Introduction: We performed a systematic review and meta‐analysis of the Alzheimer's disease (AD) literature to examine consistency of functional connectivity alterations in AD dementia and mild cognitive impairment, using resting‐state functional magnetic resonance imaging.Methods: Studies were screened using a standardized procedure. Multiresolution statistics were performed to assess the spatial consistency of findings across studies.Results: Thirty‐four studies were included (1363 participants, average 40 per study). Consistent alterations in connectivity were found in the default mode, salience, and limbic networks in patients with AD dementia, mild cognitive impairment, or in both groups. We also identified a strong tendency in the literature toward specific examination of the default mode network.Discussion: Convergent evidence across the literature supports the use of resting‐state connectivity as a biomarker of AD. The locations of consistent alterations suggest that highly connected hub regions in the brain might be an early target of AD.

Aetiological and clinical heterogeneity is increasingly recognized as a common characteristic of Alzheimer’s disease and related dementias. This heterogeneity complicates diagnosis, treatment, and the design and testing of new drugs. An important line of research is discovery of multimodal biomarkers that will facilitate the targeting of subpopulations with homogeneous pathophysiological signatures. High-throughput ‘omics’ are unbiased data-driven techniques that probe the complex aetiology of Alzheimer’s disease from multiple levels (e.g. network, cellular, and molecular) and thereby account for pathophysiological heterogeneity in clinical populations. This review focuses on data reduction analyses that identify complementary disease-relevant perturbations for three omics techniques: neuroimaging-based subtypes, metabolomics-derived metabolite panels, and genomics-related polygenic risk scores. Neuroimaging can track accrued neurodegeneration and other sources of network impairments, metabolomics provides a global small-molecule snapshot that is sensitive to ongoing pathological processes, and genomics characterizes relatively invariant genetic risk factors representing key pathways associated with Alzheimer’s disease. Following this focused review, we present a roadmap for assembling these multiomics measurements into a diagnostic tool highly predictive of individual clinical trajectories, to further the goal of personalized medicine in Alzheimer’s disease.

Introduction: Artificial intelligence (AI) and neuroimaging offer new opportunities for diagnosis and prognosis of dementia.Methods: We systematically reviewed studies reporting AI for neuroimaging in diagnosis and/or prognosis of cognitive neurodegenerative diseases.Results: A total of 255 studies were identified. Most studies relied on the Alzheimer's Disease Neuroimaging Initiative dataset. Algorithmic classifiers were the most commonly used AI method (48%) and discriminative models performed best for differentiating Alzheimer's disease from controls. The accuracy of algorithms varied with the patient cohort, imaging modalities, and stratifiers used. Few studies performed validation in an independent cohort.Discussion: The literature has several methodological limitations including lack of sufficient algorithm development descriptions and standard definitions. We make recommendations to improve model validation including addressing key clinical questions, providing sufficient description of AI methods and validating findings in independent datasets. Collaborative approaches between experts in AI and medicine will help achieve the promising potential of AI tools in practice.Highlights: There has been a rapid expansion in the use of machine learning for diagnosis and prognosis in neurodegenerative disease Most studies (71%) relied on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset with no other individual dataset used more than five times There has been a recent rise in the use of more complex discriminative models (e.g., neural networks) that performed better than other classifiers for classification of AD vs healthy controls We make recommendations to address methodological considerations, addressing key clinical questions, and validation We also make recommendations for the field more broadly to standardize outcome measures, address gaps in the literature, and monitor sources of bias

Action myoclonus–renal failure syndrome (AMRF) is a distinctive form of progressive myoclonus epilepsy associated with renal dysfunction. The syndrome was not recognized prior to the advent of dialysis and renal transplantation because of its rapidly fatal course if renal failure is untreated. The first and only description of AMRF was in four French Canadian patients in three families (Andermann et al., 1986). We now describe 15 individuals with AMRF from five countries, including a follow-up of the four French Canadian patients, allowing a more complete characterization of this disease. Our 15 patients with AMRF belong to nine different families. Segregation analyses were compatible with autosomal recessive inheritance. In addition, our findings show that AMRF can present with either renal or neurological features. Tremor (onset 17–26 years, mean 19.8 years, median 19 years) and progressively disabling action myoclonus (onset 14–29 years, mean 21.7 years, median 21 years), with infrequent generalized seizures (onset 20–28 years, mean 22.7 years, median 22 years) and cerebellar features are characteristic. Proteinuria, detected between ages 9 and 30 years in all cases, progressed to renal failure in 12 out of 15 patients within 0–8 years after proteinuria detection. Brain autopsy in two patients revealed extraneuronal pigment accumulation. Renal biopsies showed collapsing glomerulopathy, a severe variant of focal glomerulosclerosis. This study extends the AMRF phenotype, and demonstrates a more extensive ethnic and geographic distribution of a syndrome originally believed to be confined to individuals of French Canadian ancestry. The independent progression of neurological and renal disorders in AMRF suggests a unitary molecular lesion with pleiotropic effects. Our results demonstrate that the renal lesion in AMRF is a recessive form of collapsing glomerulopathy. Genes identified for focal segmental glomerulosclerosis and involved with the function of the glomerular basement membrane and related proteins are thus good candidates. Treatment can improve quality of life and extend the lifespan of these patients. Dialysis and renal transplantation are effective for the renal but not the neurological features, which continue to progress even in the presence of normalized renal function; the latter can be managed with anti-myoclonic and anti-epileptic drugs.

Introduction Brain cells secrete extracellular microvesicles (EVs) that cross the blood‐brain barrier. Involved in cell‐to‐cell communication, EVs contain surface markers and a biologically active cargo of molecules specific to their tissue (and cell) of origin, reflecting the tissue or cell's physiological state. Isolation of brain‐secreted EVs (BEVs) from blood provides a minimally invasive way to sample components of brain tissue in Alzheimer's disease (AD), and is considered a form of “liquid biopsy.” Methods We performed a comprehensive review of the PubMed literature to assess the biomarker and therapeutic potential of blood‐isolated BEVs in AD. Results We summarize methods used for BEV isolation, validation, and novel biomarker discovery, as well as provide insights from 26 studies in humans on the biomarker potential in AD of four cell‐specific BEVs isolated from blood: neuron‐, neural precursor‐, astrocyte‐, and brain vasculature–derived BEVs. Of these, neuron‐derived BEVs has been investigated on several fronts, and these include levels of amyloid‐β and tau proteins, as well as synaptic proteins. In addition, we provide a synopsis of the current landscape of BEV‐based evaluation/monitoring of AD therapeutics based on two published trials and a review of registered clinical trials. Discussion Blood‐isolated BEVs have emerged as a novel player in the study of AD, with enormous potential as a diagnostic, evaluation of therapeutics, and treatment tool. The literature has largely concentrated on neuron‐derived BEVs in the blood in AD. Given the multifactorial pathophysiology of AD, additional studies, in neuron‐derived and other brain cell–specific BEVs are warranted to establish BEVs as a robust blood‐based biomarker of AD.