DSnet: a new dual-branch network for hippocampus subfield segmentation.

The hippocampus is a critical component of the brain and is associated with many neurological disorders. It can be further subdivided into several subfields, and accurate segmentation of these subfields is of great significance for diagnosis and research. However, the structures of hippocampal subfields are irregular and have complex boundaries, and their voxel values are close to surrounding brain tissues, making the segmentation task highly challenging. Currently, many automatic segmentation tools exist for hippocampal subfield segmentation, but they suffer from high time costs and low segmentation accuracy. In this paper, we propose a new dual-branch segmentation network structure (DSnet) based on deep learning for hippocampal subfield segmentation. While traditional convolutional neural network-based methods are effective in capturing hierarchical structures, they struggle to establish long-term dependencies. The DSnet integrates the Transformer architecture and a hybrid attention mechanism, enhancing the network's global perceptual capabilities. Moreover, the dual-branch structure of DSnet leverages the segmentation results of the hippocampal region to facilitate the segmentation of its subfields. We validate the efficacy of our algorithm on the public Kulaga-Yoskovitz dataset. Experimental results indicate that our method is more effective in segmenting hippocampal subfields than conventional single-branch network structures. Compared to the classic 3D U-Net, our proposed DSnet improves the average Dice accuracy of hippocampal subfield segmentation by 0.57%.

An improved 3D-UNet-based brain hippocampus segmentation model based on MR images.

OBJECTIVE: Accurate delineation of the hippocampal region via magnetic resonance imaging (MRI) is crucial for the prevention and early diagnosis of neurosystemic diseases. Determining how to accurately and quickly delineate the hippocampus from MRI results has become a serious issue. In this study, a pixel-level semantic segmentation method using 3D-UNet is proposed to realize the automatic segmentation of the brain hippocampus from MRI results. METHODS: Two hundred three-dimensional T1-weighted (3D-T1) nongadolinium contrast-enhanced magnetic resonance (MR) images were acquired at Hangzhou Cancer Hospital from June 2020 to December 2022. These samples were divided into two groups, containing 175 and 25 samples. In the first group, 145 cases were used to train the hippocampus segmentation model, and the remaining 30 cases were used to fine-tune the hyperparameters of the model. Images for twenty-five patients in the second group were used as the test set to evaluate the performance of the model. The training set of images was processed via rotation, scaling, grey value augmentation and transformation with a smooth dense deformation field for both image data and ground truth labels. A filling technique was introduced into the segmentation network to establish the hippocampus segmentation model. In addition, the performance of models established with the original network, such as VNet, SegResNet, UNetR and 3D-UNet, was compared with that of models constructed by combining the filling technique with the original segmentation network. RESULTS: The results showed that the performance of the segmentation model improved after the filling technique was introduced. Specifically, when the filling technique was introduced into VNet, SegResNet, 3D-UNet and UNetR, the segmentation performance of the models trained with an input image size of 48 × 48 × 48 improved. Among them, the 3D-UNet-based model with the filling technique achieved the best performance, with a Dice score (Dice score) of 0.7989 ± 0.0398 and a mean intersection over union (mIoU) of 0.6669 ± 0.0540, which were greater than those of the original 3D-UNet-based model. In addition, the oversegmentation ratio (OSR), average surface distance (ASD) and Hausdorff distance (HD) were 0.0666 ± 0.0351, 0.5733 ± 0.1018 and 5.1235 ± 1.4397, respectively, which were better than those of the other models. In addition, when the size of the input image was set to 48 × 48 × 48, 64 × 64 × 64 and 96 × 96 × 96, the model performance gradually improved, and the Dice scores of the proposed model reached 0.7989 ± 0.0398, 0.8371 ± 0.0254 and 0.8674 ± 0.0257, respectively. In addition, the mIoUs reached 0.6669 ± 0.0540, 0.7207 ± 0.0370 and 0.7668 ± 0.0392, respectively. CONCLUSION: The proposed hippocampus segmentation model constructed by introducing the filling technique into a segmentation network performed better than models built solely on the original network and can improve the efficiency of diagnostic analysis.

