Deep learning-based quantification of brain atrophy using 2D T1-weighted MRI for Alzheimer's disease classification.

Background: Determining brain atrophy is crucial for the diagnosis of neurodegenerative diseases. Despite detailed brain atrophy assessments using three-dimensional (3D) T1-weighted magnetic resonance imaging, their practical utility is limited by cost and time. This study introduces deep learning algorithms for quantifying brain atrophy using a more accessible two-dimensional (2D) T1, aiming to achieve cost-effective differentiation of dementia of the Alzheimer's type (DAT) from cognitively unimpaired (CU), while maintaining or exceeding the performance obtained with T1-3D individuals and to accurately predict AD-specific atrophy similarity and atrophic changes [W-scores and Brain Age Index (BAI)]. Methods: Involving 924 participants (478 CU and 446 DAT), our deep learning models were trained on cerebrospinal fluid (CSF) volumes from 2D T1 images and compared with 3D T1 images. The performance of the models in differentiating DAT from CU was assessed using receiver operating characteristic analysis. Pearson's correlation analyses were used to evaluate the relations between 3D T1 and 2D T1 measurements of cortical thickness and CSF volumes, AD-specific atrophy similarity, W-scores, and BAIs. Results: Our deep learning models demonstrated strong correlations between 2D and 3D T1-derived CSF volumes, with correlation coefficients r ranging from 0.805 to 0.971. The algorithms based on 2D T1 accurately distinguished DAT from CU with high accuracy (area under the curve values of 0.873), which were comparable to those of algorithms based on 3D T1. Algorithms based on 2D T1 image-derived CSF volumes showed high correlations in AD-specific atrophy similarity (r = 0.915), W-scores for brain atrophy (0.732 ≤ r ≤ 0.976), and BAIs (r = 0.821) compared with those based on 3D T1 images. Conclusion: Deep learning-based analysis of 2D T1 images is a feasible and accurate alternative for assessing brain atrophy, offering diagnostic precision comparable to that of 3D T1 imaging. This approach offers the advantage of the availability of T1-2D imaging, as well as reduced time and cost, while maintaining diagnostic precision comparable to T1-3D.

Identifying the regional substrates predictive of Alzheimer's disease progression through a convolutional neural network model and occlusion.

Progressive brain atrophy is a key neuropathological hallmark of Alzheimer's disease (AD) dementia. However, atrophy patterns along the progression of AD dementia are diffuse and variable and are often missed by univariate methods. Consequently, identifying the major regional atrophy patterns underlying AD dementia progression is challenging. In the current study, we propose a method that evaluates the degree to which specific regional atrophy patterns are predictive of AD dementia progression, while holding all other atrophy changes constant using a total sample of 334 subjects. We first trained a dense convolutional neural network model to differentiate individuals with mild cognitive impairment (MCI) who progress to AD dementia versus those with a stable MCI diagnosis. Then, we retested the model multiple times, each time occluding different regions of interest (ROIs) from the model's testing set's input. We also validated this approach by occluding ROIs based on Braak's staging scheme. We found that the hippocampus, fusiform, and inferior temporal gyri were the strongest predictors of AD dementia progression, in agreement with established staging models. We also found that occlusion of limbic ROIs defined according to Braak stage III had the largest impact on the performance of the model. Our predictive model reveals the major regional patterns of atrophy predictive of AD dementia progression. These results highlight the potential for early diagnosis and stratification of individuals with prodromal AD dementia based on patterns of cortical atrophy, prior to interventional clinical trials.

Automated brain volumetric program measuring regional brain atrophy in diagnosis of mild cognitive impairment and Alzheimer's disease dementia.

