We aimed to study atrophy and glucose metabolism of the cholinergic basal forebrain in non-demented mutation carriers for autosomal dominant Alzheimer's disease (ADAD). We determined the level of ...evidence for or against atrophy and impaired metabolism of the basal forebrain in 167 non-demented carriers of the Colombian PSEN1 E280A mutation and 75 age- and sex-matched non-mutation carriers of the same kindred using a Bayesian analysis framework. We analyzed baseline MRI, amyloid PET, and FDG-PET scans of the Alzheimer's Prevention Initiative ADAD Colombia Trial. We found moderate evidence against an association of carrier status with basal forebrain volume (Bayes factor (BF
) = 0.182). We found moderate evidence against a difference of basal forebrain metabolism (BF
= 0.167). There was only inconclusive evidence for an association between basal forebrain volume and delayed memory and attention (BF
= 0.884 and 0.184, respectively), and between basal forebrain volume and global amyloid load (BF
= 2.1). Our results distinguish PSEN1 E280A mutation carriers from sporadic AD cases in which cholinergic involvement of the basal forebrain is already detectable in the preclinical and prodromal stages. This indicates an important difference between ADAD and sporadic AD in terms of pathogenesis and potential treatment targets.
The objective was to review the literature on diffusion tensor imaging as well as resting-state functional magnetic resonance imaging and electroencephalography (EEG) to unveil neuroanatomical and ...neurophysiological substrates of Alzheimer's disease (AD) as a brain neural network pathology affecting structural and functional cortical connectivity underlying human cognition.
We reviewed papers registered in PubMed and other scientific repositories on the use of these techniques in amnesic mild cognitive impairment (MCI) and clinically mild AD dementia patients compared to cognitively intact elderly individuals (Controls).
Hundreds of peer-reviewed (cross-sectional and longitudinal) papers have shown in patients with MCI and mild AD compared to Controls (1) impairment of callosal (splenium), thalamic, and anterior-posterior white matter bundles; (2) reduced correlation of resting state blood oxygen level-dependent activity across several intrinsic brain circuits including default mode and attention-related networks; and (3) abnormal power and functional coupling of resting state cortical EEG rhythms. Clinical applications of these measures are still limited.
Structural and functional (in vivo) cortical connectivity measures represent a reliable marker of cerebral reserve capacity and should be used to predict and monitor the evolution of AD and its relative impact on cognitive domains in pre-clinical, prodromal, and dementia stages of AD.
The cholinergic basal forebrain (CBF), comprising different groups of cortically projecting cholinergic neurons, plays a crucial role in higher cognitive processes and has been implicated in diverse ...neuropsychiatric disorders. A distinct corticotopic organization of CBF projections has been revealed in animal studies, but little is known about their organization in the human brain. We explored regional differences in functional connectivity (FC) profiles within the human CBF by applying a clustering approach to resting‐state functional magnetic resonance imaging (rs‐fMRI) data of healthy adult individuals (N = 85; 19–85 years). We further examined effects of age on FC of the identified CBF clusters and assessed the reproducibility of cluster‐specific FC profiles in independent data from healthy older individuals (N = 25; 65–89 years). Results showed that the human CBF is functionally organized into distinct anterior‐medial and posterior‐lateral subdivisions that largely follow anatomically defined boundaries of the medial septum/diagonal band and nucleus basalis Meynert. The anterior‐medial CBF subdivision was characterized by connectivity with the hippocampus and interconnected nodes of an extended medial cortical memory network, whereas the posterior‐lateral subdivision was specifically connected to anterior insula and dorsal anterior cingulate components of a salience/attention network. FC of both CBF subdivisions declined with increasing age, but the overall topography of subregion‐specific FC profiles was reproduced in independent rs‐fMRI data of healthy older individuals acquired in a typical clinical setting. Rs‐fMRI‐based assessments of subregion‐specific CBF function may complement established volumetric approaches for the in vivo study of CBF involvement in neuropsychiatric disorders.
Previous research has described distinct subtypes of Alzheimer's disease (AD) based on the differences in regional patterns of brain atrophy on MRI. We conducted a data-driven exploration of distinct ...AD neurodegeneration subtypes using FDG-PET as a sensitive molecular imaging marker of neurodegenerative processes.
Hierarchical clustering of voxel-wise FDG-PET data from 177 amyloid-positive patients with AD dementia enrolled in the Alzheimer's Disease Neuroimaging Initiative (ADNI) was used to identify distinct hypometabolic subtypes of AD, which were then further characterized with respect to clinical and biomarker characteristics. We then classified FDG-PET scans of 217 amyloid-positive patients with mild cognitive impairment ("prodromal AD") according to the identified subtypes and studied their domain-specific cognitive trajectories and progression to dementia over a follow-up interval of up to 72 months.
