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Abstract Parkinson’s disease (PD) is clinically heterogeneous, which suggests the existence of subtypes; however, there has been no consensus regarding their characteristics. This study included 633 PD individuals across distinct cohorts: unmedicated de novo PD, medicated PD, mild-moderate PD, and a cohort based on diagnostic work-up in clinical practice. Additionally, 233 controls were included. Clustering based on cortical and subcortical gray matter measures was conducted with and without adjusting for global atrophy in the entire PD sample and validated within each cohort. Subtypes were characterized using baseline and longitudinal demographic and clinical data. Unadjusted results identified three clusters showing a gradient of neurodegeneration and symptom severity across the entire sample and the individual cohorts. When adjusting for global atrophy eight clusters were identified in the entire sample, lacking consistency in individual cohorts. This study identified atrophy-based subtypes in PD, emphasizing the significant impact of global atrophy on subtype number, patterns, and interpretation in cross-sectional analyses.

Abstract Background Alzheimer’s disease (AD) is a progressive neurodegenerative disorder where pathophysiological changes begin decades before the onset of clinical symptoms. Analysis of brain atrophy patterns using structural MRI and multivariate data analysis are an effective tool in identifying patients with subjective cognitive decline (SCD) at higher risk of progression to AD dementia. Atrophy patterns obtained from models trained to classify advanced AD versus normal subjects, may not be optimal for subjects at an early stage, like SCD. In this study, we compared the accuracy of the SCD progression prediction using the ‘severity index’ generated using a standard classification model trained on patients with AD dementia versus a new model trained on β-amyloid (Aβ) positive patients with amnestic mild cognitive impairment (aMCI). Methods We used structural MRI data of 504 patients from the Swedish BioFINDER-1 study cohort (cognitively normal (CN), Aβ-negative = 220; SCD, Aβ positive and negative = 139; aMCI, Aβ-positive = 106; AD dementia = 39). We applied multivariate data analysis to create two predictive models trained to discriminate CN individuals from either individuals with Aβ positive aMCI or AD dementia. Models were applied to individuals with SCD to classify their atrophy patterns as either high-risk “disease-like” or low-risk “CN-like”. Clinical trajectory and model accuracy were evaluated using 8 years of longitudinal data. Results In predicting progression from SCD to MCI or dementia, the standard, dementia-based model, reached 100% specificity but only 10.6% sensitivity, while the new, aMCI-based model, reached 72.3% sensitivity and 60.9% specificity. The aMCI-based model was superior in predicting progression from SCD to MCI or dementia, reaching a higher receiver operating characteristic area under curve (AUC = 0.72; P = 0.037) in comparison with the dementia-based model (AUC = 0.57). Conclusion When predicting conversion from SCD to MCI or dementia using structural MRI data, prediction models based on individuals with milder levels of atrophy (i.e. aMCI) may offer superior clinical value compared to standard dementia-based models.

Abstract Background Multimodal lifestyle interventions can benefit overall health, including cognition, in populations at-risk for dementia. However, little is known about the effect of lifestyle interventions in patients with prodromal Alzheimer’s disease (AD). Even less is known about dietary intake and adherence to dietary recommendations within this population making it difficult to design tailored interventions for them. Method A 6-month MIND-ADmini pilot randomized controlled trial (RCT) was conducted among 93 participants with prodromal AD in Sweden, Finland, Germany, and France. Three arms were included in the RCT: 1) multimodal lifestyle intervention (nutritional guidance, exercise, cognitive training, vascular/metabolic risk management, and social stimulation); 2) multimodal lifestyle intervention + medical food product; and 3) regular health advice (control group). Adherence to dietary advice was assessed with a brief food intake questionnaire by using the Healthy Diet Index (HDI) and Mediterranean Diet Adherence Screener (MEDAS). The intake of macro- and micronutrients were analyzed on a subsample using 3-day food records. Results The dietary quality in the intervention groups, pooled together, improved compared to that of the control group at the end of the study, as measured with by HDI (p = 0.026) and MEDAS (p = 0.008). The lifestyle-only group improved significantly more in MEDAS (p = 0.046) and almost significantly in HDI (p = 0.052) compared to the control group, while the lifestyle + medical food group improved in both HDI (p = 0.042) and MEDAS (p = 0.007) during the study. There were no changes in macro- or micronutrient intake for the intervention groups at follow-up; however, the intakes in the control group declined in several vitamins and minerals when adjusted for energy intake. Conclusion These results suggest that dietary intervention as part of multimodal lifestyle interventions is feasible and results in improved dietary quality in a population with prodromal AD. Nutrient intakes remained unchanged in the intervention groups while the control group showed a decreasing nutrient density. Trial registration ClinicalTrials.gov NCT03249688, 2017–07-08.

Abstract Deep learning approaches for clinical predictions based on magnetic resonance imaging data have shown great promise as a translational technology for diagnosis and prognosis in neurological disorders, but its clinical impact has been limited. This is partially attributed to the opaqueness of deep learning models, causing insufficient understanding of what underlies their decisions. To overcome this, we trained convolutional neural networks on structural brain scans to differentiate dementia patients from healthy controls, and applied layerwise relevance propagation to procure individual-level explanations of the model predictions. Through extensive validations we demonstrate that deviations recognized by the model corroborate existing knowledge of structural brain aberrations in dementia. By employing the explainable dementia classifier in a longitudinal dataset of patients with mild cognitive impairment, we show that the spatially rich explanations complement the model prediction when forecasting transition to dementia and help characterize the biological manifestation of disease in the individual brain. Overall, our work exemplifies the clinical potential of explainable artificial intelligence in precision medicine.

Abstract Sex differences permeate many aspects of dementia with Lewy bodies (DLB), yet sex differences in patterns of neurodegeneration in DLB remain largely unexplored. Here, we test whether grey matter networks differ between sexes in DLB and compare these findings to sex differences in healthy controls. In this cross-sectional study, we analysed clinical and neuroimaging data of patients with DLB and cognitively healthy controls matched for age and sex. Grey matter networks were constructed by pairwise correlations between 58 regional volumes after correction for age, intracranial volume, and centre. Network properties were compared between sexes and diagnostic groups. Additional analyses were conducted on w-scored data to identify DLB-specific sex differences. Data from 119 (68.7 ± 8.4 years) men and 45 women (69.9 ± 9.1 years) with DLB, and 164 healthy controls were included in this study. Networks of men had a lower nodal strength compared to women. In comparison to healthy women, the grey matter networks of healthy men showed a higher global efficiency, modularity, and fewer modules. None of the network measures showed significant sex differences in DLB. Comparing DLB patients with healthy controls revealed global differences in women and more local differences in men. Modular analyses showed a more distinct demarcation between cortical and subcortical regions in men compared with women. While topologies of grey matter networks differed between sexes in healthy controls, those sex differences were diluted in DLB patients. These findings suggest a disease-driven convergence of neurodegenerative patterns in women and men with DLB, which may inform precision medicine in DLB.

Abstract Background Sarcopenia is associated with multiple adverse outcomes. Traditional methods to determine low muscle mass for the diagnosis of sarcopenia are mainly based on dual‐energy X‐ray absorptiometry (DXA), whole‐body magnetic resonance imaging (MRI) and bioelectrical impedance analysis. These tests are not always available and are rather time consuming and expensive. However, many brain and head diseases require a head MRI. In this study, we aim to provide a more accessible way to detect sarcopenia by comparing the traditional method of DXA lean mass estimation versus the tongue and masseter muscle mass assessed in a standard brain MRI. Methods The H70 study is a longitudinal study of older people living in Gothenburg, Sweden. In this cross‐sectional analysis, from 1203 participants aged 70 years at baseline, we included 495 with clinical data and MRI images available. We used the appendicular lean soft tissue index (ALSTI) in DXA images as our reference measure of lean mass. Images from the masseter and tongue were analysed and segmented using 3D Slicer. For the statistical analysis, the Spearman correlation coefficient was used, and concordance was estimated with the Kappa coefficient. Results The final sample consisted of 495 participants, of which 52.3% were females. We found a significant correlation coefficient between both tongue (0.26) and masseter (0.33) with ALSTI (P

Recent reviews of using therapeutic carbohydrate reduction to treat metabolic disease in paediatric patients have consistently made errors in the form of bias against recommending this nutrient-dense eating pattern despite strong evidence for its use in adults and emerging evidence in paediatric patients. The purpose of this perspective is to review these errors, which include conflating 4:1 ketogenic diets with well-formulated ketogenic diets and the needless medicalisation of using therapeutic carbohydrate reduction in paediatric populations.