Blood-brain barrier integrity is linked to cognitive function, but not to cerebral arterial pulsatility, among elderly.

Blood-brain barrier (BBB) disruption may contribute to cognitive decline, but questions remain whether this association is more pronounced for certain brain regions, such as the hippocampus, or represents a whole-brain mechanism. Further, whether human BBB leakage is triggered by excessive vascular pulsatility, as suggested by animal studies, remains unknown. In a prospective cohort (N = 50; 68-84 years), we used contrast-enhanced MRI to estimate the permeability-surface area product (PS) and fractional plasma volume ( v p ), and 4D flow MRI to assess cerebral arterial pulsatility. Cognition was assessed by the Montreal Cognitive Assessment (MoCA) score. We hypothesized that high PS would be associated with high arterial pulsatility, and that links to cognition would be specific to hippocampal PS. For 15 brain regions, PS ranged from 0.38 to 0.85 (·10-3 min-1) and v p from 0.79 to 1.78%. Cognition was related to PS (·10-3 min-1) in hippocampus (ß = - 2.9; p = 0.006), basal ganglia (ß = - 2.3; p = 0.04), white matter (ß = - 2.6; p = 0.04), whole-brain (ß = - 2.7; p = 0.04) and borderline-related for cortex (ß = - 2.7; p = 0.076). Pulsatility was unrelated to PS for all regions (p > 0.19). Our findings suggest PS-cognition links mainly reflect a whole-brain phenomenon with only slightly more pronounced links for the hippocampus, and provide no evidence of excessive pulsatility as a trigger of BBB disruption.

Exploring the cognitive assessment potential of MRI-based volumetric hippocampal segmentation in Parkinson's disease.

PURPOSE: To investigate the potential of magnetic resonance imaging (MRI)-based total and segmental hippocampus volume analysis in the assessment of cognitive status in Parkinson's disease (PD). METHODS: We divided participants into three groups Group A-Parkinson patients (Pp) with normal cognitive status (n = 25), Group B-Pp with dementia (n = 17), and Group C-healthy controls (n = 37). Three-dimensional T1W Fast Spoiled Gradient Recalled Echo images were used for Volbrain hippocampus subfield segmentation. We used the "Winterburn" protocol, which divides the hippocampus into five segments, Cornu Ammonis (CA),CA2/CA3, CA4/dentate gyrus, stratum radiatum, lacunosum, and moleculare, and subiculum. RESULTS: A total of 79 participants were included in the study, consisting of 42 individuals with PD (64.2% male) and 37 healthy controls (54.1% male). The mean age of PD was 60.9 ± 10.7 years and the mean age of control group was 59.27 ± 12.3 years. Significant differences were found in total hippocampal volumes between Group A and B (p = .047. Statistically significant group differences were found in total, right, and left CA1 volumes (analysis of variance [ANOVA]: F(2,76) = 8.098, p = .001; F(2,76) = 7.628, p = .001; F(2,76) = 5.084, p = .008, respectively), as well as in total subiculum volumes (ANOVA: F(2,76) = 4.368, p = .016). Post hoc tests showed that total subiculum volume was significantly lower in individuals with normal cognitive status (0.474 ± 0.116 cm3) compared to healthy controls (0.578 ± 0.151 cm3, p = .013). CONCLUSION: Volumetric hippocampal MRI can be used to assess the cognitive status of Pp. Longitudinal studies that evaluate Pp who progress from normal cognition to dementia are required to establish a causal relationship.

Retinal peri-arteriolar versus peri-venular amyloidosis, hippocampal atrophy, and cognitive impairment: exploratory trial.