Three main hypometabolic subtypes were identified: (i) "typical" (48.6%), showing a classic posterior temporo-parietal hypometabolic pattern; (ii) "limbic-predominant" (44.6%), characterized by old age and a memory-predominant cognitive profile; and (iii) a relatively rare "cortical-predominant" subtype (6.8%) characterized by younger age and more severe executive dysfunction. Subtypes classified in the prodromal AD sample demonstrated similar subtype characteristics as in the AD dementia sample and further showed differential courses of cognitive decline.
These findings complement recent research efforts on MRI-based identification of distinct AD atrophy subtypes and may provide a potentially more sensitive molecular imaging tool for early detection and characterization of AD-related neurodegeneration variants at prodromal disease stages.
A structural covariance network (SCN) has been used successfully in structural
magnetic resonance imaging (sMRI) studies. However, most SCNs have been
constructed by a unitary marker that is ...insensitive for discriminating different
disease phases. The aim of this study was to devise a novel regional radiomics
similarity network (R2SN) that could provide more comprehensive information in
morphological network analysis. R2SNs were constructed by computing the Pearson
correlations between the radiomics features extracted from any pair of regions
for each subject (AAL atlas). We further assessed the small-world property of
R2SNs, and we evaluated the reproducibility in different datasets and through
test-retest analysis. The relationships between the R2SNs and general
intelligence/interregional coexpression of genes were also explored. R2SNs could
be replicated in different datasets, regardless of the use of different feature
subsets. R2SNs showed high reproducibility in the test-retest analysis
(intraclass correlation coefficient > 0.7). In addition, the small-word
property (σ > 2) and the high correlation between gene expression
(
= 0.29,
< 0.001) and general
intelligence were determined for R2SNs. Furthermore, the results have also been
repeated in the Brainnetome atlas. R2SNs provide a novel, reliable, and
biologically plausible method to understand human morphological covariance based
on sMRI.
Gray matter volume and cortical thickness are some of the most popular brain
morphological measures of structural magnetic resonance imaging (sMRI). These
patterns are important for understanding complex brain cognitive function.
However, most of the studies typically analyze single/several anatomical regions
independently without considering associations among brain regions. The
structural covariance network (SCN) is often used to reconstruct the brain
structural network from sMRI and is commonly used to measure the association
between regions in the human brain with morphological similarity. However, most
of the individual SCNs have been constructed by a unitary marker such as gray
volume/cortical thickness with hyposensitivity. We develop a novel, reliable and
biologically plausible brain network to understand human morphological
covariance based on sMRI.
Mean diffusivity (MD) measured by diffusion tensor imaging can reflect microstructural alterations of the brain's gray matter (GM). Therefore, GM MD may be a sensitive marker of neurodegeneration ...related to Alzheimer's Disease (AD). However, due to partial volume effects (PVE), differences in MD may be overestimated because of a higher degree of brain atrophy in AD patients and in cases with mild cognitive impairment (MCI). Here, we evaluated GM MD changes in AD and MCI compared with healthy controls, and the effect of partial volume correction (PVC) on diagnostic utility of MD.
We determined region of interest (ROI) and voxel-wise group differences and diagnostic accuracy of MD and volume measures between matched samples of 39 AD, 39 MCI and 39 healthy subjects before and after PVC. Additionally, we assessed whether effects of GM MD values on diagnosis were mediated by volume.
ROI and voxel-wise group differences were reduced after PVC. When using these ROIs for predicting group separation in logistic models, both PVE corrected and uncorrected GM MD values yielded a poorer diagnostic accuracy in single predictor models than regional volume. For the discrimination of AD patients and healthy controls, the effect of GM MD on diagnosis was significantly mediated by volume of hippocampus and posterior cingulate ROIs.
Our results suggest that GM MD measurements are strongly confounded by PVE in the presence of brain atrophy, underlining the necessity of PVC when using these measurements as specific metrics of microstructural tissue degeneration. Independently of PVC, regional MD was not superior to regional volume in separating prodromal and clinical stages of AD from healthy controls.
•Uncorrected gray matter mean diffusivity (GM MD) is correlated with volume.•Partial volume correction reduces CSF contamination of gray matter MD.•GM MD is not a more accurate diagnostic marker for Alzheimer's disease than volume.