IntroductionHeterogeneity in downstream atrophy in Alzheimer’s disease (AD) is predominantly investigated in relation to pathological hallmarks (Aβ, tau) and co-pathologies (cerebrovascular burden) independently. However, the proportional contribution of each pathology in determining atrophy pattern remains unclear. We assessed heterogeneity in atrophy using two recently conceptualized dimensions: typicality (typical AD atrophy at the center and deviant atypical atrophy on either extreme including limbic predominant to hippocampal sparing patterns) and severity (overall neurodegeneration spanning minimal atrophy to diffuse typical AD atrophy) in relation to Aβ, tau, and cerebrovascular burden.MethodsWe included 149 Aβ + individuals on the AD continuum (cognitively normal, prodromal AD, AD dementia) and 163 Aβ− cognitively normal individuals from the ADNI. We modeled heterogeneity in MRI-based atrophy with continuous-scales of typicality (ratio of hippocampus to cortical volume) and severity (total gray matter volume). Partial correlation models investigated the association of typicality/severity with (a) Aβ (global Aβ PET centiloid), tau (global tau PET SUVR), cerebrovascular (total white matter hypointensity volume) burden (b) four cognitive domains (memory, executive function, language, visuospatial composites). Using multiple regression, we assessed the association of each pathological burden and typicality/severity with cognition.Results(a) In the AD continuum, typicality (r = −0.31, p

Abstract Background Donepezil is an approved therapy for the treatment of Alzheimer’s disease (AD). Results across clinical trials have been inconsistent, which may be explained by design-methodological issues, the pathophysiological heterogeneity of AD, and diversity of included study participants. We investigated whether response to donepezil differs in mild cognitive impaired (MCI) individuals demonstrating different magnetic resonance imaging (MRI) subtypes. Methods From the Hippocampus Study double-blind, randomized clinical trial, we included 173 MCI individuals (donepezil = 83; placebo = 90) with structural MRI data, at baseline and at clinical follow-up assessments (6–12-month). Efficacy outcomes were the annualized percentage change (APC) in hippocampal, ventricular, and total grey matter volumes, as well as in the AD cortical thickness signature. Participants were classified into MRI subtypes as typical AD, limbic-predominant, hippocampal-sparing, or minimal atrophy at baseline. We primarily applied a subtyping approach based on continuous scale of two subtyping dimensions. We also used the conventional categorical subtyping approach for comparison. Results Donepezil-treated MCI individuals showed slower atrophy rates compared to the placebo group, but only if they belonged to the minimal atrophy or hippocampal-sparing subtypes. Importantly, only the continuous subtyping approach, but not the conventional categorical approach, captured this differential response. Conclusions Our data suggest that individuals with MCI, with hippocampal-sparing or minimal atrophy subtype, may have improved benefit from donepezil, as compared with MCI individuals with typical or limbic-predominant patterns of atrophy. The newly proposed continuous subtyping approach may have advantages compared to the conventional categorical approach. Future research is warranted to demonstrate the potential of subtype stratification for disease prognosis and response to treatment. Trial registration ClinicalTrial.gov NCT00403520. Submission Date: November 21, 2006.

Introduction: Exposure to dementia risk factors throughout life can lead to brain atrophy and older- appearing brains on neuroimaging. Resilience mechanisms can help sustain the brain structure (brain maintenance, BM) and/or compensate for the neuropathological damage (cognitive reserve, CR), preserving cognition. Traditional proxy-based approaches (e.g., education) face challenges in measuring these mechanisms as they cannot capture the core biological dimension. However, with deep learning is possible to develop algorithms predicting the biological age of the brain from raw brain images. This study investigated whether differences between predicted brain age and chronological age (PBA-CA) can be used as a marker of BM and/or CR following the NIH-funded Collaboratory on Reserve and Resilience framework. Methods: The study population included 719 dementia-free septuagenarians from the Gothenburg H70-1944 MRI cohort. We applied the deep learning model—developed in-house using minimally processed T1-w MRI from ∼17000 neurologically intact individuals from UK Biobank, ADNI, AIBL, and GENIC and validated via hold-out and cross-validation approaches—to H70 participants’ MRI to predict their brain age, and computed PBA-CA. MRI markers of brain pathology included cortical thickness (overall and Alzheimer's disease-related), cerebral small vessel disease (SVD; individual markers and score), and white-matter microstructural alterations (DTI's fractional anisotropy). Global and domain-specific cognitive function was based on composite scores from ten tests. Data analysis included regression models and stratification by sex. Results: In the brain, decreasing differences between PBA and CA (reflecting younger-appearing brains) were associated with a thicker brain (overall and AD signature areas), lower SVD score—particularly lower white matter hyperintensities volume, lacunes, large infarcts—, and higher fractional anisotropy (more integrity). Decreasing differences were also related to better cognitive performance, globally and in attention/speed, executive function, and visuospatial abilities. In stratified analysis by sex, such associations were evident in men but not in women, except fractional anisotropy [robust regressions’ β-coefficients for women -4.89 (95%CI-8.87,-0.90) and men -23.7 (95%CI -32.9,-14.4)]. Discussion: Negative differences PBA

Introduction: Distinct patterns of brain atrophy exists in common neurodegenerative disorders. However, whether these patterns are present also in non-clinical populations remains elusive, yet crucial to detect people at-risk of dementia. This cross-sectional study aimed to identify specific patterns of grey matter atrophy and their determinants among cognitively intact older individuals. Methods: We included 746 septuagenarians from the Gothenburg H70-1944 Birth cohort who did not have dementia or neuropsychiatric disorders and with available MRI. Unsupervised classification using Random Forest was applied to identify distinct patterns of brain atrophy based on grey matter volume and thickness from 34 cortical and 7 subcortical regions. Linear and multinomial logistic regression models were used to compare the clusters pairwise (cluster 1 vs. remaining) and estimate their associations with sociodemographic and vascular risk factors, medical conditions, lifestyle, and genetic factors. Furthermore, the clusters were compared in relation to cognition and biomarkers (MRI, DTI, CSF, or blood) of neurodegeneration, cerebrovascular pathology, inflammation, and lipidic abnormalities. Results: We identified 5 clusters that significantly differed in atrophy severity and typicality (location). Cluster 1 was the most prevalent (n=278, 37.3%). As it showed no atrophy differences from the overall sample, Cluster 1 was set as the reference group. Cluster 2 (n=142, 19%) exhibited diffused atrophy, primarily concentrated in frontal regions. Cluster 3 (n=121, 16.2%) and Cluster 4 (n=157, 21%) were characterized by the least atrophy, particularly in frontotemporal and temporal regions. Cluster 5 (n=48, 6.4%) showed more atrophy in parietal-occipital regions.Moreover, regression analyses indicated that small vessel disease (white matter hyperintensities and lacunes), vascular risk factors (smoking, overweight/obesity, alcohol risk consumption), cardiometabolic disorders, lipid alterations, depression, and education play a key role in separating the clusters. Discussion: Our study revealed the presence of distinct patterns of atrophy in elderly individuals otherwise cognitively healthy. Small vessel disease, vascular risk factors, and cardiometabolic conditions may contribute to these patterns. These findings highlight the importance of vascular health to preserve cognitive function and brain structure in old age.