The relationship between amyloidosis and vasculature in cognitive impairment and Alzheimer's disease (AD) pathogenesis is increasingly acknowledged. We conducted a quantitative and topographic assessment of retinal perivascular amyloid plaque (AP) distribution in individuals with both normal and impaired cognition. Using a retrospective dataset of scanning laser ophthalmoscopy fluorescence images from twenty-eight subjects with varying cognitive states, we developed a novel image processing method to examine retinal peri-arteriolar and peri-venular curcumin-positive AP burden. We further correlated retinal perivascular amyloidosis with neuroimaging measures and neurocognitive scores. Our study unveiled that peri-arteriolar AP counts surpassed peri-venular counts throughout the entire cohort (P < 0.0001), irrespective of the primary, secondary, or tertiary vascular branch location, with a notable increase among cognitively impaired individuals. Moreover, secondary branch peri-venular AP count was elevated in the cognitively impaired (P < 0.01). Significantly, peri-venular AP count, particularly in secondary and tertiary venules, exhibited a strong correlation with clinical dementia rating, Montreal cognitive assessment score, hippocampal volume, and white matter hyperintensity count. In conclusion, our exploratory analysis detected greater peri-arteriolar versus peri-venular amyloidosis and a marked elevation of amyloid deposition in secondary branch peri-venular regions among cognitively impaired subjects. These findings underscore the potential feasibility of retinal perivascular amyloid imaging in predicting cognitive decline and AD progression. Larger longitudinal studies encompassing diverse populations and AD-biomarker confirmation are warranted to delineate the temporal-spatial dynamics of retinal perivascular amyloid deposition in cognitive impairment and the AD continuum.

Amnestic Syndrome in Memory Clinics: Similar Morphological Brain Patterns in Older Adults with and without Alzheimer's Disease.

Background: Amnestic syndrome of the hippocampal type (ASHT) in Memory Clinics is a presentation common to Alzheimer's disease (AD). However, ASHT can be found in other neurodegenerative disorders. Objective: To compare brain morphometry including hippocampal volumes between amnestic older adults with and without AD pathology and investigate their relationship with memory performance and cerebrospinal fluid (CSF) biomarkers. Methods: Brain morphometry of 92 consecutive patients (72.5±6.8 years old; 39% female) with Free and Cued Selective Recall Reminding Test (FCSRT) total recall < 40/48 was assessed with an automated algorithm and compared between AD and non-AD patients, as defined by CSF biomarkers. Results: AD and non-AD patients presented comparable brain morphology. Total recall was associated to hippocampal volume irrespectively from AD pathology. Conclusions: Brain morphometry, including hippocampal volumes, is similar between AD and non-AD older adults with ASHT evaluated in a Memory Clinic, underlying the importance of using molecular biomarkers for the diagnosis of AD.

Aspects of Area Deprivation Index in Relation to Hippocampal Volume Among Children.

Importance: Area deprivation index (ADI) has been shown to be associated with reduced hippocampal volume (HV) among youths. The social environment may interact with the association between ADI and HV. Objective: To investigate which aspects of ADI are uniquely associated with bilateral HV and whether school and family environments have moderating interactions in associations between ADI and HV. Design, Setting, and Participants: This cross-sectional study used data from the Adolescent Brain and Cognitive Development (ABCD) study. Participants aged 9 and 10 years were recruited from 21 sites in the US between September 2016 and August 2018. Data analysis was performed between March 2023 and April 2024. Exposures: ADI aspects were derived from participant primary home addresses provided by parents or guardians. Main Outcomes and Measures: HV was automatically segmented from structural brain images ascertained from magnetic resonance imaging. Multiple generalized linear mixed modeling tested associations between 9 indices of ADI and bilateral HV, with family groups and recruitment sites as random effects. After stepwise backward selection, models were adjusted for individual-level covariates, including age, sex, race and ethnicity, parental education, household income, and estimated intracranial volume. Results: This study included 10 114 participants aged 9 and 10 years (median [IQR] age, 9.92 [9.33-10.48] years; 5294 male [52.3%]; 200 Asian [2.0%], 1411 Black [14.0%], and 6655 White [65.8%]; 1959 Hispanic [19.4%]). After stepwise backward selection and adjusting for covariates, only the percentage of neighborhood-level single-parent households was associated with right HV (adjusted ß per 1-SD increase in single-parent households, -0.03; 95% CI, -0.06 to -0.01; P = .01). School environment interacted with neighborhood-level single-parent households in its association with right HV (adjusted ß per 1-SD increase in score, 0.02; 95% CI, 0.01 to 0.03; P = .003), such that there was an inverse association only among those at a school with the mean environment score (adjusted ß per 1% increase in single-parent households, -0.03; 95% CI, -0.05 to -0.01; P = .02) and worse (-1 SD score) school environment score (adjusted ß per 1% increase in single-parent households, -0.05; 95% CI, -0.09 to -0.01; P < .001) but not among those at better (+1 SD score) school environments. Conclusions and Relevance: In this study, an increased percentage of neighborhood-level single-parent households was associated with reduced right HV among children in schools with the mean or worse but not better environment score. These findings suggest that longitudinal research concerning the association of neighborhood-level characteristics and school environments with hippocampal development may be warranted to better understand complex interactions between various social factors and child neurodevelopment and mental health outcomes.

