Designing implicit population learners: a permutation-equivariant state space approach for brain disease diagnosis.

A causal bidirectional selective state space model for imaging genetics in neurodegenerative diseases.

Some philosophical aspects of Alzheimer's discovery: an American perspective.- The aging brain and its disorders.- Epidemiology of Alzheimer's disease.- Descriptive and analytic epidemiology of Alzheimer's disease.- A proposed classification of familial Alzheimer's disease based on analysis of 32 multigeneration pedigrees.- Morphology of Alzheimer's disease and related disorders.- Morphology of white matter, subcortical, dementia in Alzheimer's disease.- Morphology of the cerebral cortex in relation to Alzheimer's dementia.- Quantitative investigations of presenile and senile changes of the human entorhinal region.- Neuronal plasticity of the septo-hippocampal pathway in patients suffering from dementia of Alzheimer type.- Morphology of neurofibrillary tangles and senile plaques.- An in vitro model for the study of the neurofibrillary degeneration of the Alzheimer type.- Molecular and cellular changes associated with neurofibrillary tangles and senile plaques.- Brain abnormalities in aged monkeys: a model sharing features with Alzheimer's disease.- Aged dogs: an animal model to study beta-protein amyloidogenesis.- Immunocytochemical and ultrastructural pathology of nerve cells in Alzheimer's disease and related disorders.- Choline-acetyltransferase immunoreactivity in the hippocampal formation of control subjects and patients with Alzheimer's disease.- Calbindin immunoreactive _eurons in Alzheimer-type dementia.- Lactate production and glycolytic enzymes in sporadic and familial Alzheimer's disease.- Impairment of cerebral glucose metabolism parallels learning and memory dysfunctions after intracerebral streptozotocin.- Choline levels, the regulation of acetylcholine and phosphatidylcholine synthesis, and Alzheimer's disease.- Acetylcholine synthesis and membrane phospholipids.- Hippocampal and cardiovascular effects of muscarinic agents.- Cholinergie and monoaminergic neuromediator systems in DAT. Neuropathological and neurochemical findings.- Alterations in catecholamine neurons in the locus coeruleus in dementias of Alzheimer's and Parkinson's disease.- Tyrosine hydroxylase, tryptophan hydroxylase, biopterin and neopterin in the brains and biopterin and neopterin in sera from patients with Alzheimer's disease.- Postreceptorial enhancement of neurotransmission for the treatment of cognitive disorders.- Excitatory dicarboxylic amino acid and pyramidal neurone neurotransmission of the cerebral cortex in Alzheimer's disease.- The sequence within the two polyadenylation sites of the A4 amyloid peptide precursor stimulates the translation.- Alzheimer-like changes of cortical amino acid transmitters in elderly Down's syndrome.- Characteristics of learning deficit induced by ibotenic acid lesion of the frontal cortex in rats.- Memory loss by glutamate antagonists: an animal model of Alzheimer's disease?.- Convulsant properties of methylxanthines, potential cognitive enhancers in dementia syndromes.- Neurodegenerative diseases: CSF amines, lactate and clinical findings.- Somatostatin-like immunoreactivity and neurotransmitter metabolites in the cerebrospinal fluid of patients with senile dementia of Alzheimer type and Parkinson's disease.- Nerve growth factor in serum of patients with dementia (Alzheimer type).- Neuroendocrine dysfunction in early-onset Alzheimer's disease.- Urinary excretion of salsolinol enantiomers and 1,2-dehydrosalsolinol in patients with degenerative dementia.- Alzheimer's disease - one, two or several?.- Diagnostic criteria of Alzheimer's disease.- Clinical diagnosis of Alzheimer's disease: DSM-III-R, ICD-10 - what else?.- Clinical aspects and terminology of dementing syndromes.- Symptoms of depression in the course of multi-infarct dementia and dementia of Alzheimer's type.- Cognitive deterioration and dementia outcome in depression: the search for prognostic factors.- Diagnostic significance of language evaluation in early stages of Alzheimer's disease.- The Alzheimer patient in the family context: how to help the family to cope.- Towards a clinically specific profile of severe senile primary degenerative dementia of the Alzheimer type (PDDAT).- Sequential clinical approach to differential diagnosis of dementia.- Do old patients with Down's syndrome develop premature brain atrophy?.- Results of EEG brain mapping and neuroimaging methods in Senile Dementia of Alzheimer's Typ (SDAT) and Vascular Dementia (VD).- Regulation of EEG delta activity by the cholinergic nucleus basalis.- EEG- and cognitive changes in Alzheimer's disease - a correlative follow-up study.- Decreased hippocampal metabolic rate in patients with SDAT assessed by positron emission tomography during olfactory memory task.- PET criteria for diagnosis of Alzheimer's disease and other dementias.- Oxygen metabolism in the degenerative dementias.- Positron emission tomography for differential diagnosis of dementia: a case of familial dementia.- Comparison between cerebral glucose metabolism and late evoked potentials in patients with Alzheimer's disease.- High resolution regional cerebral blood flow measurements in Alzheimer's disease and other dementia disorders.- Single photon emission computed tomography (SPECT) in Pick's disease: two case reports.- In vivo studies of hippocampal atrophy in Alzheimer's disease.- Outline for the evaluation of nootropic drugs.- Drug treatment of dementia.- Cognitive enhancing properties of antagonist ?-carbolines: new insights into clinical research on the treatment of dementias?.- Long term treatment of SDAT patients with pyritinol.- Listed in Current Contents.