A quantitative analysis of brain volume can assist in the diagnosis of Alzheimer's disease (AD) which is ususally accompanied by brain atrophy. With an automated analysis program Quick Brain Volumetry (QBraVo) developed for volumetric measurements, we measured regional volumes and ratios to evaluate their performance in discriminating AD dementia (ADD) and mild cognitive impairment (MCI) patients from normal controls (NC). Validation of QBraVo was based on intra-rater and inter-rater reliability with a manual measurement. The regional volumes and ratios to total intracranial volume (TIV) and to total brain volume (TBV) or total cerebrospinal fluid volume (TCV) were compared among subjects. The regional volume to total cerebellar volume ratio named Standardized Atrophy Volume Ratio (SAVR) was calculated to compare brain atrophy. Diagnostic performances to distinguish among NC, MCI, and ADD were compared between MMSE, SAVR, and the predictive model. In total, 56 NCs, 44 MCI, and 45 ADD patients were enrolled. The average run time of QBraVo was 5 min 36 seconds. Intra-rater reliability was 0.999. Inter-rater reliability was high for TBV, TCV, and TIV (R = 0.97, 0.89 and 0.93, respectively). The medial temporal SAVR showed the highest performance for discriminating ADD from NC (AUC = 0.808, diagnostic accuracy = 80.2%). The predictive model using both MMSE and medial temporal SAVR improved the diagnostic performance for MCI in NC (AUC = 0.844, diagnostic accuracy = 79%). Our results demonstrated QBraVo is a fast and accurate method to measure brain volume. The regional volume calculated as SAVR could help to diagnose ADD and MCI and increase diagnostic accuracy for MCI.

Model-based stratification of progression along the Alzheimer disease continuum highlights the centrality of biomarker synergies.

BACKGROUND: The progression rates of Alzheimer's disease (AD) are variable and dynamic, yet the mechanisms that contribute to heterogeneity in progression rates remain ill-understood. Particularly, the role of synergies in pathological processes reflected by biomarkers for amyloid-beta ('A'), tau ('T'), and neurodegeneration ('N') in progression along the AD continuum is not fully understood. METHODS: Here, we used a combination of model and data-driven approaches to address this question. Working with a large dataset (N = 321 across the training and testing cohorts), we first applied unsupervised clustering on longitudinal cognitive assessments to divide individuals on the AD continuum into those showing fast vs. moderate decline. Next, we developed a deep learning model that differentiated fast vs. moderate decline using baseline AT(N) biomarkers. RESULTS: Training the model with AT(N) biomarker combination revealed more prognostic utility than any individual biomarkers alone. We additionally found little overlap between the model-driven progression phenotypes and established atrophy-based AD subtypes. Our model showed that the combination of all AT(N) biomarkers had the most prognostic utility in predicting progression along the AD continuum. A comprehensive AT(N) model showed better predictive performance than biomarker pairs (A(N) and T(N)) and individual biomarkers (A, T, or N). CONCLUSIONS: This study combined data and model-driven methods to uncover the role of AT(N) biomarker synergies in the progression of cognitive decline along the AD continuum. The results suggest a synergistic relationship between AT(N) biomarkers in determining this progression, extending previous evidence of A-T synergistic mechanisms.

Differential Role for Hippocampal Subfields in Alzheimer's Disease Progression Revealed with Deep Learning.

Mild cognitive impairment (MCI) is often considered the precursor of Alzheimer's disease. However, MCI is associated with substantially variable progression rates, which are not well understood. Attempts to identify the mechanisms that underlie MCI progression have often focused on the hippocampus but have mostly overlooked its intricate structure and subdivisions. Here, we utilized deep learning to delineate the contribution of hippocampal subfields to MCI progression. We propose a dense convolutional neural network architecture that differentiates stable and progressive MCI based on hippocampal morphometry with an accuracy of 75.85%. A novel implementation of occlusion analysis revealed marked differences in the contribution of hippocampal subfields to the performance of the model, with presubiculum, CA1, subiculum, and molecular layer showing the most central role. Moreover, the analysis reveals that 10.5% of the volume of the hippocampus was redundant in the differentiation between stable and progressive MCI.

Subtyping of mild cognitive impairment using a deep learning model based on brain atrophy patterns.

Trajectories of cognitive decline vary considerably among individuals with mild cognitive impairment (MCI). To address this heterogeneity, subtyping approaches have been developed, with the objective of identifying more homogeneous subgroups. To date, subtyping of MCI has been based primarily on cognitive measures, often resulting in indistinct boundaries between subgroups and limited validity. Here, we introduce a subtyping method for MCI based solely upon brain atrophy. We train a deep learning model to differentiate between Alzheimer's disease (AD) and cognitively normal (CN) subjects based on whole-brain MRI features. We then deploy the trained model to classify MCI subjects based on whole-brain gray matter resemblance to AD-like or CN-like patterns. We subsequently validate the subtyping approach using cognitive, clinical, fluid biomarker, and molecular imaging data. Overall, the results suggest that atrophy patterns in MCI are sufficiently heterogeneous and can thus be used to subtype individuals into biologically and clinically meaningful subgroups.