Although convolutional neural networks (CNNs) achieve high diagnostic accuracy for detecting Alzheimer's disease (AD) dementia based on magnetic resonance imaging (MRI) scans, they are not yet ...applied in clinical routine. One important reason for this is a lack of model comprehensibility. Recently developed visualization methods for deriving CNN relevance maps may help to fill this gap as they allow the visualization of key input image features that drive the decision of the model. We investigated whether models with higher accuracy also rely more on discriminative brain regions predefined by prior knowledge.
We trained a CNN for the detection of AD in N = 663 T1-weighted MRI scans of patients with dementia and amnestic mild cognitive impairment (MCI) and verified the accuracy of the models via cross-validation and in three independent samples including in total N = 1655 cases. We evaluated the association of relevance scores and hippocampus volume to validate the clinical utility of this approach. To improve model comprehensibility, we implemented an interactive visualization of 3D CNN relevance maps, thereby allowing intuitive model inspection.
Across the three independent datasets, group separation showed high accuracy for AD dementia versus controls (AUC ≥ 0.91) and moderate accuracy for amnestic MCI versus controls (AUC ≈ 0.74). Relevance maps indicated that hippocampal atrophy was considered the most informative factor for AD detection, with additional contributions from atrophy in other cortical and subcortical regions. Relevance scores within the hippocampus were highly correlated with hippocampal volumes (Pearson's r ≈ -0.86, p < 0.001).
The relevance maps highlighted atrophy in regions that we had hypothesized a priori. This strengthens the comprehensibility of the CNN models, which were trained in a purely data-driven manner based on the scans and diagnosis labels. The high hippocampus relevance scores as well as the high performance achieved in independent samples support the validity of the CNN models in the detection of AD-related MRI abnormalities. The presented data-driven and hypothesis-free CNN modeling approach might provide a useful tool to automatically derive discriminative features for complex diagnostic tasks where clear clinical criteria are still missing, for instance for the differential diagnosis between various types of dementia.
Diffusion tensor imaging (DTI) based assessment of white matter fiber tract integrity can support the diagnosis of Alzheimer's disease (AD). The use of DTI as a biomarker, however, depends on its ...applicability in a multicenter setting accounting for effects of different MRI scanners. We applied multivariate machine learning (ML) to a large multicenter sample from the recently created framework of the European DTI study on Dementia (EDSD). We hypothesized that ML approaches may amend effects of multicenter acquisition. We included a sample of 137 patients with clinically probable AD (MMSE 20.6±5.3) and 143 healthy elderly controls, scanned in nine different scanners. For diagnostic classification we used the DTI indices fractional anisotropy (FA) and mean diffusivity (MD) and, for comparison, gray matter and white matter density maps from anatomical MRI. Data were classified using a Support Vector Machine (SVM) and a Naïve Bayes (NB) classifier. We used two cross-validation approaches, (i) test and training samples randomly drawn from the entire data set (pooled cross-validation) and (ii) data from each scanner as test set, and the data from the remaining scanners as training set (scanner-specific cross-validation). In the pooled cross-validation, SVM achieved an accuracy of 80% for FA and 83% for MD. Accuracies for NB were significantly lower, ranging between 68% and 75%. Removing variance components arising from scanners using principal component analysis did not significantly change the classification results for both classifiers. For the scanner-specific cross-validation, the classification accuracy was reduced for both SVM and NB. After mean correction, classification accuracy reached a level comparable to the results obtained from the pooled cross-validation. Our findings support the notion that machine learning classification allows robust classification of DTI data sets arising from multiple scanners, even if a new data set comes from a scanner that was not part of the training sample.
Abstract Clinical, genetic, and pathological findings suggest a close relationship between amyotrophic lateral sclerosis (ALS) and frontotemporal dementia (FTD). We studied the patterns of cortical ...atrophy across the spectrum between ALS and ALS-FTD. A surface-based morphometry analysis based on an age- and sex-matched sample of 81 ALS patients and 62 healthy control subjects (HC) was conducted. In addition, we used an age-matched subsample of 57 ALS patients and 31 HC to compare cortical thickness between 3 groups of neuropsychologically characterized ALS patients: (1) cognitively unimpaired; (2) cognitively impaired; and (3) ALS-FTD patients. Compared with HC, the entire sample of patients demonstrated cortical thinning in the bilateral precentral gyrus, right precuneus, and right frontal and temporal lobes. ALS-FTD patients showed cortical thinning in regions including the frontal and temporal gyri and the posterior cingulate cortex. Cognitively impaired ALS patients showed cortical thinning in regions largely overlapping with those found in ALS-FTD, but changes were less widespread. In conclusion, the cognitive status of ALS subjects is associated with different patterns of cortical atrophy.
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