dementias.

Concurrent neurodegenerative and vascular pathologies pose a diagnostic challenge in the clinical setting, with histopathology remaining the definitive modality for dementia-type diagnosis. To address this clinical challenge, we introduce a neuropathology-based, data-driven, multi-label deep learning framework to identify and quantify in-vivo biomarkers for Alzheimer's disease (AD), vascular dementia (VD), and Lewy body dementia (LBD) using antemortem T1-weighted MRI scans of 423 demented and 361 control participants from NACC and ADNI datasets. Based on the best-performing deep learning model, explainable heatmaps are extracted to visualize disease patterns, and the novel Deep Signature of Pathology Atrophy REcognition (DeepSPARE) indices are developed, where a higher DeepSPARE score indicates more brain alterations associated with that specific pathology. A substantial discrepancy in clinical and neuropathology diagnosis was observed in the demented patients: 71% of them had more than one pathology, but 67% of them were clinically diagnosed as AD only. Based on these neuropathology diagnoses and leveraging cross-validation principles, the deep learning model achieved the best performance with a balanced accuracy of 0.844, 0.839, and 0.623 for AD, VD, and LBD, respectively, and was used to generate the explainable deep-learning heatmaps and DeepSPARE indices. The explainable deep-learning heatmaps revealed distinct neuroimaging brain alteration patterns for each pathology: the AD heatmap highlighted bilateral hippocampal regions, the VD heatmap emphasized white matter regions, and the LBD heatmap exposed occipital alterations. The DeepSPARE indices were validated by examining their associations with cognitive testing, neuropathological, and neuroimaging measures using linear mixed-effects models. The DeepSPARE-AD index was associated with MMSE, Trail B, memory, PFDR-adjustedhippocampal volume, Braak stages, CERAD scores, and Thal phases (PFDR-adjusted < 0.05). The DeepSPARE-VD index was associated with white matter hyperintensity volume and cerebral amyloid angiopathy (PFDR-adjusted < 0.001). The DeepSPARE-LBD index was associated with Lewy body stages (PFDR-adjusted < 0.05). The findings were replicated in an out-of-sample ADNI dataset by testing associations with cognitive, imaging, plasma, and CSF measures. CSF and plasma pTau181 were significantly associated with DeepSPARE-AD in the AD/MCIΑβ+ group (PFDR-adjusted < 0.001), and CSF α-synuclein was associated solely with DeepSPARE-LBD (PFDR-adjusted = 0.036). Overall, these findings demonstrate the advantages of our innovative deep-learning framework in detecting antemortem neuroimaging signatures linked to different pathologies. The newly deep learning-derived DeepSPARE indices are precise, pathology-sensitive, and single-valued noninvasive neuroimaging metrics, bridging the traditional widely available in-vivo T1 imaging with histopathology.