Automatic Segmentation of Corpus Callosum in Midsagittal Based on Bayesian Inference Consisting of Sparse Representation Error and Multi-Atlas Voting.

In this paper, we introduce a novel automatic method for Corpus Callosum (CC) in midsagittal plane segmentation. The robust segmentation of CC in midsagittal plane is key role for quantitative study of structural features of CC associated with various neurological disorder such as epilepsy, autism, Alzheimer's disease, and so on. Our approach is based on Bayesian inference using sparse representation and multi-atlas voting which both methods are used in various medical imaging, and show outstanding performance. Prior information in the proposed Bayesian inference is obtained from probability map generated from multi-atlas voting. The probability map contains the information of shape and location of CC of target image. Likelihood in the proposed Bayesian inference is obtained from gamma distribution function, generated from reconstruction errors (or sparse representation error), which are calculated in sparse representation of target patch using foreground dictionary and background dictionary each. Unlike the usual sparse representation method, we added gradient magnitude and gradient direction information to the patches of dictionaries and target, which had better segmentation performance than when not added. We compared three main segmentation results as follow: (1) the joint label fusion (JLF) method which is state-of-art method in multi-atlas voting based segmentation for evaluation of our method; (2) prior information estimated from multi-atlas voting only; (3) likelihood estimated from comparison of the reconstruction errors from sparse representation error only; (4) the proposed Bayesian inference. The methods were evaluated using two data sets of T1-weighted images, which one data set consists of 100 normal young subjects and the other data set consist of 25 normal old subjects and 22 old subjects with heavy drinker. In both data sets, the proposed Bayesian inference method has significantly the best segmentation performance than using each method separately.

Lateral Ventricle Enlargement and Cortical Thinning in Idiopathic Normal-pressure Hydrocephalus Patients.

We utilized three-dimensional, surface-based, morphometric analysis to investigate ventricle shape between 2 groups: (1) idiopathic normal-pressure hydrocephalus (INPH) patients who had a positive response to the cerebrospinal fluid tap test (CSFTT) and (2) healthy controls. The aims were (1) to evaluate the location of INPH-related structural abnormalities of the lateral ventricles and (2) to investigate relationships between lateral ventricular enlargement and cortical thinning in INPH patients. Thirty-three INPH patients and 23 healthy controls were included in this study. We used sparse canonical correlation analysis to show correlated regions of ventricular surface expansion and cortical thinning. Significant surface expansion in the INPH group was observed mainly in clusters bilaterally located in the superior portion of the lateral ventricles, adjacent to the high convexity of the frontal and parietal regions. INPH patients showed a significant bilateral expansion of both the temporal horns of the lateral ventricles and the medial aspects of the frontal horns of the lateral ventricles to surrounding brain regions, including the medial frontal lobe. Ventricular surface expansion was associated with cortical thinning in the bilateral orbitofrontal cortex, bilateral rostral anterior cingulate cortex, left parahippocampal cortex, left temporal pole, right insula, right inferior temporal cortex, and right fusiform gyrus. These results suggest that patients with INPH have unique patterns of ventricular surface expansion. Our findings encourage future studies to elucidate the underlying mechanism of lateral ventricular morphometric abnormalities in INPH patients.

Multi-Modality Sparse Representation for Alzheimer's Disease Classification.