Introduction: Cardiovascular and metabolic disorders rarely occur alone in old age but often cluster together in the so-called metabolic syndrome (MetS). While individual components of MetS, such as obesity, hypertension, and diabetes, have been linked to dementia and brain atrophy, their combined impact on cognitive and brain aging remains poorly explored. Our current study aims to investigate the relationship between MetS and markers of neurodegenerative and cerebrovascular pathologies while also considering potential differences between sexes. Methods: We included 741 cognitively intact septuagenarians from the Gothenburg H70-Birth cohort 1944 with available brain MRI (286 had cerebrospinal fluid [CSF] sampling). MetS was defined by standard criteria (at least three among adiposity, raised blood pressure, blood glucose, reduced HDL-cholesterol, elevated triglycerides). MRI markers of brain pathology included overall (mean cortical thickness) and Alzheimer's disease (AD; average cortical thickness in signature areas) neurodegeneration, cerebral small vessel disease (SVD; markers such as white matter hyperintensity, lacunes, microbleeds, and perivascular spaces, and SVD score) and DTI's white- matter microstructural changes (fractional anisotropy). CSF biomarkers included t-tau, neurofilament light, neurogranin (overall neurodegeneration), Aβ42 and p-tau (AD), and CSF/serum albumin ratio (blood-brain barrier integrity). Analyses included regression models and stratification by sex. Results: Overall, 410 participants (53%, n=196 women) had MetS. MetS was associated with increased odds of overall (OR=2.0, 95%CI 1.4–2.9) and AD-related (OR=1.8, 95%CI 1.4–2.6) neurodegeneration, high SVD burden (presence of ≥3 SVD compared to none: OR=2.1, 95%CI 1.1–4.3), and white-matter microstructural changes. Within the SVD markers, MetS was particularly related to white matter hyperintensities, lacunes, and perivascular spaces in basal ganglia. Furthermore, MetS was associated with alterations of blood-brain barrier integrity in the CSF subsample. Sex differences showed increased SVD burden in men with MetS, especially enlarged perivascular spaces (OR=2.9, 95%CI 1.7–5.0), while no differences were observed in women. No associations were detected with amyloid and tau. Discussion: MetS in late life is linked to neurodegenerative and cerebrovascular pathologies, with SVD more prominent in men with MetS, but not in women.

Introduction: Heterogeneity in downstream atrophy is predominantly investigated in relation to pathological hallmarks (Aβ, tau) while the contribution of vascular pathology remains unclear in Alzheimer's disease (AD). We assessed heterogeneity in atrophy using two recently conceptualized dimensions: typicality (capturing degree of atypical atrophy spanning limbic predominant to hippocampal sparing patterns) and severity (capturing disease stage spanning typical AD to minimal atrophy patterns) in relation to Aβ, tau, vascular pathologies. Methods: We included 149 Aβ+ individuals (AD continuum: cognitively normal, prodromal AD, AD dementia) and 163 Aβ- cognitively normal individuals from the ADNI. We modelled heterogeneity in MRI-based atrophy with continuous-scales of typicality (ratio of hippocampus to cortical volume) and severity (total grey matter volume adjusted for intracranial volume). Partial correlation models investigated the association of typicality/severity with (a) Aβ (global PET composite in centiloid), tau (global PET SUVR), vascular (total white matter hypointensity volume in MRI) burden, and (b) four cognitive domains (memory, executive function, language, visuospatial composites) controlled for the three pathologies. Severity was adjusted when assessing typicality and vice versa. Results: (a) In the AD continuum, typicality was associated with tau pathology (r=-0.28, p

Introduction: Genome-wide association studies (GWASs) have shown that several genes, beside the “high-effect” APOE gene, are involved in modulating the risk of dementia and longevity, and some of these genes overlap between the traits. Considering longevity, there are also associations with genes related to other age-related disorders, such as diabetes and cancer. Previous studies have shown associations between genetics of dementia and longevity and measures reflecting brain age, but the vast majority of these studies were performed in age heterogeneous samples. The aim of our study is to investigate how polygenic scores for dementia, general cognitive function, and longevity, associate with predicted biological brain age in 70-year-olds from the general population. Methods: The study sample included 710 individuals from the Gothenburg H70 1944 cohort with data on genetics and MRI (after QC). Polygenic scores (for longevity, general cognitive function, and Alzheimer's disease) were constructed based on genetic data derived by genome-wide genotyping and summary statistics from previous GWASs. A deep learning model, developed in- house using minimally processed T1-w MRI from ∼17000 neurologically intact individuals from UK Biobank, ADNI, AIBL, and GENIC and validated via hold-out and cross-validation approaches, was applied to generate the predicted brain ages. Statistical analyses were performed using linear regression, in the total sample and after stratification by sex. Results: Higher polygenic score for longevity was associated with lower biological brain age in men (β = -0.24; SD = 0.10; p = 0.02), and higher polygenic score for general cognitive performance was associated with lower biological brain age in women (β = -0.25; SD = 0.10; p = 0.02). No associations were seen with polygenic score for Alzheimer's disease. Discussion: The results of our study indicate that genetic variation related to longevity and general cognitive performance is associated with biological brain age in 70-year-olds, but the identified associations appear to differ between the sexes. A possible explanation might be divergent pathways involved in biological aging of the brain in men and women.

Introduction: A higher adherence to a Mediterranean diet (MeDi) has been associated with more brain integrity, and slower cognitive decline. In 2019, the EAT-Lancet commission presented a new, mainly plant-based healthy reference diet, aimed to be environmentally sustainable. However, health benefits of the diet have been questioned, and the impact on cognition and brain health has not been established. This cross-sectional study aims to compare the EAT-Lancet diet vs. the MeDi in relation to cognitive function and neuroimaging markers of neurodegenerative and cerebrovascular alterations among older adults. Methods: The study sample included 615 dementia-free 70-year-olds (54% women) from the Gothenburg H70 Birth Cohort study (examined 2014–16). Dietary adherence was measured with an EAT-Lancet dietary index (score 0–42), and a MeDi index (score 0–9). The scores were transformed into z-scores to enable comparability. Brain magnetic resonance imaging measures included mean total cortical thickness (marker of overall neurodegeneration), and a global small vessel disease score (SVD score, ranged 0–3). Cognitive performance was measured with a global cognitive composite score, derived from eight cognitive tests. Linear and ordinal regression analyses were performed adjusted for sex, and multi-adjusted for potential confounders. Results: The sex-adjusted linear regression analyses showed that higher adherence to the MeDi, and to the EAT-Lancet diet were associated with better cognitive performance. After further adjusting for energy intake, physical activity, smoking status, and education, only the MeDi–cognition association remained (β: 0.071, 95% CI: 0.024–0.117, p=0.003). Furthermore, both adherence to the MeDi (β: 0.007, 95% CI: 0.000–0.013, p=0.046), and to the EAT-Lancet diet (β: 0.009, 95% CI: 0.003–0.016, p=0.005) were positively associated with cortical thickness (less atrophy) in the multi-adjusted model. The EAT-Lancet diet was associated with lower SVD burden in the sex-adjusted model (OR: 0.85, 95% CI: 0.73–0.99, p=0.035), but not in the multi-adjusted model. No association was detected between the MeDi and SVD score. Discussion: Results from this study confirms previously found associations between higher adherence to a MeDi and better cognitive function and brain integrity. The findings also indicate that an environmentally sustainable diet may be beneficial for brain health.

