Multifactorial mechanisms underlying late-onset Alzheimer's disease (LOAD) are poorly characterized from an integrative perspective. Here spatiotemporal alterations in brain amyloid-β deposition, metabolism, vascular, functional activity at rest, structural properties, cognitive integrity and peripheral proteins levels are characterized in relation to LOAD progression. We analyse over 7,700 brain images and tens of plasma and cerebrospinal fluid biomarkers from the Alzheimer's Disease Neuroimaging Initiative (ADNI). Through a multifactorial data-driven analysis, we obtain dynamic LOAD–abnormality indices for all biomarkers, and a tentative temporal ordering of disease progression. Imaging results suggest that intra-brain vascular dysregulation is an early pathological event during disease development. Cognitive decline is noticeable from initial LOAD stages, suggesting early memory deficit associated with the primary disease factors. High abnormality levels are also observed for specific proteins associated with the vascular system's integrity. Although still subjected to the sensitivity of the algorithms and biomarkers employed, our results might contribute to the development of preventive therapeutic interventions.

Numerous kindreds with familial frontotemporal dementia and/or amyotrophic lateral sclerosis have been linked to chromosome 9, and an expansion of the GGGGCC hexanucleotide repeat in the non-coding region of chromosome 9 open reading frame 72 has recently been identified as the pathogenic mechanism. We describe the key characteristics in the probands and their affected relatives who have been evaluated at Mayo Clinic Rochester or Mayo Clinic Florida in whom the hexanucleotide repeat expansion were found. Forty-three probands and 10 of their affected relatives with DNA available (total 53 subjects) were shown to carry the hexanucleotide repeat expansion. Thirty-six (84%) of the 43 probands had a familial disorder, whereas seven (16%) appeared to be sporadic. Among examined subjects from the 43 families (n = 63), the age of onset ranged from 33 to 72 years (median 52 years) and survival ranged from 1 to 17 years, with the age of onset <40 years in six (10%) and >60 in 19 (30%). Clinical diagnoses among examined subjects included behavioural variant frontotemporal dementia with or without parkinsonism (n = 30), amyotrophic lateral sclerosis (n = 18), frontotemporal dementia/amyotrophic lateral sclerosis with or without parkinsonism (n = 12), and other various syndromes (n = 3). Parkinsonism was present in 35% of examined subjects, all of whom had behavioural variant frontotemporal dementia or frontotemporal dementia/amyotrophic lateral sclerosis as the dominant clinical phenotype. No subject with a diagnosis of primary progressive aphasia was identified with this mutation. Incomplete penetrance was suggested in two kindreds, and the youngest generation had significantly earlier age of onset (>10 years) compared with the next oldest generation in 11 kindreds. Neuropsychological testing showed a profile of slowed processing speed, complex attention/executive dysfunction, and impairment in rapid word retrieval. Neuroimaging studies showed bilateral frontal abnormalities most consistently, with more variable degrees of parietal with or without temporal changes; no case had strikingly focal or asymmetric findings. Neuropathological examination of 14 patients revealed a range of transactive response DNA binding protein molecular weight 43 pathology (10 type A and four type B), as well as ubiquitin-positive cerebellar granular neuron inclusions in all but one case. Motor neuron degeneration was detected in nine patients, including five patients without ante-mortem signs of motor neuron disease. While variability exists, most cases with this mutation have a characteristic spectrum of demographic, clinical, neuropsychological, neuroimaging and especially neuropathological findings.

Brain iron elevation is implicated in Alzheimer's disease (AD) pathogenesis, but the impact of iron on disease outcomes has not been previously explored in a longitudinal study. Ferritin is the major iron storage protein of the body; by using cerebrospinal fluid (CSF) levels of ferritin as an index, we explored whether brain iron status impacts longitudinal outcomes in the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort. We show that baseline CSF ferritin levels were negatively associated with cognitive performance over 7 years in 91 cognitively normal, 144 mild cognitive impairment (MCI) and 67 AD subjects, and predicted MCI conversion to AD. Ferritin was strongly associated with CSF apolipoprotein E levels and was elevated by the Alzheimer's risk allele, APOE-ɛ4. These findings reveal that elevated brain iron adversely impacts on AD progression, and introduce brain iron elevation as a possible mechanism for APOE-ɛ4 being the major genetic risk factor for AD.