Association of Basal Forebrain Atrophy With Cognitive Decline in Early Alzheimer Disease.

BACKGROUND AND OBJECTIVES: In early Alzheimer disease (AD), ß-amyloid (Aß) deposition is associated with volume loss in the basal forebrain (BF) and cognitive decline. However, the extent to which Aß-related BF atrophy manifests as cognitive decline is not understood. This study sought to characterize the relationship between BF atrophy and the decline in memory and attention in patients with early AD. METHODS: Participants from the Australian Imaging, Biomarkers and Lifestyle (AIBL) study who completed Aß-PET imaging and repeated MRI and cognitive assessments were included. At baseline, participants were classified based on their clinical dementia stage and Aß status, yielding groups that were cognitively unimpaired (CU) Aß-, CU Aß+, and mild cognitive impairment (MCI) Aß+. Linear mixed-effects models were used to assess changes in volumetric measures of BF subregions and the hippocampus and changes in AIBL memory and attention composite scores for each group compared with CU Aß- participants. Associations between Aß burden, brain atrophy, and cognitive decline were evaluated and explored further using mediation analyses. RESULTS: The cohort included 476 participants (72.6 ± 5.9 years, 55.0% female) with longitudinal data from a median follow-up period of 6.1 years. Compared with the CU Aß- group (n = 308), both CU Aß+ (n = 107) and MCI Aß+ (n = 61) adults showed faster decline in BF and hippocampal volumes and in memory and attention (Cohen d = 0.73-1.74). Rates of atrophy in BF subregions and the hippocampus correlated with cognitive decline, and each individually mediated the impact of Aß burden on memory and attention decline. When all mediators were considered simultaneously, hippocampal atrophy primarily influenced the effect of Aß burden on memory decline (ß [SE] = -0.139 [0.032], proportion mediated [PM] = 28.0%) while the atrophy of the posterior nucleus basalis of Meynert in the BF (ß [SE] = -0.068 [0.029], PM = 13.1%) and hippocampus (ß [SE] = -0.121 [0.033], PM = 23.4%) distinctively influenced Aß-related attention decline. DISCUSSION: These findings highlight the significant role of BF atrophy in the complex pathway linking Aß to cognitive impairment in early stages of AD. Volumetric assessment of BF subregions could be essential in elucidating the relationships between the brain structure and behavior in AD.

Clinical Significance of the Plasma Biomarker Panels in Amyloid-Negative and Tau PET-Positive Amnestic Patients: Comparisons with Alzheimer's Disease and Unimpaired Cognitive Controls.