IntroductionIn the last few years, several models trying to calculate the biological brain age have been proposed based on structural magnetic resonance imaging scans (T1-weighted MRIs, T1w) using multivariate methods and machine learning. We developed and validated a convolutional neural network (CNN)-based biological brain age prediction model that uses one T1w MRI preprocessing step when applying the model to external datasets to simplify implementation and increase accessibility in research settings. Our model only requires rigid image registration to the MNI space, which is an advantage compared to previous methods that require more preprocessing steps, such as feature extraction.MethodsWe used a multicohort dataset of cognitively healthy individuals (age range = 32.0–95.7 years) comprising 17,296 MRIs for training and evaluation. We compared our model using hold-out (CNN1) and cross-validation (CNN2–4) approaches. To verify generalisability, we used two external datasets with different populations and MRI scan characteristics to evaluate the model. To demonstrate its usability, we included the external dataset’s images in the cross-validation training (CNN3). To ensure that our model used only the brain signal on the image, we also predicted brain age using skull-stripped images (CNN4).Results:The trained models achieved a mean absolute error of 2.99, 2.67, 2.67, and 3.08 years for CNN1–4, respectively. The model’s performance in the external dataset was in the typical range of mean absolute error (MAE) found in the literature for testing sets. Adding the external dataset to the training set (CNN3), overall, MAE is unaffected, but individual cohort MAE improves (5.63–2.25 years). Salience maps of predictions reveal that periventricular, temporal, and insular regions are the most important for age prediction.DiscussionWe provide indicators for using biological (predicted) brain age as a metric for age correction in neuroimaging studies as an alternative to the traditional chronological age. In conclusion, using different approaches, our CNN-based model showed good performance using one T1w brain MRI preprocessing step. The proposed CNN model is made publicly available for the research community to be easily implemented and used to study ageing and age-related disorders.

Abstract The relation between cerebrospinal fluid (CSF) biomarkers of Alzheimer's disease (AD) and magnetic resonance imaging (MRI) measures is poorly understood in cognitively healthy individuals from the general population. Participants’ (n = 226) mean age was 70.9 years (SD = 0.4). CSF concentrations of amyloid beta (Aβ)1‐42, total tau (t‐tau), phosphorylated tau (p‐tau), neurogranin, and neurofilament light, and volumes of hippocampus, amygdala, total basal forebrain (TBF), and cortical thickness were measured. Linear associations between CSF biomarkers and MRI measures were investigated. In Aβ1‐42 positives, higher t‐tau and p‐tau were associated with smaller hippocampus (P = 0.001 and P = 0.003) and amygdala (P = 0.005 and P = 0.01). In Aβ1‐42 negatives, higher t‐tau, p‐tau, and neurogranin were associated with larger TBF volume (P = 0.001, P = 0.001, and P = 0.01). No associations were observed between the CSF biomarkers and an AD signature score of cortical thickness. AD‐specific biomarkers in cognitively healthy 70‐year‐olds may be related to TBF, hippocampus, and amygdala. Lack of association with cortical thickness might be due to early stage of disease.

Abstract Background Subtypes and patterns are defined using tau-PET (tau pathology) and structural MRI (atrophy) in Alzheimer’s disease (AD). However, the relationship between tau pathology and atrophy across these subtypes/patterns remains unclear. Therefore, we investigated the biological association between baseline tau-PET patterns and longitudinal atrophy in the AD continuum; and the methodological characterization of heterogeneity as a continuous phenomenon over the conventional discrete subgrouping. Methods In 366 individuals (amyloid-beta-positive cognitively normal, prodromal AD, AD dementia; amyloid-beta-negative cognitively normal), we examined the association between tau-PET patterns and longitudinal MRI. We modeled tau-PET patterns as a (a) continuous phenomenon with key dimensions: typicality and severity; and (b) discrete phenomenon by categorization into patterns: typical, limbic predominant, cortical predominant and minimal tau. Tau-PET patterns and associated longitudinal atrophy were contextualized within the Amyloid/Tau/Neurodegeneration (A/T/N) biomarker scheme. Results Localization and longitudinal atrophy change vary differentially across different tau-PET patterns in the AD continuum. Atrophy, a downstream event, did not always follow a topography akin to the corresponding tau-PET pattern. Further, heterogeneity as a continuous phenomenon offered an alternative and useful characterization, sharing correspondence with the conventional subgrouping. Tau-PET patterns also show differential A/T/N profiles. Conclusions The site and rate of atrophy are different across the tau-PET patterns. Heterogeneity should be treated as a continuous, not discrete, phenomenon for greater sensitivity. Pattern-specific A/T/N profiles highlight differential multimodal interactions underlying heterogeneity. Therefore, tracking multimodal interactions among biomarkers longitudinally, modeling disease heterogeneity as a continuous phenomenon, and examining heterogeneity across the AD continuum could offer avenues for precision medicine.

Abstract Dementia with Lewy bodies (DLB) is a neurodegenerative disorder with a wide heterogeneity of symptoms, which suggests the existence of different subtypes. We used data-driven analysis of magnetic resonance imaging (MRI) data to investigate DLB subtypes. We included 165 DLB from the Mayo Clinic and 3 centers from the European DLB consortium and performed a hierarchical cluster analysis to identify subtypes based on gray matter (GM) volumes. To characterize the subtypes, we used demographic and clinical data, as well as β-amyloid, tau, and cerebrovascular biomarkers at baseline, and cognitive decline over three years. We identified 3 subtypes: an older subtype with reduced cortical GM volumes, worse cognition, and faster cognitive decline (n = 49, 30%); a subtype with low GM volumes in fronto-occipital regions (n = 76, 46%); and a subtype of younger patients with the highest cortical GM volumes, proportionally lower GM volumes in basal ganglia and the highest frequency of cognitive fluctuations (n = 40, 24%). This study shows the existence of MRI subtypes in DLB, which may have implications for clinical workout, research, and therapeutic decisions.

Abstract Neuropathology and neuroimaging studies have identified several subtypes of Alzheimer’s disease (AD): hippocampal sparing AD, typical AD, and limbic predominant AD. An unresolved question is whether hippocampal sparing AD cases can present with neurofibrillary tangles (NFT) in association cortices while completely sparing the hippocampus. To address that question, we conducted a systematic review and performed original analyses on tau positron emission tomography (PET) data. We searched EMBASE, PubMed, and Web of Science databases until October 2022. We also implemented several methods for AD subtyping on tau PET to identify hippocampal sparing AD cases. Our findings show that seven out of the eight reviewed neuropathologic studies included cases at Braak stages IV or higher and therefore, could not identify hippocampal sparing cases with NFT completely sparing the hippocampus. In contrast, tau PET did identify AD participants with tracer retention in the association cortex while completely sparing the hippocampus. We conclude that tau PET can identify hippocampal sparing AD cases with NFT completely sparing the hippocampus. Based on the accumulating data, we suggest two possible pathways of tau spread: (1) a canonical pathway with early involvement of transentorhinal cortex and subsequent involvement of limbic regions and association cortices, and (2) a less common pathway that affects association cortices with limbic involvement observed at end stages of the disease or not at all.

Abstract Understanding Alzheimer’s disease (AD) heterogeneity is important for understanding the underlying pathophysiological mechanisms of AD. However, AD atrophy subtypes may reflect different disease stages or biologically distinct subtypes. Here we use longitudinal magnetic resonance imaging data (891 participants with AD dementia, 305 healthy control participants) from four international cohorts, and longitudinal clustering to estimate differential atrophy trajectories from the age of clinical disease onset. Our findings (in amyloid-β positive AD patients) show five distinct longitudinal patterns of atrophy with different demographical and cognitive characteristics. Some previously reported atrophy subtypes may reflect disease stages rather than distinct subtypes. The heterogeneity in atrophy rates and cognitive decline within the five longitudinal atrophy patterns, potentially expresses a complex combination of protective/risk factors and concomitant non-AD pathologies. By alternating between the cross-sectional and longitudinal understanding of AD subtypes these analyses may allow better understanding of disease heterogeneity.