BACKGROUND: Alzheimer's disease (AD) and mild cognitive impairment (MCI) are age-related neurodegenerative diseases characterized by progressive loss of memory and irreversible cognitive functions. The hippocampus, a brain area critical for learning and memory processes, is especially susceptible to damage at early stages of AD. OBJECTIVE: We aimed to develop prediction model using a multi-modality sparse representation approach. METHODS: We proposed a sparse representation approach to the hippocampus using structural T1-weighted magnetic resonance imaging (MRI) and 18-fluorodeoxyglucose-positron emission tomography (FDG-PET) to distinguish AD/MCI from healthy control subjects (HCs). We considered structural and function information for the hippocampus and applied a sparse patch-based approach to effectively reduce the dimensions of neuroimaging biomarkers. RESULTS: In experiments using Alzheimer's Disease Neuroimaging Initiative data, our proposed method demonstrated more reliable than previous classification studies. The effects of different parameters on segmentation accuracy were also evaluated. The mean classification accuracy obtained with our proposed method was 0.94 for AD/HCs, 0.82 for MCI/HCs, and 0.86 for AD/MCI. CONCLUSION: We extracted multi-modal features from automatically defined hippocampal regions of training subjects and found this method to be discriminative and robust for AD and MCI classification. The extraction of features in T1 and FDG-PET images is expected to improve classification performance due to the relationship between brain structure and function.

Synergistic effects of longitudinal amyloid and vascular changes on lobar microbleeds.

OBJECTIVE: To determine whether amyloid and hypertensive cerebral small vessel disease (hCSVD) changes synergistically affect the progression of lobar microbleeds in patients with subcortical vascular mild cognitive impairment (svMCI). METHODS: Among 72 patients with svMCI who underwent brain MRI and [11C] Pittsburgh compound B (PiB)-PET, 52 (72.2%) completed the third year of follow-up. These patients were evaluated by annual neuropsychological testing, brain MRI, and follow-up PiB-PET. RESULTS: Over 3 years, 31 of 52 patients (59.6%) had incident cerebral microbleeds (CMBs) in the lobar and deep regions. Both baseline and longitudinal changes in lacune numbers were associated with increased numbers of lobar and deep microbleeds, while baseline and longitudinal changes in PiB uptake ratio were associated only with the progression of lobar microbleeds, especially in the temporal, parietal, and occipital areas. Regional white matter hyperintensity severity was also associated with regional lobar CMBs in the parietal and occipital regions. There were interactive effects between baseline and longitudinal lacune number and PiB retention on lobar microbleed progression. Increased lobar, but not deep, CMBs were associated with decreased scores in the digit span backward task and Rey-Osterrieth Complex Figure Test. CONCLUSIONS: Our findings suggest that amyloid-related pathology and hCSVD have synergistic effects on the progression of lobar microbleeds, providing new clinical insight into the interaction between amyloid burden and hCSVD on CMB progression and cognitive decline with implications for developing effective prevention strategies.

A Comparison of Substantia Nigra T1 Hyperintensity in Parkinson's Disease Dementia, Alzheimer's Disease and Age-Matched Controls: Volumetric Analysis of Neuromelanin Imaging.

OBJECTIVE: Neuromelanin loss of substantia nigra (SN) can be visualized as a T1 signal reduction on T1-weighted high-resolution imaging. We investigated whether volumetric analysis of T1 hyperintensity for SN could be used to differentiate between Parkinson's disease dementia (PDD), Alzheimer's disease (AD) and age-matched controls. MATERIALS AND METHODS: This retrospective study enrolled 10 patients with PDD, 18 patients with AD, and 13 age-matched healthy elderly controls. MR imaging was performed at 3 tesla. To measure the T1 hyperintense area of SN, we obtained an axial thin section high-resolution T1-weighted fast spin echo sequence. The volumes of interest for the T1 hyperintense SN were drawn onto heavily T1-weighted FSE sequences through midbrain level, using the MIPAV software. The measurement differences were tested using the Kruskal-Wallis test followed by a post hoc comparison. RESULTS: A comparison of the three groups showed significant differences in terms of volume of T1 hyperintensity (p < 0.001, Bonferroni corrected). The volume of T1 hyperintensity was significantly lower in PDD than in AD and normal controls (p < 0.005, Bonferroni corrected). However, the volume of T1 hyperintensity was not different between AD and normal controls (p = 0.136, Bonferroni corrected). CONCLUSION: The volumetric measurement of the T1 hyperintensity of SN can be an imaging marker for evaluating neuromelanin loss in neurodegenerative diseases and a differential in PDD and AD cases.

Posterior Ventricular Enlargement to Differentiate Dementia with Lewy Bodies from Alzheimer's Disease.