To validate the value of whole-brain computed tomography perfusion (CTP) and CT angiography (CTA) in the diagnosis of mild cognitive impairment (MCI) and Alzheimer's disease (AD). Whole-brain CTP and four-dimensional CT angiography (4D-CTA) images were acquired in 30 MCI, 35 mild AD patients, 35 moderate AD patients, 30 severe AD patients and 50 normal controls (NC). Cerebral blood flow (CBF), cerebral blood volume (CBV), mean transit time (MTT), time to peak (TTP), and correlation between CTP and 4D-CTA were analysed. Elevated CBF in the left frontal and temporal cortex was found in MCI compared with the NC group. However, TTP was increased in the left hippocampus in mild AD patients compared with NC. In moderate and severe AD patients, hypoperfusion was found in multiple brain areas compared with NC. Finally, we found that the extent of arterial stenosis was negatively correlated with CBF in partial cerebral cortex and hippocampus, and positively correlated with TTP in these areas of AD and MCI patients. Our findings suggest that whole-brain CTP and 4D-CTA could serve as a diagnostic modality in distinguishing MCI and AD, and predicting conversion from MCI based on TTP of left hippocampus. • Whole-brain perfusion using the full 160-mm width of 320 detector rows • Provide clinical experience of 320-row CT in cerebrovascular disorders of Alzheimer's disease • Initial combined 4D CTA-CTP data analysed perfusion and correlated with CT angiography • Whole-brain CTP and 4D-CTA have high value for monitoring MCI to AD progression • TTP in the left hippocampus may predict the transition from MCI to AD.

Alzheimer’s Disease (AD) is a progressive neurodegenerative disorder marked by neuronal loss, leading to cognitive and behavioral decline. With the aging global population, AD incidence and its socioeconomic burden are increasing. Developing effective early diagnostic methods is thus critical for improving patient outcomes and slowing disease progression. In this paper, an enhanced Particle Swarm Optimization (PSO) algorithm, which integrates opposition-based Latin squares sampling initialization (OL) with dynamic inertia weights and learning factors (D), termed OLDPSO, is proposed to improve feature selection and classification within a Support Vector Machine (SVM) model for AD diagnosis using magnetic resonance imaging (MRI) data. MRI, as a non-invasive modality, reveals structural brain changes, particularly in gray matter (GM) and white matter (WM) volumes, which are key biomarkers for AD. However, extracting essential features from complex GM and WM data remains a significant challenge. To address this, the proposed OLDPSO, which adaptively balances global exploration and local exploitation, overcomes traditional PSO limitations. Benchmark experiments show that OLDPSO outperforms existing PSO variants in solution quality and convergence speed. Validated with data from the AD Neuroimaging Initiative (ADNI), the OLDPSO-SVM model demonstrates superior performance in differentiating AD, mild cognitive impairment (MCI), and normal control (NC) groups, particularly in classifying MCI subtypes (MCI-NC and MCI-C). Results show that combining GM and WM features yields higher diagnostic accuracy than using either alone, and the model identified key brain regions associated with AD progression. Specifically, the model achieved accuracies of 99.11%, 89.76%, 99.07%, 88.38%, 94.69%, and 87.96% in the diagnosis of AD vs. NC, NC vs. MCI-NC, NC vs. MCI-C, MCI-NC vs. MCI-C, MCI-NC vs. AD, and MCI-C vs. AD, respectively. Through optimized feature selection, the OLDPSO-SVM model enhances diagnostic performance and provides valuable insights for developing MRI-based multimodal diagnostic tools for AD.

