Whole brain segmentation from structural magnetic resonance imaging (MRI) is a prerequisite for most morphological analyses, but is computationally intense and can therefore delay the availability of ...image markers after scan acquisition. We introduce QuickNAT, a fully convolutional, densely connected neural network that segments a MRI brain scan in 20 s. To enable training of the complex network with millions of learnable parameters using limited annotated data, we propose to first pre-train on auxiliary labels created from existing segmentation software. Subsequently, the pre-trained model is fine-tuned on manual labels to rectify errors in auxiliary labels. With this learning strategy, we are able to use large neuroimaging repositories without manual annotations for training. In an extensive set of evaluations on eight datasets that cover a wide age range, pathology, and different scanners, we demonstrate that QuickNAT achieves superior segmentation accuracy and reliability in comparison to state-of-the-art methods, while being orders of magnitude faster. The speed up facilitates processing of large data repositories and supports translation of imaging biomarkers by making them available within seconds for fast clinical decision making.
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•Introduces a deep learning based whole brain segmentation tool called QuickNAT, processing each 3D MRI T1 brain scans in 20 secs.•The high segmentation accuracy of QuickNAT was evaluated on 5 different benchmark datasets, containing a wide age range, subjects with different pathologies (AD, MCI and CN), and different scanners (1.5T and 3.0T).•QuickNAT can be effectively used for longitudinal studies as it performs well in test-retest and multi-center experiments.
Alzheimer's Disease (AD) is a progressive neurodegenerative disease where biomarkers for disease based on pathophysiology may be able to provide objective measures for disease diagnosis and staging. ...Neuroimaging scans acquired from MRI and metabolism images obtained by FDG-PET provide in-vivo measurements of structure and function (glucose metabolism) in a living brain. It is hypothesized that combining multiple different image modalities providing complementary information could help improve early diagnosis of AD. In this paper, we propose a novel deep-learning-based framework to discriminate individuals with AD utilizing a multimodal and multiscale deep neural network. Our method delivers 82.4% accuracy in identifying the individuals with mild cognitive impairment (MCI) who will convert to AD at 3 years prior to conversion (86.4% combined accuracy for conversion within 1-3 years), a 94.23% sensitivity in classifying individuals with clinical diagnosis of probable AD, and a 86.3% specificity in classifying non-demented controls improving upon results in published literature.
The use of anticholinergic (AC) medication is linked to cognitive impairment and an increased risk of dementia. To our knowledge, this is the first study to investigate the association between AC ...medication use and neuroimaging biomarkers of brain metabolism and atrophy as a proxy for understanding the underlying biology of the clinical effects of AC medications.
To assess the association between AC medication use and cognition, glucose metabolism, and brain atrophy in cognitively normal older adults from the Alzheimer's Disease Neuroimaging Initiative (ADNI) and the Indiana Memory and Aging Study (IMAS).
The ADNI and IMAS are longitudinal studies with cognitive, neuroimaging, and other data collected at regular intervals in clinical and academic research settings. For the participants in the ADNI, visits are repeated 3, 6, and 12 months after the baseline visit and then annually. For the participants in the IMAS, visits are repeated every 18 months after the baseline visit (402 cognitively normal older adults in the ADNI and 49 cognitively normal older adults in the IMAS were included in the present analysis). Participants were either taking (hereafter referred to as the AC+ participants 52 from the ADNI and 8 from the IMAS) or not taking (hereafter referred to as the AC- participants 350 from the ADNI and 41 from the IMAS) at least 1 medication with medium or high AC activity. Data analysis for this study was performed in November 2015.
Cognitive scores, mean fludeoxyglucose F 18 standardized uptake value ratio (participants from the ADNI only), and brain atrophy measures from structural magnetic resonance imaging were compared between AC+ participants and AC- participants after adjusting for potential confounders. The total AC burden score was calculated and was related to target measures. The association of AC use and longitudinal clinical decline (mean SD follow-up period, 32.1 24.7 months range, 6-108 months) was examined using Cox regression.