Abstract Background Dementia with Lewy bodies (DLB) includes various core clinical features that result in different phenotypes. In addition, Alzheimer’s disease (AD) and cerebrovascular pathologies are common in DLB. All this increases the heterogeneity within DLB and hampers clinical diagnosis. We addressed this heterogeneity by investigating subgroups of patients with similar biological, clinical, and demographic features. Methods We studied 107 extensively phenotyped DLB patients from the European DLB consortium. Factorial analysis of mixed data (FAMD) was used to identify dimensions in the data, based on sex, age, years of education, disease duration, Mini-Mental State Examination (MMSE), cerebrospinal fluid (CSF) levels of AD biomarkers, core features of DLB, and regional brain atrophy. Subsequently, hierarchical clustering analysis was used to subgroup individuals based on the FAMD dimensions. Results We identified 3 dimensions using FAMD that explained 38% of the variance. Subsequent hierarchical clustering identified 4 clusters. Cluster 1 was characterized by amyloid-β and cerebrovascular pathologies, medial temporal atrophy, and cognitive fluctuations. Cluster 2 had posterior atrophy and showed the lowest frequency of visual hallucinations and cognitive fluctuations and the worst cognitive performance. Cluster 3 had the highest frequency of tau pathology, showed posterior atrophy, and had a low frequency of parkinsonism. Cluster 4 had virtually normal AD biomarkers, the least regional brain atrophy and cerebrovascular pathology, and the highest MMSE scores. Conclusions This study demonstrates that there are subgroups of DLB patients with different biological, clinical, and demographic characteristics. These findings may have implications in the diagnosis and prognosis of DLB, as well as in the treatment response in clinical trials.

Abstract Background Epidemiological studies show an association between masticatory function and cognitive impairment. This has further strengthened the notion that tooth loss and impaired masticatory function may be risk factors for dementia and cognitive decline. Animal experiments have indicated a causal relationship and several possible mechanisms have been discussed. This evidence is, however, lacking in humans. Therefore, in the current interventional study, we aim to investigate the effect of rehabilitation of masticatory function on cognition in older adults. Methods Eighty patients indicated for prosthodontic rehabilitation will be randomly assigned to an experimental or a control group. Participants will conduct neuropsychological assessments, masticatory performance tests, saliva tests, optional magnetic resonance imaging, and answer questionnaires on oral health impact profiles and hospital anxiety and depression scale before, 3 months, and 1 year after oral rehabilitation. The difference between the two groups is that the control group will be tested an additional time, (at an interval of about 3 months) before the onset of the oral rehabilitation procedure. The primary outcome is a change in measures of episodic memory performance. Discussion Although tooth loss and masticatory function are widespread in older people, it is still an underexplored modifiable risk factor potentially contributing to the development of cognitive impairment. If rehabilitation of masticatory function shows positive effects on the neurocognitive function, this will have great implications on future health care for patients with impaired masticatory status. The present project may provide a new avenue for the prevention of cognitive decline in older individuals. Trial registration: The protocol for the study was retrospectively registered in ClinicalTrials.gov Identifier: NCT04458207, dated 02-07-2020.

ObjectiveThe apolipoprotein E (APOE) ε4 allele is the main genetic risk factor for dementia and Alzheimer's disease (AD), but the underlying mechanism for the increased risk is not well understood. Cerebral small vessel disease (SVD) is prevalent among patients with cognitive impairment and is thought to play an important role in the pathophysiology of dementia. We aimed to investigate the association between the APOE ε genotype and magnetic resonance imaging (MRI) markers of SVD in a memory clinic population.Material and MethodsThis is a cross-sectional study with a total of 520 patients undergoing dementia investigation, including an MRI brain scan and APOE genotyping in all patients enrolled, and cerebrospinal fluid (CSF) analysis for routine AD biomarkers in 399 patients. MR images were assessed for markers of SVD: cerebral microbleeds (CMBs), cortical superficial siderosis, intracerebral hemorrhage, white matter hyperintensities, lacunar infarcts, and enlarged perivascular spaces.ResultsApolipoprotein E carriers with AD had a higher number of CMBs when looking at all brain regions and lobar brain regions (p < 0.001). A lower number of CMBs were seen in APOE ε2 (p < 0.05), ε3 and ε3/3 carriers (p < 0.001) when looking at all brain regions. A higher number of CMBs in deep and infratentorial regions were seen in APOE ε2 and ε3 (p < 0.05). In APOE ε4/4 carriers, CMBs, cortical superficial siderosis, white matter hyperintensities, and enlarged perivascular spaces were associated with lower levels of CSF amyloid β (Aβ) 42 in the whole cohort, and in individuals with AD and mild cognitive impairment (p < 0.05).ConclusionApolipoprotein E ε4 is associated with MRI markers of SVD related to amyloid pathology, specifically CMBs and Aβ42 plaque formation in the brain, as reflected by decreased CSF Aβ42 levels, whereas APOE ε3 and ε2 are associated with the markers of hypertensive arteriopathy, as reflected by the association with CMBs in deep and infratentorial brain regions.

Abstract Background Previous research has described distinct subtypes of Alzheimer’s disease (AD) based on the differences in regional patterns of brain atrophy on MRI. We conducted a data-driven exploration of distinct AD neurodegeneration subtypes using FDG-PET as a sensitive molecular imaging marker of neurodegenerative processes. Methods Hierarchical clustering of voxel-wise FDG-PET data from 177 amyloid-positive patients with AD dementia enrolled in the Alzheimer’s Disease Neuroimaging Initiative (ADNI) was used to identify distinct hypometabolic subtypes of AD, which were then further characterized with respect to clinical and biomarker characteristics. We then classified FDG-PET scans of 217 amyloid-positive patients with mild cognitive impairment (“prodromal AD”) according to the identified subtypes and studied their domain-specific cognitive trajectories and progression to dementia over a follow-up interval of up to 72 months. Results Three main hypometabolic subtypes were identified: (i) “typical” (48.6%), showing a classic posterior temporo-parietal hypometabolic pattern; (ii) “limbic-predominant” (44.6%), characterized by old age and a memory-predominant cognitive profile; and (iii) a relatively rare “cortical-predominant” subtype (6.8%) characterized by younger age and more severe executive dysfunction. Subtypes classified in the prodromal AD sample demonstrated similar subtype characteristics as in the AD dementia sample and further showed differential courses of cognitive decline. Conclusions These findings complement recent research efforts on MRI-based identification of distinct AD atrophy subtypes and may provide a potentially more sensitive molecular imaging tool for early detection and characterization of AD-related neurodegeneration variants at prodromal disease stages.

Abstract Subjective cognitive decline (SCD) has been proposed as a risk factor for future cognitive decline and dementia. Given the heterogeneity of SCD and the lack of consensus about how to classify this condition, different operationalization approaches still need to be compared. In this study, we used the same sample of individuals to compare different SCD operationalization approaches. We included 399 cognitively healthy individuals from a community-based cohort. SCD was assessed through nine questions about memory and non-memory subjective complaints. We applied four approaches to operationalize SCD: two hypothesis-driven approaches and two data-driven approaches. We characterized the resulting groups from each operationalization approach using multivariate methods on comprehensive demographic, clinical, cognitive, and neuroimaging data. We identified two main phenotypes: an amnestic phenotype characterized by an Alzheimer’s Disease (AD) signature pattern of brain atrophy; and an anomic phenotype, which was mainly related to cerebrovascular pathology. Furthermore, language complaints other than naming helped to identify a subgroup with subclinical cognitive impairment and difficulties in activities of daily living. This subgroup also showed an AD signature pattern of atrophy. The identification of SCD phenotypes, characterized by different syndromic and biomarker profiles, varies depending on the operationalization approach used. In this study we discuss how these findings may be used in clinical practice and research.

The genetic architecture underlying brainstem regions and how this links to common brain disorders is not well understood. Here, the authors use MRI and GWAS data from 27,034 individuals to identify genetic and morphological brainstem features that influence common brain disorders.