To assist doctors to diagnose mild cognitive impairment (MCI) and Alzheimer's disease (AD) early and accurately, convolutional neural networks based on structural magnetic resonance imaging (sMRI) images have been developed and shown excellent performance. However, they are still limited in their capacity in extracting discriminative features because of large sMRI image volumes yet small lesion regions and the small number of sMRI images. We proposed a task-driven hierarchical attention network (THAN) taking advantage of the merits of patch-based and attention-based convolutional neural networks for MCI and AD diagnosis. THAN consists of an information sub-network and a hierarchical attention sub-network. In the information sub-network, an information map extractor, a patch-assistant module, and a mutual-boosting loss function are designed to generate a task-driven information map, which automatically highlights disease-related regions and their importance for final classification. In the hierarchical attention sub-network, a visual attention module and a semantic attention module are devised based on the information map to extract discriminative features for disease diagnosis. Extensive experiments were conducted for four classification tasks: MCI versus (vs.) normal controls (NC), AD vs. NC, AD vs. MCI, and AD vs. MCI vs. NC. Results demonstrated that THAN attained the accuracy of 81.6% for MCI vs. NC, 93.5% for AD vs. NC, 80.8% for AD vs. MCI, and 62.9% for AD vs. MCI vs. NC. It outperformed advanced attention-based and patch-based methods. Moreover, information maps generated by the information sub-network could highlight the potential biomarkers of MCI and AD, such as the hippocampus and ventricles. Furthermore, when the visual and semantic attention modules were combined, the performance of the four tasks was highly improved. The information sub-network can automatically highlight the disease-related regions. The hierarchical attention sub-network can extract discriminative visual and semantic features. Through the two sub-networks, THAN fully exploits the visual and semantic features of disease-related regions and meanwhile considers global features of sMRI images, which finally facilitate the diagnosis of MCI and AD.

INTRODUCTIONBlood protein biomarkers demonstrate potential for Alzheimer's disease (AD) diagnosis. Limited studies examine the molecular changes in AD blood cells.METHODSBulk RNA‐sequencing of blood cells was performed on AD patients of Chinese descent (n = 214 and 26 in the discovery and validation cohorts, respectively) with normal controls (n = 208 and 38 in the discovery and validation cohorts, respectively). Weighted gene co‐expression network analysis (WGCNA) and deconvolution analysis identified AD‐associated gene modules and blood cell types. Regression and unsupervised clustering analysis identified AD‐associated genes, gene modules, cell types, and established AD classification models.RESULTSWGCNA on differentially expressed genes revealed 15 gene modules, with 6 accurately classifying AD (areas under the receiver operating characteristics curve [auROCs] > 0.90). These modules stratified AD patients into subgroups with distinct disease states. Cell‐type deconvolution analysis identified specific blood cell types potentially associated with AD pathogenesis.DISCUSSIONThis study highlights the potential of blood transcriptome for AD diagnosis, patient stratification, and mechanistic studies.HighlightsWe comprehensively analyze the blood transcriptomes of a well‐characterized Alzheimer's disease cohort to identify genes, gene modules, pathways, and specific blood cells associated with the disease.Blood transcriptome analysis accurately classifies and stratifies patients with Alzheimer's disease, with some gene modules achieving classification accuracy comparable to that of the plasma ATN biomarkers.Immune‐associated pathways and immune cells, such as neutrophils, have potential roles in the pathogenesis and progression of Alzheimer's disease.