The 52 AC+ participants (mean SD age, 73.3 6.6 years) from the ADNI showed lower mean scores on Weschler Memory Scale-Revised Logical Memory Immediate Recall (raw mean scores: 13.27 for AC+ participants and 14.16 for AC- participants; P = .04) and the Trail Making Test Part B (raw mean scores: 97.85 seconds for AC+ participants and 82.61 seconds for AC- participants; P = .04) and a lower executive function composite score (raw mean scores: 0.58 for AC+ participants and 0.78 for AC- participants; P = .04) than the 350 AC- participants (mean SD age, 73.3 5.8 years) from the ADNI. Reduced total cortical volume and temporal lobe cortical thickness and greater lateral ventricle and inferior lateral ventricle volumes were seen in the AC+ participants relative to the AC- participants.
The use of AC medication was associated with increased brain atrophy and dysfunction and clinical decline. Thus, use of AC medication among older adults should likely be discouraged if alternative therapies are available.
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.
The Geminiviruses genome is a small, single strand DNA that replicates in the plant cell nucleus. Analogous to animal DNA viruses, Geminiviruses depend on the host replication machinery to amplify ...their genomes and only supply the factors required to initiate their replication. Consequently, Geminiviruses remove the cell-cycle arrest and induce the host replication machinery using an endocycle process. They encode proteins, such as the conserved replication-associated proteins (Rep) that interact with retinoblastoma-like proteins in plants and alter the cell division cycle in yeasts. Therefore, the aim of this work is to analyze the impact of Pepper Golden Mosaic Virus (PepGMV) Rep protein in mammalian cells. Results indicate that the pTracer-SV40:Rep construction obtained in this work can be used to analyze the Rep protein effect in mammalian cells in order to compare the cell cycle regulation mechanisms in plants and animals.
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.
To test the hypothesis that cortical and hippocampal volumes, measured in vivo from volumetric MRI (vMRI) scans, could be used to identify variant subtypes of Alzheimer disease (AD) and to ...prospectively predict the rate of clinical decline.
Amyloid-positive participants with AD from the Alzheimer's Disease Neuroimaging Initiative (ADNI) 1 and ADNI2 with baseline MRI scans (n = 229) and 2-year clinical follow-up (n = 100) were included. AD subtypes (hippocampal sparing HpSp
, limbic predominant LP
, typical AD tAD
) were defined according to an algorithm analogous to one recently proposed for tau neuropathology. Relationships between baseline hippocampal volume to cortical volume ratio (HV:CTV) and clinical variables were examined by both continuous regression and categorical models.
When participants were divided categorically, the HpSp
group showed significantly more AD-like hypometabolism on
F-fluorodeoxyglucose-PET (
< 0.05) and poorer baseline executive function (
< 0.001). Other baseline clinical measures did not differ across the 3 groups. Participants with HpSp
also showed faster subsequent clinical decline than participants with LP
on the Alzheimer's Disease Assessment Scale, 13-Item Subscale (ADAS-Cog
), Mini-Mental State Examination (MMSE), and Functional Assessment Questionnaire (all
< 0.05) and tAD
on the MMSE and Clinical Dementia Rating Sum of Boxes (CDR-SB) (both
< 0.05). Finally, a larger HV:CTV was associated with poorer baseline executive function and a faster slope of decline in CDR-SB, MMSE, and ADAS-Cog
score (
< 0.05). These associations were driven mostly by the amount of cortical rather than hippocampal atrophy.
AD subtypes with phenotypes consistent with those observed with tau neuropathology can be identified in vivo with vMRI. An increased HV:CTV ratio was predictive of faster clinical decline in participants with AD who were clinically indistinguishable at baseline except for a greater dysexecutive presentation.
Late-life depression (LLD) is characterized by considerable heterogeneity in clinical manifestation. Unraveling such heterogeneity might aid in elucidating etiological mechanisms and support ...precision and individualized medicine.
To cross-sectionally and longitudinally delineate disease-related heterogeneity in LLD associated with neuroanatomy, cognitive functioning, clinical symptoms, and genetic profiles.
The Imaging-Based Coordinate System for Aging and Neurodegenerative Diseases (iSTAGING) study is an international multicenter consortium investigating brain aging in pooled and harmonized data from 13 studies with more than 35 000 participants, including a subset of individuals with major depressive disorder. Multimodal data from a multicenter sample (N = 996), including neuroimaging, neurocognitive assessments, and genetics, were analyzed in this study. A semisupervised clustering method (heterogeneity through discriminative analysis) was applied to regional gray matter (GM) brain volumes to derive dimensional representations. Data were collected from July 2017 to July 2020 and analyzed from July 2020 to December 2021.