The purpose of this study was to investigate whether plasma biomarkers can help to diagnose, differentiate from Alzheimer disease (AD), and stage cognitive performance in patients with positron emission tomography (PET)-confirmed primary age-related tauopathy, termed tau-first cognitive proteinopathy (TCP) in this study. In this multi-center study, we enrolled 285 subjects with young-onset AD (YOAD; n = 55), late-onset AD (LOAD; n = 96), TCP (n = 44), and cognitively unimpaired controls (CTL; n = 90) and analyzed plasma Aß42/Aß40, pTau181, neurofilament light (NFL), and total-tau using single-molecule assays. Amyloid and tau centiloids reflected pathological burden, and hippocampal volume reflected structural integrity. Receiver operating characteristic curves and areas under the curves (AUCs) were used to determine the diagnostic accuracy of plasma biomarkers compared to hippocampal volume and amyloid and tau centiloids. The Mini-Mental State Examination score (MMSE) served as the major cognitive outcome. Logistic stepwise regression was used to assess the overall diagnostic accuracy, combining fluid and structural biomarkers and a stepwise linear regression model for the significant variables for MMSE. For TCP, tau centiloid reached the highest AUC for diagnosis (0.79), while pTau181 could differentiate TCP from YOAD (accuracy 0.775) and LOAD (accuracy 0.806). NFL reflected the clinical dementia rating in TCP, while pTau181 (rho = 0.3487, p = 0.03) and Aß42/Aß40 (rho = -0.36, p = 0.02) were significantly correlated with tau centiloid. Hippocampal volume (unstandardized ß = 4.99, p = 0.01) outperformed all of the fluid biomarkers in predicting MMSE scores in the TCP group. Our results support the superiority of tau PET to diagnose TCP, pTau181 to differentiate TCP from YOAD or LOAD, and NFL for functional staging.

Matrix Remodeling Enzymes as Potential Fluid Biomarkers of Neurodegeneration in Alzheimer's Disease.

This study investigated the diagnostic accuracy of plasma biomarkers-specifically, matrix metalloproteinase (MMP-9), tissue inhibitor of metalloproteinase (TIMP-1), CD147, and the MMP-/TIMP-1 ratio in patients with Alzheimer's disease (AD) dementia. The research cohort comprised patients diagnosed with probable AD dementia and a control group of cognitively unimpaired (CU) individuals. Neuroradiological assessments included brain magnetic resonance imaging (MRI) following dementia protocols, with subsequent volumetric analysis. Additionally, cerebrospinal fluid (CSF) AD biomarkers were classified using the A/T/N system, and apolipoprotein E (APOE) ε4 carrier status was determined. Findings revealed elevated plasma levels of MMP-9 and TIMP-1 in AD dementia patients compared to CU individuals. Receiver operating characteristic (ROC) curve analysis demonstrated significant differences in the areas under the curve (AUC) for MMP-9 (p < 0.001) and TIMP-1 (p < 0.001). Notably, plasma TIMP-1 levels were significantly lower in APOE ε4+ patients than in APOE ε4- patients (p = 0.041). Furthermore, APOE ε4+ patients exhibited reduced hippocampal volume, particularly in total, right, and left hippocampal measurements. TIMP-1 levels exhibited a positive correlation, while the MMP-9/TIMP-1 ratio showed a negative correlation with hippocampal volume parameters. This study sheds light on the potential use of TIMP-1 as a diagnostic marker and its association with hippocampal changes in AD.

Estimating individual trajectories of structural and cognitive decline in mild cognitive impairment for early prediction of progression to dementia of the Alzheimer's type.

Only a third of individuals with mild cognitive impairment (MCI) progress to dementia of the Alzheimer's type (DAT). Identifying biomarkers that distinguish individuals with MCI who will progress to DAT (MCI-Converters) from those who will not (MCI-Non-Converters) remains a key challenge in the field. In our study, we evaluate whether the individual rates of loss of volumes of the Hippocampus and entorhinal cortex (EC) with age in the MCI stage can predict progression to DAT. Using data from 758 MCI patients in the Alzheimer's Disease Neuroimaging Database, we employ Linear Mixed Effects (LME) models to estimate individual trajectories of regional brain volume loss over 12 years on average. Our approach involves three key analyses: (1) mapping age-related volume loss trajectories in MCI-Converters and Non-Converters, (2) using logistic regression to predict progression to DAT based on individual rates of hippocampal and EC volume loss, and (3) examining the relationship between individual estimates of these volumetric changes and cognitive decline across different cognitive functions-episodic memory, visuospatial processing, and executive function. We find that the loss of Hippocampal volume is significantly more rapid in MCI-Converters than Non-Converters, but find no such difference in EC volumes. We also find that the rate of hippocampal volume loss in the MCI stage is a significant predictor of conversion to DAT, while the rate of volume loss in the EC and other additional regions is not. Finally, individual estimates of rates of regional volume loss in both the Hippocampus and EC, and other additional regions, correlate strongly with individual rates of cognitive decline. Across all analyses, we find significant individual variation in the initial volumes and the rates of changes in volume with age in individuals with MCI. This study highlights the importance of personalized approaches in predicting AD progression, offering insights for future research and intervention strategies.