In the past decade, many studies have been conducted to advance computer-aided systems for Alzheimer’s disease (AD) diagnosis. Most of them have recently developed systems concentrated on extracting and combining features from MRI, PET, and CSF. For the most part, they have obtained very high performance. However, improving the performance of a classification problem is complicated, specifically when the model’s accuracy or other performance measurements are higher than 90%. In this study, a novel methodology is proposed to address this problem, specifically in Alzheimer’s disease diagnosis classification. This methodology is the first of its kind in the literature, based on the notion of replication on the feature space instead of the traditional sample space. Briefly, the main steps of the proposed method include extracting, embedding, and exploring the best subset of features. For feature extraction, we adopt VBM-SPM; for embedding features, a concatenation strategy is used on the features to ultimately create one feature vector for each subject. Principal component analysis is applied to extract new features, forming a low-dimensional compact space. A novel process is applied by replicating selected components, assessing the classification model, and repeating the replication until performance divergence or convergence. The proposed method aims to explore most significant features and highest-preforming model at the same time, to classify normal subjects from AD and mild cognitive impairment (MCI) patients. In each epoch, a small subset of candidate features is assessed by support vector machine (SVM) classifier. This repeating procedure is continued until the highest performance is achieved. Experimental results reveal the highest performance reported in the literature for this specific classification problem. We obtained a model with accuracies of 98.81%, 81.61%, and 81.40% for AD vs. normal control (NC), MCI vs. NC, and AD vs. MCI classification, respectively.

Early diagnosis in Alzheimer's disease (AD) is important for the administration of new treatments. The purpose of this study was to differentiate mildly/moderately demented AD patients from normal controls by means of activational brain SPECT, and to investigate the correlation between regional cerebral blood flow and dementia severity. Activational brain SPECT was performed 1 week after basal brain SPECT in 12 mild/moderate AD patients according to NINCDS-ADRDA criteria (mean age 69+/-7 years) and in seven healthy, age-matched, volunteer controls (mean age 65+/-9 years). In order to activate the parietal cortex, patients were asked to subtract serial 5's from 100, 2 min before and after the intravenous administration of 925 MBq technetium-99m labelled D,L-hexamethyl-propylene amine oxime (99Tcm-HMPAO). Using a three-headed gamma camera equipped with high resolution collimators, 128 images of 35 s duration in a 64 x 64 matrix were obtained over 360 degrees. Region to whole brain ratios (R/WB) were calculated in three consecutive transaxial slices 2 pixels thick above the orbitomeatal line, and the activation percentage was calculated. No statistical difference was detected between AD patients and normal controls for parietal cortex activation. The correlation coefficient between the Mini-Mental State Examination (MMSE) scoring and the activation percentage was 0.475 in normal controls and 0.175 in AD patients for the left anterior parietal cortex, and 0.353 in normal controls and 0.146 in AD patients for the right anterior parietal cortex. In a visual evaluation of parietal cortex activation, 50% of AD patients were able to activate the parietal cortex, whereas 86% of the normal controls could do so. In our current study, the subtraction of serial 5's was not regarded as a promising task. Further studies are needed to clarify the importance of such tasks in the differential diagnosis of mild/moderate AD patients from normal elderly.

In this work, we propose a novel subclass-based multi-task learning method for feature selection in computer-aided Alzheimer's Disease (AD) or Mild Cognitive Impairment (MCI) diagnosis. Unlike the previous methods that often assumed a unimodal data distribution, we take into account the underlying multipeak1 distribution of classes. The rationale for our approach is that it is highly likely for neuroimaging data to have multiple peaks or modes in distribution, e.g., mixture of Gaussians, due to the inter-subject variability. In this regard, we use a clustering method to discover the multipeak distributional characteristics and define subclasses based on the clustering results, in which each cluster covers a peak in the underlying multipeak distribution. Specifically, after performing clustering for each class, we encode the respective subclasses, i.e., clusters, with their unique codes. In encoding, we impose the subclasses of the same original class close to each other and those of different original classes distinct from each other. By setting the codes as new label vectors of our training samples, we formulate a multi-task learning problem in a ℓ2,1-penalized regression framework, through which we finally select features for classification. In our experimental results on the ADNI dataset, we validated the effectiveness of the proposed method by improving the classification accuracies by 1% (AD vs. Normal Control: NC), 3.25% (MCI vs. NC), 5.34% (AD vs. MCI), and 7.4% (MCI Converter: MCI-C vs. MCI Non-Converter: MCI-NC) compared to the competing single-task learning method. It is remarkable for the performance improvement in MCI-C vs. MCI-NC classification, which is the most important for early diagnosis and treatment. It is also noteworthy that with the strategy of modality-adaptive weights by means of a multi-kernel support vector machine, we maximally achieved the classification accuracies of 96.18% (AD vs. NC), 81.45% (MCI vs. NC), 73.21% (AD vs. MCI), and 74.04% (MCI-C vs. MCI-NC), respectively.