Two dimensions were identified to delineate LLD-associated heterogeneity in voxelwise GM maps, white matter (WM) fractional anisotropy, neurocognitive functioning, clinical phenotype, and genetics.
A total of 501 participants with LLD (mean SD age, 67.39 5.56 years; 332 women) and 495 healthy control individuals (mean SD age, 66.53 5.16 years; 333 women) were included. Patients in dimension 1 demonstrated relatively preserved brain anatomy without WM disruptions relative to healthy control individuals. In contrast, patients in dimension 2 showed widespread brain atrophy and WM integrity disruptions, along with cognitive impairment and higher depression severity. Moreover, 1 de novo independent genetic variant (rs13120336; chromosome: 4, 186387714; minor allele, G) was significantly associated with dimension 1 (odds ratio, 2.35; SE, 0.15; P = 3.14 ×108) but not with dimension 2. The 2 dimensions demonstrated significant single-nucleotide variant-based heritability of 18% to 27% within the general population (N = 12 518 in UK Biobank). In a subset of individuals having longitudinal measurements, those in dimension 2 experienced a more rapid longitudinal change in GM and brain age (Cohen f2 = 0.03; P = .02) and were more likely to progress to Alzheimer disease (Cohen f2 = 0.03; P = .03) compared with those in dimension 1 (N = 1431 participants and 7224 scans from the Alzheimer's Disease Neuroimaging Initiative ADNI, Baltimore Longitudinal Study of Aging BLSA, and Biomarkers for Older Controls at Risk for Dementia BIOCARD data sets).
This study characterized heterogeneity in LLD into 2 dimensions with distinct neuroanatomical, cognitive, clinical, and genetic profiles. This dimensional approach provides a potential mechanism for investigating the heterogeneity of LLD and the relevance of the latent dimensions to possible disease mechanisms, clinical outcomes, and responses to interventions.
We used a data-driven Bayesian model to automatically identify distinct latent factors of overlapping atrophy patterns from voxel-wise structural MRIs of late-onset Alzheimer’s disease (AD) dementia ...patients. Our approach estimated the extent to which multiple distinct atrophy patterns were expressed within each participant rather than assuming that each participant expressed a single atrophy factor. The model revealed a temporal atrophy factor (medial temporal cortex, hippocampus, and amygdala), a subcortical atrophy factor (striatum, thalamus, and cerebellum), and a cortical atrophy factor (frontal, parietal, lateral temporal, and lateral occipital cortices). To explore the influence of each factor in early AD, atrophy factor compositions were inferred in beta-amyloid–positive (Aβ+) mild cognitively impaired (MCI) and cognitively normal (CN) participants. All three factors were associated with memory decline across the entire clinical spectrum, whereas the cortical factor was associated with executive function decline in Aβ+ MCI participants and AD dementia patients. Direct comparison between factors revealed that the temporal factor showed the strongest association with memory, whereas the cortical factor showed the strongest association with executive function. The subcortical factor was associated with the slowest decline for both memory and executive function compared with temporal and cortical factors. These results suggest that distinct patterns of atrophy influence decline across different cognitive domains. Quantification of this heterogeneity may enable the computation of individual-level predictions relevant for disease monitoring and customized therapies. Factor compositions of participants and code used in this article are publicly available for future research.
Time-dependent data collected in studies of Alzheimer's disease usually has missing and irregularly sampled data points. For this reason time series methods which assume regular sampling cannot be ...applied directly to the data without a pre-processing step. In this paper we use a random forest to learn the relationship between pairs of data points at different time separations. The input vector is a summary of the time series history and it includes both demographic and non-time varying variables such as genetic data. To test the method we use data from the TADPOLE grand challenge, an initiative which aims to predict the evolution of subjects at risk of Alzheimer's disease using demographic, physical and cognitive input data. The task is to predict diagnosis, ADAS-13 score and normalised ventricles volume. While the competition proceeds, forecasting methods may be compared using a leaderboard dataset selected from the Alzheimer's Disease Neuroimaging Initiative (ADNI) and with standard metrics for measuring accuracy. For diagnosis, we find an mAUC of 0.82, and a classification accuracy of 0.73 compared with a benchmark SVM predictor which gives mAUC = 0.62 and BCA = 0.52. The results show that the method is effective and comparable with other methods.
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