BACKGROUND: Enlargement of the lateral ventricle is observed in dementia associated with Alzheimer's disease (AD) and dementia with Lewy bodies (DLB). OBJECTIVE: The degree of anteroposterior ventricular enlargement and its correlation with clinical and neuropsychological features were investigated in DLB patients. METHODS: Forty-eight patients with DLB, 76 with AD, and 45 subjects with normal cognition (NC) underwent structural brain MRI and detailed neuropsychological tests. Ventricular shape was compared among the groups by visual inspection. Posterior ventricle enlargement (PVE) was defined as the ratio of the distance between the temporal and occipital horns of the lateral ventricle to the distance between the temporal horn of the lateral ventricle and occipital pole of the brain. RESULTS: After controlling for age, sex, and education, higher PVE was observed in the DLB group than in the AD group (68.5 ± 7.9% versus 62.8 ± 9.0%, respectively; p = 0.001) or the NC group (61.9 ± 9.9%, p = 0.002). However, higher PVE was not associated with poorer neuropsychological performance, nor was it associated with any clinical features in the DLB group after controlling for age, sex, and education. CONCLUSION: PVE occurs more often in DLB than in AD and NC. However, it is unclear how PVE is related to the clinical and neuropsychological features of DLB.

Cognitive impairment differs between neuromyelitis optica spectrum disorder and multiple sclerosis.

OBJECTIVE: To compare the frequency and pattern of cognitive impairment (CI) between patients with neuromyelitis optica spectrum disorder (NMOSD) and multiple sclerosis (MS). METHODS: A total of 82 NMOSD patients, 58 MS patients, and 45 healthy controls (HCs) underwent a neuropsychological assessment. RESULTS: CI was observed in 29% of NMOSD and 50% of MS patients (p < 0.001); CI was considered present if a patient scored lower than the fifth percentile compared with HCs in at least three domains. A lower frequency of CI was consistently found when CI was indicated by at least two failed tests (p < 0.001). MS patients performed worse than did NMOSD patients on verbal learning and verbal and visual memory tests. Levels of education and depression and the interval from disease onset to treatment were associated with a negative influence on cognition in patients with NMOSD. CONCLUSION: CI in patients with NMOSD may be not as common as in patients with MS. MS patients exhibited severe impairment, particularly on learning and memory tests, compared with NMOSD patients. Differential prevalence and patterns of CI between NMOSD and MS patients suggest that the two diseases have different mechanisms of brain injury.

Automated Segmentation of Cerebellum Using Brain Mask and Partial Volume Estimation Map.

While segmentation of the cerebellum is an indispensable step in many studies, its contrast is not clear because of the adjacent cerebrospinal fluid, meninges, and cerebra peduncle. Thus, various cerebellar segmentation methods, such as a deformable model or a template-based algorithm might exhibit incorrect segmentation of the venous sinuses and the cerebellar peduncle. In this study, we propose a fully automated procedure combining cerebellar tissue classification, a template-based approach, and morphological operations sequentially. The cerebellar region was defined approximately by removing the cerebral region from the brain mask. Then, the noncerebellar region was trimmed using a morphological operator and the brain-stem atlas was aligned to the individual brain to define the brain-stem area. The proposed method was validated with the well-known FreeSurfer and ITK-SNAP packages using the dice similarity index and recall and precision scores. As a result, the proposed method was significantly better than the other methods for the dice similarity index (0.93, FreeSurfer: 0.92, ITK-SNAP: 0.87) and precision (0.95, FreeSurfer: 0.90, ITK-SNAP: 0.93). Therefore, it could be said that the proposed method yielded a robust and accurate segmentation result. Moreover, additional postprocessing with the brain-stem atlas could improve its result.

Multimodal Discrimination of Alzheimer's Disease Based on Regional Cortical Atrophy and Hypometabolism.