The hippocampus dissociates present from past and future goals.

Our brain adeptly navigates goals across time frames, distinguishing between urgent needs and those of the past or future. The hippocampus is a region known for supporting mental time travel and organizing information along its longitudinal axis, transitioning from detailed posterior representations to generalized anterior ones. This study investigates the role of the hippocampus in distinguishing goals over time: whether the hippocampus encodes time regardless of detail or abstraction, and whether the hippocampus preferentially activates its anterior region for temporally distant goals (past and future) and its posterior region for immediate goals. We use a space-themed experiment with 7T functional MRI on 31 participants to examine how the hippocampus encodes the temporal distance of goals. During a simulated Mars mission, we find that the hippocampus tracks goals solely by temporal proximity. We show that past and future goals activate the left anterior hippocampus, while current goals engage the left posterior hippocampus. This suggests that the hippocampus maps goals using timestamps, extending its long axis system to include temporal goal organization.

Unveiling critical ADHD biomarkers in limbic system and cerebellum using a binary hypothesis testing approach.

Attention deficit hyperactivity disorder (ADHD) is a common childhood developmental disorder. In recent years, pattern recognition methods have been increasingly applied to neuroimaging studies of ADHD. However, these methods often suffer from limited accuracy and interpretability, impeding their contribution to the identification of ADHD-related biomarkers. To address these limitations, we applied the amplitude of low-frequency fluctuation (ALFF) results for the limbic system and cerebellar network as input data and conducted a binary hypothesis testing framework for ADHD biomarker detection. Our study on the ADHD-200 dataset at multiple sites resulted in an average classification accuracy of 93%, indicating strong discriminative power of the input brain regions between the ADHD and control groups. Moreover, our approach identified critical brain regions, including the thalamus, hippocampal gyrus, and cerebellum Crus 2, as biomarkers. Overall, this investigation uncovered potential ADHD biomarkers in the limbic system and cerebellar network through the use of ALFF realizing highly credible results, which can provide new insights for ADHD diagnosis and treatment.

Diffusion MRI of the Hippocampus.

The hippocampus is a brain structure that plays key roles in a variety of cognitive processes. Critically, a wide range of neurological disorders are associated with degeneration of the hippocampal microstructure, defined as neurons, dendrites, glial cells, and more. Thus, the hippocampus is a key target for methods that are sensitive to these microscale properties. Diffusion MRI is one such method, which can noninvasively probe neural architecture. Here we review the extensive use of diffusion MRI to capture hippocampal microstructure in both health and disease. The results of these studies indicate that (1) diffusion tensor imaging is sensitive but not specific to the hippocampal microstructure; (2) biophysical modeling of diffusion MRI signals is a promising avenue to capture more specific aspects of the hippocampal microstructure; (3) use of ultra-short diffusion times have shown unique laminar-specific microstructure and response to hippocampal injury; (4) dispersion of microstructure is likely abundant in the hippocampus; and (5) the angular richness of the diffusion MRI signal can be leveraged to improve delineation of the internal hippocampal circuitry. Overall, extant findings suggest that diffusion MRI offers a promising avenue for characterizing hippocampal microstructure.