Abstract Background The heterogeneity within Alzheimer’s disease (AD) seriously challenges the development of disease-modifying treatments. We investigated volume of the basal forebrain, hippocampus, and precuneus in atrophy subtypes of AD and explored the relevance of subtype stratification in a small clinical trial on encapsulated cell biodelivery (ECB) of nerve growth factor (NGF) to the basal forebrain. Methods Structural MRI data was collected for 90 amyloid-positive patients and 69 amyloid-negative healthy controls at baseline, 6-, 12-, and 24-month follow-up. The effect of the NGF treatment was investigated in 10 biopsy-verified AD patients with structural MRI data at baseline and at 6- or 12-month follow-up. Patients were classified as typical, limbic-predominant, hippocampal-sparing, or minimal atrophy AD, using a validated visual assessment method. Volumetric analyses were performed using a region-of-interest approach. Results All AD subtypes showed reduced basal forebrain volume as compared with the healthy controls. The limbic-predominant subtype showed the fastest basal forebrain atrophy rate, whereas the minimal atrophy subtype did not show any significant volume decline over time. Atrophy rates of the hippocampus and precuneus also differed across subtypes. Our preliminary data from the small NGF cohort suggest that the NGF treatment seemed to slow the rate of atrophy in the precuneus and hippocampus in some hippocampal-sparing AD patients and in one typical AD patient. Conclusions The cholinergic system is differentially affected in distinct atrophy subtypes of AD. Larger studies in the future should confirm that this differential involvement of the cholinergic system may contribute to subtype-specific response to cholinergic treatment. Our preliminary findings suggest that future clinical trials should target specific subtypes of AD, or at least report treatment effects stratified by subtype. Trial registration ClinicalTrials.gov identifier: NCT01163825 . Registered 14 July 2010.

Abstract Background There remains a lack of large-scale clinical studies of cognitive impairment that aim to increase diagnostic and prognostic accuracy as well as validate previous research findings. The MemClin project will amass large quantities of cross-disciplinary data allowing for the construction of robust models to improve diagnostic accuracy, expand our knowledge on differential diagnostics, strengthen longitudinal prognosis, and harmonise examination protocols across centres. The current article describes the Memory Clinic (MemClin) project’s study-design, materials and methods, and patient characteristics. In addition, we present preliminary descriptive data from the ongoing data collection. Methods Nine out of ten memory clinics in the greater Stockholm area, which largely use the same examination methods, are included. The data collection of patients with different stages of cognitive impairment and dementia is coordinated centrally allowing for efficient and secure large-scale database construction. The MemClin project rest directly on the memory clinics examinations with cognitive measures, health parameters, and biomarkers. Results Currently, the MemClin project has informed consent from 1543 patients. Herein, we present preliminary data from 835 patients with confirmed cognitive diagnosis and neuropsychological test data available. Of those, 239 had dementia, 487 mild cognitive impairment (MCI), and 104 subjective cognitive impairment (SCI). In addition, we present descriptive data on visual ratings of brain atrophy and cerebrospinal fluid markers. Conclusions Based on our current progress and preliminary data, the MemClin project has a high potential to provide a large-scale database of 1200–1500 new patients annually. This coordinated data collection will allow for the construction of improved diagnostic and prognostic models for neurodegenerative disorders and other cognitive conditions in their naturalistic setting.

Brain tissue segmentation plays a crucial role in feature extraction, volumetric quantification, and morphometric analysis of brain scans. For the assessment of brain structure and integrity, CT is a non-invasive, cheaper, faster, and more widely available modality than MRI. However, the clinical application of CT is mostly limited to the visual assessment of brain integrity and exclusion of copathologies. We have previously developed two-dimensional (2D) deep learning-based segmentation networks that successfully classified brain tissue in head CT. Recently, deep learning-based MRI segmentation models successfully use patch-based three-dimensional (3D) segmentation networks. In this study, we aimed to develop patch-based 3D segmentation networks for CT brain tissue classification. Furthermore, we aimed to compare the performance of 2D- and 3D-based segmentation networks to perform brain tissue classification in anisotropic CT scans. For this purpose, we developed 2D and 3D U-Net-based deep learning models that were trained and validated on MR-derived segmentations from scans of 744 participants of the Gothenburg H70 Cohort with both CT and T1-weighted MRI scans acquired timely close to each other. Segmentation performance of both 2D and 3D models was evaluated on 234 unseen datasets using measures of distance, spatial similarity, and tissue volume. Single-task slice-wise processed 2D U-Nets performed better than multitask patch-based 3D U-Nets in CT brain tissue classification. These findings provide support to the use of 2D U-Nets to segment brain tissue in one-dimensional (1D) CT. This could increase the application of CT to detect brain abnormalities in clinical settings.

Background: Hypertension is an important risk factor for Alzheimer’s disease (AD). The pathophysiological mechanisms underlying the relationship between AD and hypertension are not fully understood, but they most likely involve microvascular dysfunction and cerebrovascular pathology. Although previous studies have assessed the impact of hypertension on different markers of brain integrity, no study has yet provided a comprehensive comparison of cerebrospinal fluid (CSF) biomarkers and structural brain differences between normotensive and hypertensive groups in a single and large cohort of older adults in relationship to cognitive performances.Objective: The aim of the present work was to investigate the differences in cognitive performances, CSF biomarkers and magnetic resonance imaging (MRI) of brain structure between normotensive, controlled hypertensive, uncontrolled hypertensive, and untreated hypertensive older adults from the Gothenburg H70 Birth Cohort Studies.Methods: As an indicator of vascular brain pathology, we measured white matter hyperintensities (WMHs), lacunes, cerebral microbleeds, enlarged perivascular space (epvs), and fractional anisotropy (FA). To assess markers of AD pathology/neurodegeneration, we measured hippocampal volume, temporal cortical thickness on MRI, and amyloid-β42, phosphorylated tau, and neurofilament light protein (NfL) in cerebrospinal fluid. Various neuropsychological tests were used to assess performances in memory, attention/processing speed, executive function, verbal fluency, and visuospatial abilities.Results: We found more white matter pathology in hypertensive compared to normotensive participants, with the highest vascular burden in uncontrolled participants (e.g., lower FA, more WMHs, and epvs). No significant difference was found in any MRI or CSF markers of AD pathology/neurodegeneration when comparing normotensive and hypertensive participants, nor among hypertensive groups. No significant difference was found in most cognitive functions between groups.Conclusion: Our results suggest that good blood pressure control may help prevent cerebrovascular pathology. In addition, hypertension may contribute to cognitive decline through its effect on cerebrovascular pathology rather than AD-related pathology. These findings suggest that hypertension is associated with MRI markers of vascular pathology in the absence of a significant decline in cognitive functions.

Automatic methods for feature extraction, volumetry, and morphometric analysis in clinical neuroscience typically operate on images obtained with magnetic resonance (MR) imaging equipment. Although CT scans are less expensive to acquire and more widely available than MR scans, their application is currently limited to the visual assessment of brain integrity and the exclusion of co-pathologies. CT has rarely been used for tissue classification because the contrast between grey matter and white matter was considered insufficient. In this study, we propose an automatic method for segmenting grey matter (GM), white matter (WM), cerebrospinal fluid (CSF), and intracranial volume (ICV) from head CT images. A U-Net deep learning model was trained and validated on CT images with MRI-derived segmentation labels. We used data from 744 participants of the Gothenburg H70 Birth Cohort Studies for whom CT and T1-weighted MR images had been acquired on the same day. Our proposed model predicted brain tissue classes accurately from unseen CT images (Dice coefficients of 0.79, 0.82, 0.75, 0.93 and 0.98 for GM, WM, CSF, brain volume and ICV, respectively). To contextualize these results, we generated benchmarks based on established MR-based methods and intentional image degradation. Our findings demonstrate that CT-derived segmentations can be used to delineate and quantify brain tissues, opening new possibilities for the use of CT in clinical practice and research.

Objectives: Cognitive aging has been extensively investigated using both univariate and multivariate analyses. Sophisticated multivariate approaches such as graph theory could potentially capture unknown complex associations between multiple cognitive variables. The aim of this study was to assess whether cognition is organized into a structure that could be called the “cognitive connectome,” and whether such connectome differs between age groups.Methods: A total of 334 cognitively unimpaired individuals were stratified into early-middle-age (37–50 years, n = 110), late-middle-age (51–64 years, n = 106), and elderly (65–78 years, n = 118) groups. We built cognitive networks from 47 cognitive variables for each age group using graph theory and compared the groups using different global and nodal graph measures.Results: We identified a cognitive connectome characterized by five modules: verbal memory, visual memory—visuospatial abilities, procedural memory, executive—premotor functions, and processing speed. The elderly group showed reduced transitivity and average strength as well as increased global efficiency compared with the early-middle-age group. The late-middle-age group showed reduced global and local efficiency and modularity compared with the early-middle-age group. Nodal analyses showed the important role of executive functions and processing speed in explaining the differences between age groups.Conclusions: We identified a cognitive connectome that is rather stable during aging in cognitively healthy individuals, with the observed differences highlighting the important role of executive functions and processing speed. We translated the connectome concept from the neuroimaging field to cognitive data, demonstrating its potential to advance our understanding of the complexity of cognitive aging.