Bruno Dubois: transforming the diagnosis of Alzheimer's disease

Specific patterns of brain atrophy may be helpful in the diagnosis of Alzheimer's disease (AD). In the present study, we set out to evaluate the utility of grey-matter volume in the classification of AD and amnestic mild cognitive impairment (aMCI) compared to normal control (NC) individuals. Voxel-based morphometric analyses were performed on structural MRIs from 35 AD patients, 27 aMCI patients, and 27 NC participants. A two-sample two-tailed t-test was computed between the NC and AD groups to create a map of abnormal grey matter in AD. The brain areas with significant differences were extracted as regions of interest (ROIs), and the grey-matter volumes in the ROIs of the aMCI patients were included to evaluate the patterns of change across different disease severities. Next, correlation analyses between the grey-matter volumes in the ROIs and all clinical variables were performed in aMCI and AD patients to determine whether they varied with disease progression. The results revealed significantly decreased grey matter in the bilateral hippocampus/parahippocampus, the bilateral superior/middle temporal gyri, and the right precuneus in AD patients. The grey-matter volumes were positively correlated with clinical variables. Finally, we performed exploratory linear discriminative analyses to assess the classifying capacity of grey-matter volumes in the bilateral hippocampus and parahippocampus among AD, aMCI, and NC. Leave-one-out cross-validation analyses demonstrated that grey-matter volumes in hippocampus and parahippocampus accurately distinguished AD from NC. These findings indicate that grey-matter volumes are useful in the classification of AD.

High Activities of BACE1 in Brains with Mild Cognitive Impairment

Alzheimer’s disease (AD) constitutes a fatal neurodegenerative disorder and represents the most prevalent form of dementia among the elderly population. Traditional manual AD classification methods, such as clinical diagnosis, are known to be time-consuming and labor-intensive, with relatively low accuracy. Therefore, our work aims to develop a new deep learning framework to tackle this challenge. Our proposed model integrates ConvNeXt with three-dimensional (3D) convolution and incorporates a 3D Squeeze-and-Excitation (3D-SE) attention mechanism to enhance early classification of AD. The experimental data is sourced from the publicly accessible Alzheimer’s disease Neuroimaging Initiative (ADNI) database, with raw Magnetic Resonance Imaging (MRI) data preprocessed using SPM12 software. Subsequently, the preprocessed data is input into the 3D-SEConvNeXt network to perform four classification tasks: distinguishing between AD and Normal Control (NC), Mild Cognitive Impairment (MCI) and NC, AD and MCI, as well as AD, MCI, and NC. The experimental results indicate that the 3D-SEConvNeXt model consistently outperforms alternative models in terms of accuracy, achieving commendable outcomes in early AD diagnostic tasks.

The world’s aging population has given rise to an increasing awareness towards neurodegenerative disorders, including Alzheimers Disease (AD). Treatment options for AD are currently limited, but it is believed that future success depends on our ability to detect the onset of the disease in its early stages. The most frequently used tools for this include neuropsychological assessments, along with genetic, proteomic, and image-based diagnosis. Recently, the applicability of Diffusion Magnetic Resonance Imaging (dMRI) analysis for early diagnosis of AD has also been reported. The sensitivity of dMRI to the microstructural organization of cerebral tissue makes it particularly well-suited to detecting changes which are known to occur in the early stages of AD. Existing dMRI approaches can be divided into two broad categories: region-based and tract-based. In this work, we propose a new approach, which extends region-based approaches to the simultaneous characterization of multiple brain regions. Given a predefined set of features derived from dMRI data, we compute the probabilistic distances between different brain regions and treat the resulting connectivity pattern as an undirected, fully-connected graph. The characteristics of this graph are then used as markers to discriminate between AD subjects and normal controls (NC). Although in this preliminary work we omit subjects in the prodromal stage of AD, mild cognitive impairment (MCI), our method demonstrates perfect separability between AD and NC subject groups with substantial margin, and thus holds promise for fine-grained stratification of NC, MCI and AD populations.

Reactive EEG Biomarkers for Diagnosis and Prognosis of Alzheimer's Disease and Mild Cognitive Impairment.