Selective activations and functional connectivities to the sight of faces, scenes, body parts and tools in visual and non-visual cortical regions leading to the human hippocampus.

Connectivity maps are now available for the 360 cortical regions in the Human Connectome Project Multimodal Parcellation atlas. Here we add function to these maps by measuring selective fMRI activations and functional connectivity increases to stationary visual stimuli of faces, scenes, body parts and tools from 956 HCP participants. Faces activate regions in the ventrolateral visual cortical stream (FFC), in the superior temporal sulcus (STS) visual stream for face and head motion; and inferior parietal visual (PGi) and somatosensory (PF) regions. Scenes activate ventromedial visual stream VMV and PHA regions in the parahippocampal scene area; medial (7m) and lateral parietal (PGp) regions; and the reward-related medial orbitofrontal cortex. Body parts activate the inferior temporal cortex object regions (TE1p, TE2p); but also visual motion regions (MT, MST, FST); and the inferior parietal visual (PGi, PGs) and somatosensory (PF) regions; and the unpleasant-related lateral orbitofrontal cortex. Tools activate an intermediate ventral stream area (VMV3, VVC, PHA3); visual motion regions (FST); somatosensory (1, 2); and auditory (A4, A5) cortical regions. The findings add function to cortical connectivity maps; and show how stationary visual stimuli activate other cortical regions related to their associations, including visual motion, somatosensory, auditory, semantic, and orbitofrontal cortex value-related, regions.

The hippocampus supports the representation of abstract concepts: Implications for the study of recognition memory.

Words, unlike images, are symbolic representations. The associative details inherent within a word's meaning and the visual imagery it generates, are inextricably connected to the way words are processed and represented. It is well recognised that the hippocampus associatively binds components of a memory to form a lasting representation, and here we show that the hippocampus is especially sensitive to abstract word processing. Using fMRI during recognition, we found that the increased abstractness of words produced increased hippocampal activation regardless of memory outcome. Interestingly, word recollection produced hippocampal activation regardless of word content, while the parahippocampal cortex was sensitive to concreteness of word representations, regardless of memory outcome. We reason that the hippocampus has assumed a critical role in the representation of uncontextualized abstract word meaning, as its information-binding ability allows the retrieval of the semantic and visual associates that, when bound together, generate the abstract concept represented by word symbols. These insights have implications for research on word representation, memory, and hippocampal function, perhaps shedding light on how the human brain has adapted to encode and represent abstract concepts.

Longitudinal alcohol-related brain changes in older adults: The Sydney Memory and Ageing Study.

Increases in harmful drinking among older adults indicate the need for a more thorough understanding of the relationship between later-life alcohol use and brain health. The current study investigated the relationships between alcohol use and progressive grey and white matter changes in older adults using longitudinal data. A total of 530 participants (aged 70 to 90 years; 46.0% male) were included. Brain outcomes assessed over 6 years included total grey and white matter volume, as well as volume of the hippocampus, thalamus, amygdala, corpus callosum, orbitofrontal cortex and insula. White matter integrity was also investigated. Average alcohol use across the study period was the main exposure of interest. Past-year binge drinking and reduction in drinking from pre-baseline were additional exposures of interest. Within the context of low-level average drinking (averaging 11.7 g per day), higher average amount of alcohol consumed was associated with less atrophy in the left (B = 7.50, pFDR = 0.010) and right (B = 5.98, pFDR = 0.004) thalamus. Past-year binge-drinking was associated with poorer white matter integrity (B = -0.013, pFDR = 0.024). Consuming alcohol more heavily in the past was associated with greater atrophy in anterior (B = -12.73, pFDR = 0.048) and posterior (B = -17.88, pFDR = 0.004) callosal volumes over time. Across alcohol exposures and neuroimaging markers, no other relationships were statistically significant. Within the context of low-level drinking, very few relationships between alcohol use and brain macrostructure were identified. Meanwhile, heavier drinking was negatively associated with white matter integrity.