Neural compensatory mechanisms associated with broad cognitive abilities have been studied. However, those associated with specific cognitive subdomains (e.g., verbal fluency) remain to be investigated in healthy aging. Here, we delineate: (a) neural substrates of verbal (phonemic) fluency, and (b) compensatory mechanisms mediating the association between these neural substrates and phonemic fluency. We analyzed resting-state functional magnetic resonance imaging from 133 right-handed, cognitively normal individuals who underwent the Controlled Oral Word Association Test (COWAT) to record their phonemic fluency. We evaluated functional connectivity in an established and extended language network comprising Wernicke, Broca, thalamic and anti-correlated modules. (a) We conducted voxel-wise multiple linear regression to identify the brain areas associated with phonemic fluency. (b) We used mediation effects of cognitive reserve, measured by the Wechsler Adult Intelligence Scale—Information subtest, upon the association between functional connectivity and phonemic fluency tested to investigate compensation. We found that: (a) Greater functional connectivity between the Wernicke module and brain areas within the anti-correlated module was associated with better performance in phonemic fluency, (b) Cognitive reserve was an unlikely mediator in younger adults. In contrast, cognitive reserve was a partial mediator of the association between functional connectivity and phonemic fluency in older adults, likely representing compensation to counter the effect of aging. We conclude that in healthy aging, higher performance in phonemic fluency at older ages could be attributed to greater functional connectivity partially facilitated by higher cognitive reserve, presumably reflecting compensatory mechanisms to minimize the effect of aging.

Subjective cognitive decline (SCD) may be the first sign of Alzheimer's disease (AD), but it can also reflect other pathologies such as cerebrovascular disease or conditions like depressive symptomatology. The role of depressive symptomatology in SCD is controversial. We investigated the association between depressive symptomatology, cerebrovascular disease, and SCD. We recruited 225 cognitively unimpaired individuals from a prospective community-based study [mean age (SD) = 54.64 (10.18); age range 35–77 years; 55% women; 123 individuals with one or more subjective cognitive complaints, 102 individuals with zero complaints]. SCD was assessed with a scale of 9 memory and non-memory subjective complaints. Depressive symptomatology was assessed with established questionnaires. Cerebrovascular disease was assessed with magnetic resonance imaging markers of white matter signal abnormalities (WMSA) and mean diffusivity (MD). We combined correlation, multiple regression, and mediation analyses to investigate the association between depressive symptomatology, cerebrovascular disease, and SCD. We found that SCD was associated with more cerebrovascular disease, older age, and increased depressive symptomatology. In turn, depressive symptomatology was not associated with cerebrovascular disease. Variability in MD was mediated by WMSA burden, presumably reflecting cerebrovascular disease. We conclude that, in our community-based cohort, depressive symptomatology is associated with SCD but not with cerebrovascular disease. In addition, depressive symptomatology did not influence the association between cerebrovascular disease and SCD. We suggest that therapeutic interventions for depressive symptomatology could alleviate the psychological burden of negative emotions in people with SCD, and intervening on vascular risk factors to reduce cerebrovascular disease should be tested as an opportunity to minimize neurodegeneration in SCD individuals from the community.

Alzheimer’s disease (AD) is a neurodegenerative disorder which spans several years from preclinical manifestations to dementia. In recent years, interest in the application of machine learning (ML) algorithms to personalized medicine has grown considerably, and a major challenge that such models face is the transferability from the research settings to clinical practice. The objective of this work was to demonstrate the transferability of the Subtype and Stage Inference (SuStaIn) model from well-characterized research data set, employed as training set, to independent less-structured and heterogeneous test sets representative of the clinical setting. The training set was composed of MRI data of 1043 subjects from the Alzheimer’s disease Neuroimaging Initiative (ADNI), and the test set was composed of data from 767 subjects from OASIS, Pharma-Cog, and ViTA clinical datasets. Both sets included subjects covering the entire spectrum of AD, and for both sets volumes of relevant brain regions were derived from T1-3D MRI scans processed with Freesurfer v5.3 cross-sectional stream. In order to assess the predictive value of the model, subpopulations of subjects with stable mild cognitive impairment (MCI) and MCIs that progressed to AD dementia (pMCI) were identified in both sets. SuStaIn identified three disease subtypes, of which the most prevalent corresponded to the typical atrophy pattern of AD. The other SuStaIn subtypes exhibited similarities with the previously defined hippocampal sparing and limbic predominant atrophy patterns of AD. Subject subtyping proved to be consistent in time for all cohorts and the staging provided by the model was correlated with cognitive performance. Classification of subjects on the basis of a combination of SuStaIn subtype and stage, mini mental state examination and amyloid-β1-42 cerebrospinal fluid concentration was proven to predict conversion from MCI to AD dementia on par with other novel statistical algorithms, with ROC curves that were not statistically different for the training and test sets and with area under curve respectively equal to 0.77 and 0.76. This study proves the transferability of a SuStaIn model for AD from research data to less-structured clinical cohorts, and indicates transferability to the clinical setting.

Abstract Introduction As cerebrospinal fluid (CSF) neurofilament light protein (NfL) and the CSF/serum albumin ratio (QAlb) are used in the clinical routine, the impact of demographic factors on these biomarkers is important to understand. Methods Participants were derived from two Swedish samples: the population‐based H70 Study (n = 308, age 70) and a clinical routine cohort (CSF NfL, n = 8995, QAlb, n = 39252, age 0 to 95). In the population‐based study, QAlb and NfL were examined in relation to sex, cardiovascular risk factors, and cerebral white matter lesions (WMLs). In the clinical cohort, QAlb and NfL sex differences were tested in relation to age. Results Men had higher QAlb and NfL concentrations and had higher QAlb and NfL concentrations from adolescence throughout life. NfL was not related to WML, but QAlb correlated positively with WMLs. Discussion The CSF NfL sex difference could not be explained by vascular pathology. Future studies should consider using different reference limits for men and women.

BackgroundIndividuals with early phase cognitive impairment are frequently affected by existential distress, social avoidance and associated health issues (including symptoms of stress, anxiety, and depression). The demand for efficient psychological support is crucial from both an individual and a societal perspective. We have developed a novel psychological intervention (Psychological Intervention tailored for Patients with Cognitive Impairment, PIPCI) manual for providing a non-medical path to enhanced psychological health in the cognitively impaired population. The current article provides specific information on the randomized controlled trial (RCT)-design and methods. The main hypothesis is that participants receiving PIPCI will increase their psychological flexibility (the ability to notice and accept interfering thoughts, emotions, and bodily sensations without acting on them, when this serves action in line with personal values) compared to participants in the active control (cognitive training) group and the waiting list control group. The secondary hypotheses are that participants receiving PIPCI will improve psychological health (stress measures, quality of life, depression, and general health) compared to participants in the active control group and the waiting list control group.Materials and MethodsThis three-arm RCT will recruit participants from the cognitive centers at Karolinska University Hospital in Stockholm and randomize approximately 120 individuals in the early phase of cognitive impairment to either an experimental group (psychological intervention once a week for 10 weeks), an active control group (cognitive training once a week for 10 weeks) or a waiting list control group. Intervention outcome will be evaluated with self-report questionnaires on physical and psychological aspects of health, cognitive assessment, biological markers (obtained from blood and saliva) and health care costs. Assessments will be performed at pre- (1 week before the interventions) and post-intervention (1 week after the interventions), as well as at a 6-month follow-up.DiscussionThe development of a potentially feasible and effective psychological intervention tailored for early phase cognitive impairment (PIPCI) has the potential to advance the non-pharmacological intervention field. This is especially important given the extensive burden for many affected individuals and their families and the current lack of effective treatments. If the psychological intervention discussed here shows feasibility and efficacy, there is potential for far-reaching healthcare implications for patients with early cognitive impairment at risk of developing dementia.Clinical Trial RegistrationClinicalTrials.gov: NCT04356924. Date of registration: April 22, 2020. URL: https://clinicaltrials.gov/ct2/show/NCT04356924.