There is considerable debate whether Alzheimer's disease (AD) originates in basal forebrain or entorhinal cortex. Here we examined whether longitudinal decreases in basal forebrain and entorhinal cortex grey matter volume were interdependent and sequential. In a large cohort of age-matched older adults ranging from cognitively normal to AD, we demonstrate that basal forebrain volume predicts longitudinal entorhinal degeneration. Models of parallel degeneration or entorhinal origin received negligible support. We then integrated volumetric measures with an amyloid biomarker sensitive to pre-symptomatic AD pathology. Comparison between cognitively matched normal adult subgroups, delineated according to the amyloid biomarker, revealed abnormal degeneration in basal forebrain, but not entorhinal cortex. Abnormal degeneration in both basal forebrain and entorhinal cortex was only observed among prodromal (mildly amnestic) individuals. We provide evidence that basal forebrain pathology precedes and predicts both entorhinal pathology and memory impairment, challenging the widely held belief that AD has a cortical origin.

A new chaos-wavelet approach is presented for electroencephalogram (EEG)-based diagnosis of Alzheimer's disease (AD) employing a recently developed concept in graph theory, visibility graph (VG). The approach is based on the research ideology that nonlinear features may not reveal differences between AD and control group in the band-limited EEG, but may represent noticeable differences in certain sub-bands. Hence, complexity of EEGs is computed using the VGs of EEGs and EEG sub-bands produced by wavelet decomposition. Two methods are employed for computation of complexity of the VGs: one based on the power of scale-freeness of a graph structure and the other based on the maximum eigenvalue of the adjacency matrix of a graph. Analysis of variation is used for feature selection. Two classifiers are applied to the selected features to distinguish AD and control EEGs: a Radial Basis Function Neural Network (RBFNN) and a two-stage classifier consisting of Principal Component Analysis (PCA) and the RBFNN. After comprehensive statistical studies, effective classification features and mathematical markers were discovered. Finally, using the discovered features and a two-stage classifier (PCA-RBFNN), a high diagnostic accuracy of 97.7% was obtained.

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Recently the senior author and his associates developed a spatiotemporal wavelet-chaos methodology for the analysis of electroencephalograms (EEGs) and their subbands for discovering potential markers of abnormality in Alzheimer disease (AD). In this study, fractal dimension (FD) is used for the evaluation of the dynamical changes in the AD brain. The approach presented in this study is based on the research ideology that nonlinear features, such as FD, may not show significant differences between the AD and the control groups in the band-limited EEG, but may manifest in certain subbands. First, 2 different FD algorithms for computing the fractality of EEGs are investigated and their efficacy for yielding potential mathematical markers of AD is compared. They are Katz FD (KFD) and Higuchi FD. Significant features in different loci and different EEG subbands or band-limited EEG for discrimination of the AD and the control groups are determined by analysis of variation. The most discriminative FD and the corresponding loci and EEG subbands for discriminating between AD and healthy EEGs are discovered. As KFD of all loci in the β subband showed very high ability (P value <0.001) in discriminating between the groups, all KFDs are abstracted in 1 global KFD by averaging across loci in each of the 2 eyes-closed and eyes-open conditions. This leads to a more robust classification in terms of common variation of electrode positions than a classification based on separate KFDs of certain loci. Finally, based on the 2 global features separately and together, linear discriminant analysis is used to classify EEGs of AD and elderly normal patients. A high accuracy of 99.3% was obtained for the diagnosis of the AD based on the global KFD in the β-band of the eyes-closed condition with a sensitivity of 100% and a specificity of 97.8%.

Accurate diagnosis of mild cognitive impairment (MCI) before conversion to Alzheimer’s disease (AD) is invaluable for patient treatment. Many works showed that MCI and AD affect functional and structural connections between brain regions as well as the shape of cortical regions. However, ‘shape connections’ between brain regions are rarely investigated -e.g., how morphological attributes such as cortical thickness and sulcal depth of a specific brain region change in relation to morphological attributes in other regions. To fill this gap, we unprecedentedly design morphological brain multiplexes for late MCI/AD classification. Specifically, we use structural T1-w MRI to define morphological brain networks, each quantifying similarity in morphology between different cortical regions for a specific cortical attribute. Then, we define a brain multiplex where each intra-layer represents the morphological connectivity network of a specific cortical attribute, and each inter-layer encodes the similarity between two consecutive intra-layers. A significant performance gain is achieved when using the multiplex architecture in comparison to other conventional network analysis architectures. We also leverage this architecture to discover morphological connectional biomarkers fingerprinting the difference between late MCI and AD stages, which included the right entorhinal cortex and right caudal middle frontal gyrus.