[Features of the clinical and neuroimaging picture in patients with early-onset Alzheimer's disease]. / Osobennosti klinicheskoi i neirovizualizatsionnoi kartiny u patsientov s bolezn'yu Al'tsgeimera s rannim nachalom.

The most common cause of severe cognitive impairment in adults is Alzheimer's disease (AD). Depending on the age of onset, AD is divided into early (<65 years) and late (≥65 years) forms. Early-onset AD (EOAD) is significantly less common than later-onset AD (LOAD) and accounts for only about 5-10% of cases. However, its medical and social significance, as a disease leading to loss of ability to work and legal capacity, as well as premature death in patients aged 40-64 years, is extremely high. Patients with EOAD compared with LOAD have a greater number of atypical clinical variants - 25% and 6-12.5%, respectively, which complicates the differential diagnosis of EOAD with other neurodegenerative diseases. However, the typical classical amnestic variant predominates in both EOAD and LOAD. Also, patients with EOAD have peculiarities according to neuroimaging data: when performing MRI of the brain, patients with EOAD often have more pronounced parietal atrophy and less pronounced hippocampal atrophy compared to patients with LOAD. The article pays attention to the features of the clinical and neuroimaging data in patients with EOAD; a case of a patient with EOAD is presented.

Examining iron-related off-target binding effects of 18F-AV1451 PET in the cortex of Aß+ individuals.

The presence of neurofibrillary tangles containing hyper-phosphorylated tau is a characteristic of Alzheimer's disease (AD) pathology. The positron emission tomography (PET) radioligand sensitive to tau neurofibrillary tangles (18F-AV1451) also binds with iron. This off-target binding effect may be enhanced in older adults on the AD spectrum, particularly those with amyloid-positive biomarkers. Here, we examined group differences in 18F-AV1451 PET after controlling for iron-sensitive measures from magnetic resonance imaging (MRI) and its relationships to tissue microstructure and cognition in 40 amyloid beta positive (Aß+) individuals, 20 amyloid beta negative (Aß-) with MCI and 31 Aß- control participants. After controlling for iron, increased 18F-AV1451 PET uptake was found in the temporal lobe and hippocampus of Aß+ participants compared to Aß- MCI and control participants. Within the Aß+ group, significant correlations were seen between 18F-AV1451 PET uptake and tissue microstructure and these correlations remained significant after controlling for iron. These findings indicate that off-target binding of iron to the 18F-AV1451 ligand may not affect its sensitivity to Aß status or cognition in early-stage AD.

The Mediterranean diet is not associated with neuroimaging or cognition in middle-aged adults: a cross-sectional analysis of the PREVENT dementia programme.

BACKGROUND AND PURPOSE: The Mediterranean diet (MedDiet) has been associated with reduced dementia incidence in several studies. It is important to understand if diet is associated with brain health in midlife, when Alzheimer's disease and related dementias are known to begin. METHODS: This study used data from the PREVENT dementia programme. Three MedDiet scores were created (the Pyramid, Mediterranean Diet Adherence Screener [MEDAS] and MEDAS continuous) from a self-reported food frequency questionnaire. Primary outcomes were hippocampal volume and cube-transformed white matter hyperintensity volume. Secondary outcomes included cornu ammonis 1 and subiculum hippocampal subfield volumes, cortical thickness and measures of cognition. Sex-stratified analyses were run to explore differential associations between diet and brain health by sex. An exploratory path analysis was conducted to study if any associations between diet and brain health were mediated by cardiovascular risk factors for dementia. RESULTS: In all, 504 participants were included in this analysis, with a mean Pyramid score of 8.10 (SD 1.56). There were no significant associations between any MedDiet scoring method and any of the primary or secondary outcomes. There were no differences by sex in any analyses and no significant mediation between the Pyramid score and global cognition by cardiovascular risk factors. CONCLUSIONS: Overall, this study did not find evidence for an association between the MedDiet and either neuroimaging or cognition in a midlife population study. Future work should investigate associations between the MedDiet and Alzheimer's disease and related dementias biomarkers as well as functional neuroimaging in a midlife population.