Individuals with congenital sensory deprivation usually demonstrate altered brain morphology in areas associated with early processing of the absent sense. Here, we aimed to establish whether this also applies to individuals born without a sense of smell (congenital anosmia) by comparing cerebral morphology between 33 individuals with isolated congenital anosmia and matched controls. We detected no morphological alterations in the primary olfactory (piriform) cortex. However, individuals with anosmia demonstrated gray matter volume atrophy in bilateral olfactory sulci, explained by decreased cortical area, curvature, and sulcus depth. They further demonstrated increased gray matter volume and cortical thickness in the medial orbital gyri; regions closely associated with olfactory processing, sensory integration, and value-coding. Our results suggest that a lifelong absence of sensory input does not necessarily lead to morphological alterations in primary sensory cortex and extend previous findings with divergent morphological alterations in bilateral orbitofrontal cortex, indicating influences of different developmental processes.

Objectives: Cholinergic dysfunction plays a prominent role in cognitive impairment in Parkinson's disease (PD). The aim of this study was to assess the relationship of baseline and longitudinal basal forebrain atrophy with cognitive decline and dementia in PD. Methods: We included 106 non-demented PD patients, 19 PD dementia (PDD) patients and 42 controls with longitudinal structural MRI and cognitive testing. After 4.2 ± 1.8 years, 20 non-demented PD patients were diagnosed with dementia (PD-dementia converters), whereas the rest of PD patients remained non-demented (stable-PD). We compared MRI volumes of the medial septum/diagonal band (Ch1/Ch2) and nucleus basalis of Meynert (Ch4) between groups. Cox regression analyses were applied to test whether Ch1/Ch2 or Ch4 atrophy could predict future dementia and linear mixed models assessed their association with cognitive decline. Results: Compared to controls, we found reduced Ch4 baseline volumes in PD-dementia converters (p = .003) and those who already had PDD (p

The integrity of the cholinergic system plays a central role in cognitive decline both in normal aging and neurological disorders including Alzheimer’s disease and vascular cognitive impairment. Most of the previous neuroimaging research has focused on the integrity of the cholinergic basal forebrain, or its sub-region the nucleus basalis of Meynert (NBM). Tractography using diffusion tensor imaging data may enable modelling of the NBM white matter projections. We investigated the contribution of NBM volume, NBM white matter projections, small vessel disease (SVD), and age to performance in attention and memory in 262 cognitively normal individuals (39–77 years of age, 53% female). We developed a multimodal MRI pipeline for NBM segmentation and diffusion-based tracking of NBM white matter projections, and computed white matter hypointensities (WM-hypo) as a marker of SVD. We successfully tracked pathways that closely resemble the spatial layout of the cholinergic system as seen in previous post-mortem and DTI tractography studies. We found that high WM-hypo load was associated with older age, male sex, and lower performance in attention and memory. A high WM-hypo load was also associated with lower integrity of the cholinergic system above and beyond the effect of age. In a multivariate model, age and integrity of NBM white matter projections were stronger contributors than WM-hypo load and NBM volume to performance in attention and memory. We conclude that the integrity of NBM white matter projections plays a fundamental role in cognitive aging. This and other modern neuroimaging methods offer new opportunities to re-evaluate the cholinergic hypothesis of cognitive aging.

Medial temporal lobe (MTL) atrophy is an important morphological marker of many dementias and is closely related to cognitive decline. In this study we aimed to characterize longitudinal progression of MTL atrophy in 93 individuals with subjective cognitive decline and mild cognitive impairment followed up over six years, and to assess if clinical rating scales are able to detect these changes. All MRI images were visually rated according to Scheltens’ scale of medial temporal atrophy (MTA) by two neuroradiologists and AVRA, a software for automated MTA ratings. The images were also segmented using FreeSurfer’s longitudinal pipeline in order to compare the MTA ratings to volumes of the hippocampi and inferior lateral ventricles. We found that MTL atrophy rates increased with CSF biomarker abnormality, used to define preclinical stages of Alzheimer’s Disease. Both AVRA’s and the radiologists’ MTA ratings showed similar longitudinal trends as the subcortical volumes, suggesting that visual rating scales provide a valid alternative to automatic segmentations. Our results further showed that it took more than 8 years on average for individuals with mild cognitive impairment, and an Alzheimer’s disease biomarker profile, to increase the MTA score by one. This suggests that discrete MTA ratings are too coarse for tracking individual MTL atrophy in short time spans. While the MTA scores from each radiologist showed strong correlations to subcortical volumes, the inter-rater agreement was low. We conclude that the main limitation of quantifying MTL atrophy with visual ratings in clinics is the subjectiveness of the assessment.

Background: Alzheimer’s disease (AD)-related pathology is frequently found in patients with dementia with Lewy bodies (DLB). However, it is unknown how amyloid-β and tau-related pathologies influence neurodegeneration in DLB. Understanding the mechanisms underlying brain atrophy in DLB can improve our knowledge about disease progression, differential diagnosis, drug development and testing of anti-amyloid and anti-tau therapies in DLB. Objectives: We aimed at investigating the combined effect of CSF amyloid-β42, phosphorylated tau and total tau on regional brain atrophy in DLB in the European DLB (E-DLB) cohort. Methods: 86 probable DLB patients from the E-DLB cohort with CSF and MRI data were included. Random forest was used to analyze the association of CSF biomarkers (predictors) with visual rating scales for medial temporal lobe atrophy (MTA), posterior atrophy (PA) and global cortical atrophy scale-frontal subscale (GCA-F) (outcomes), including age, sex, education and disease duration as extra predictors. Results: DLB patients with abnormal MTA scores had abnormal CSF Aβ42, shorter disease duration and older age. DLB patients with abnormal PA scores had abnormal levels of CSF Aβ42 and p-tau, older age, lower education and shorter disease duration. Abnormal GCA-F scores were associated with lower education, male sex, and older age, but not with any AD-related CSF biomarker. Conclusions: This study shows preliminary data on the potential combined effect of amyloid-β and tau-related pathologies on the integrity of posterior brain cortices in DLB patients, whereas only amyloid-β seems to be related to MTA. Future availability of α-synuclein biomarkers will help us to understand the effect of α-synuclein and AD-related pathologies on brain integrity in DLB.

Performing an accurate segmentation of the hippocampus from brain magnetic resonance images is a crucial task in neuroimaging research, since its structural integrity is strongly related to several neurodegenerative disorders, including Alzheimer’s disease (AD). Some automatic segmentation tools are already being used, but, in recent years, new deep learning (DL)-based methods have been proven to be much more accurate in various medical image segmentation tasks. In this work, we propose a DL-based hippocampus segmentation framework that embeds statistical shape of the hippocampus as context information into the deep neural network (DNN). The inclusion of shape information is achieved with three main steps: (1) a U-Net-based segmentation, (2) a shape model estimation, and (3) a second U-Net-based segmentation which uses both the original input data and the fitted shape model. The trained DL architectures were tested on image data of three diagnostic groups [AD patients, subjects with mild cognitive impairment (MCI) and controls] from two cohorts (ADNI and AddNeuroMed). Both intra-cohort validation and cross-cohort validation were performed and compared with the conventional U-net architecture and some variations with other types of context information (i.e., autocontext and tissue-class context). Our results suggest that adding shape information can improve the segmentation accuracy in cross-cohort validation, i.e., when DNNs are trained on one cohort and applied to another. However, no significant benefit is observed in intra-cohort validation, i.e., training and testing DNNs on images from the same cohort. Moreover, compared to other types of context information, the use of shape context was shown to be the most successful in increasing the accuracy, while keeping the computational time in the order of a few minutes.