Hippocampus atrophy is an early structural feature that can be measured from magnetic resonance imaging (MRI) to improve the diagnosis of neurological diseases. An accurate and robust standardized hippocampus segmentation method is required for reliable atrophy assessment. The aim of this work was to develop and evaluate an automatic segmentation tool (DeepHarp) for hippocampus delineation according to the ADNI harmonized hippocampal protocol (HarP). DeepHarp utilizes a two-step process. First, the approximate location of the hippocampus is identified in T1-weighted MRI datasets using an atlas-based approach, which is used to crop the images to a region-of-interest (ROI) containing the hippocampus. In the second step, a convolutional neural network trained using datasets with corresponding manual hippocampus annotations is used to segment the hippocampus from the cropped ROI. The proposed method was developed and validated using 107 datasets with manually segmented hippocampi according to the ADNI-HarP standard as well as 114 multi-center datasets of patients with Alzheimer’s disease, mild cognitive impairment, cerebrovascular disease, and healthy controls. Twenty-three independent datasets manually segmented according to the ADNI-HarP protocol were used for testing to assess the accuracy, while an independent test-retest dataset was used to assess precision. The proposed DeepHarp method achieved a mean Dice similarity score of 0.88, which was significantly better than four other established hippocampus segmentation methods used for comparison. At the same time, the proposed method also achieved a high test-retest precision (mean Dice score: 0.95). In conclusion, DeepHarp can automatically segment the hippocampus from T1-weighted MRI datasets according to the ADNI-HarP protocol with high accuracy and robustness, which can aid atrophy measurements in a variety of pathologies.

Objective: Attention-deficit/hyperactivity disorder (ADHD) in adulthood and dementia with Lewy bodies (DLB) share many cognitive and noncognitive similarities. The overlapping features between both disorders complicate differential diagnosis. The aim of the current systematic review was to compare patterns of neuropsychological profiles in older adults with ADHD and DLB. Method: Of the 1989 ADHD-related articles and 1332 DLB-related articles screened, 3 ADHD and 25 DLB articles were retained for qualitative synthesis and review. Results: A synthesis of individual study findings revealed isolated working memory deficits for late-life ADHD, and performance deficits in areas of attention, memory, language, and visuoperceptual abilities for DLB. Results were limited by small samples and absence of data in some cognitive domains. Conclusion: These initial findings support potentially unique neurocognitive profiles for ADHD in later life and DLB that would enable practitioners to differentially diagnose and appropriately treat older adults presenting with these phenotypically similar disorders.

Background: The percentage of verbal forgetting (VF%) measure of the Rey Auditory Verbal Learning Test (RAVLT) has been proposed to differentiate patients diagnosed clinically with Alzheimer's disease (AD) and dementia with Lewy bodies (DLB). Objective: To determine if VF% aligns with gold-standard biomarker and autopsy evidence of AD and DLB neuropathology. Methods: Clinical, cognitive, sociodemographic, and biomarker data were collected from 315 patients with baseline cognitive impairment and 485 normal controls from the Alzheimer's Disease Neuroimaging Initiative (ADNI). AD markers included reduced cerebrospinal fluid (CSF) amyloid-␤, elevated total-tau and phosphorylated-tau, hippocampal atrophy, and the presence of amyloid plaques and neurofibrillary tangles at autopsy. DLB markers included reduced CSF ␣-synuclein, preserved hippocampus, atrophied putamen, occipital glucose metabolism, and the presence of Lewy bodies at autopsy. Cognitively impaired participants were classified as AD VF% (n = 190) or DLB VF% (n = 125) based on their RAVLT VF% scores using a 75% cut-off (≥75% = AD VF% , <75% = DLB VF% ). Postmortem data were available for 13 AD VF% participants, 13 DLB VF% patients, and six healthy controls. Results: AD VF% and DLB VF% participants did not differ on CSF or neuroimaging biomarkers, with the exception of total tau levels which were higher in AD VF% . In the subset of participants with autopsy data, comorbid AD and DLB pathology was most frequent in AD VF% participants, and pure DLB pathology was most frequent in DLB VF% participants, however, these differences were not statistically significant. Conclusion:The RAVLT VF% measure does not reliably align with AD and DLB neuropathology in ADNI participants.

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Structural brain changes indicative of dementia occur up to 20 years before the onset of clinical symptoms. Efforts to modify the disease process after the onset of cognitive symptoms have been unsuccessful in recent years. Thus, future trials must begin during the preclinical phases of the disease before symptom onset. Age related cognitive decline is often the result of two coexisting brain pathologies: Alzheimer’s disease (amyloid, tau, and neurodegeneration) and vascular disease. This review article highlights some of the common neuroimaging techniques used to visualize the accumulation of neurodegenerative and vascular pathologies during the preclinical stages of dementia such as structural magnetic resonance imaging, positron emission tomography, and white matter hyperintensities. We also describe some emerging neuroimaging techniques such as arterial spin labeling, diffusion tensor imaging, and quantitative susceptibility mapping. Recent literature suggests that structural imaging may be the most sensitive and cost-effective marker to detect cognitive decline, while molecular positron emission tomography is primarily useful for detecting disease specific pathology later in the disease process. Currently, the presence of vascular disease on magnetic resonance imaging provides a potential target for optimizing vascular risk reduction strategies, and the presence of vascular disease may be useful when combined with molecular and metabolic markers of neurodegeneration for identifying the risk of cognitive impairment.