Structural MR image (MRI) and 18F-Fluorodeoxyglucose-positron emission tomography (FDG-PET) have been widely employed in diagnosis of both Alzheimer's disease (AD) and mild cognitive impairment (MCI) pathology, which has led to the development of methods to distinguish AD and MCI from normal controls (NC). Synaptic dysfunction leads to a reduction in the rate of metabolism of glucose in the brain and is thought to represent AD progression. FDG-PET has the unique ability to estimate glucose metabolism, providing information on the distribution of hypometabolism. In addition, patients with AD exhibit significant neuronal loss in cerebral regions, and previous AD research has shown that structural MRI can be used to sensitively measure cortical atrophy. In this paper, we introduced a new method to discriminate AD from NC based on complementary information obtained by FDG and MRI. For accurate classification, surface-based features were employed and 12 predefined regions were selected from previous studies based on both MRI and FDG-PET. Partial least square linear discriminant analysis was employed for making diagnoses. We obtained 93.6% classification accuracy, 90.1% sensitivity, and 96.5% specificity in discriminating AD from NC. The classification scheme had an accuracy of 76.5% and sensitivity and specificity of 46.5% and 89.6%, respectively, for discriminating MCI from AD. Our method exhibited a superior classification performance compared with single modal approaches and yielded parallel accuracy to previous multimodal classification studies using MRI and FDG-PET.

Topology of brainstem lesions associated with subjective visual vertical tilt.

OBJECTIVE: We aimed to determine the topology of anatomical pathways for verticality perception in the brainstem. METHODS: We measured the subjective visual vertical (SVV) in 82 patients with acute unilateral infarction of the brainstem alone. The topology of the brainstem lesions responsible for pathologic SVV tilt were determined using MRI-based voxel-wise lesion-behavior mapping, from which probabilistic lesion maps were constructed. RESULTS: Fifty percent of patients (41/82) with acute unilateral brainstem infarcts had abnormal SVV tilt, of which 76% (31/41) had ipsiversive tilt and 24% (10/41) had contraversive tilt. Patients with contraversive SVV tilt exhibited overlapping lesions of the rostral medial vestibular nucleus, medial longitudinal fasciculus, rostral interstitial medial longitudinal fasciculus, and interstitial nucleus of Cajal. In contrast, patients with ipsiversive SVV tilt and oculomotor disturbances exhibited lesions of the medial and inferior vestibular nuclei in the caudal medulla, while those with isolated vertical perceptual changes had injury to the medial side of the medial lemniscus. CONCLUSIONS: Our findings provide evidence of a pathway transmitting ipsiversive otolithic signals that bypass the oculomotor system at the medial side of the medial lemniscus, called the ipsilateral vestibulothalamic tract.

Homonymous Visual Field Loss without Structural Lesion on Magnetic Resonance Imaging: Documented with Positron Emission Tomography and Diffusion Tensor Imaging.

The authors describe a 35-year-old man suffering from homonymous hemianopia after head trauma 4 years before but with negative magnetic resonance imaging (MRI) findings. Brain fluorine-18 fluorodeoxyglucose positron emission tomography (18FDG-PET) showed hypometabolism at the unilateral occipital lobe and crossed cerebellar hemisphere, and diffusion tensor imaging (DTI) revealed that the ipsilateral optic radiations were completely interrupted. The crossed cerebellar diaschisis (CCD) observed in the chronic stage of brain damage was caused by cerebellar suppression of the cerebral blood flow due to an involvement of the corticopontocerebellar tract. PET and DTI provide objective means for determining the relationship of functional deficits to head trauma, even in cases where the injury was sustained years prior to the evaluation.

Fully-automated approach to hippocampus segmentation using a graph-cuts algorithm combined with atlas-based segmentation and morphological opening.

The hippocampus has been known to be an important structure as a biomarker for Alzheimer's disease (AD) and other neurological and psychiatric diseases. However, it requires accurate, robust and reproducible delineation of hippocampal structures. In this study, an automated hippocampal segmentation method based on a graph-cuts algorithm combined with atlas-based segmentation and morphological opening was proposed. First of all, the atlas-based segmentation was applied to define initial hippocampal region for a priori information on graph-cuts. The definition of initial seeds was further elaborated by incorporating estimation of partial volume probabilities at each voxel. Finally, morphological opening was applied to reduce false positive of the result processed by graph-cuts. In the experiments with twenty-seven healthy normal subjects, the proposed method showed more reliable results (similarity index=0.81±0.03) than the conventional atlas-based segmentation method (0.72±0.04). Also as for segmentation accuracy which is measured in terms of the ratios of false positive and false negative, the proposed method (precision=0.76±0.04, recall=0.86±0.05) produced lower ratios than the conventional methods (0.73±0.05, 0.72±0.06) demonstrating its plausibility for accurate, robust and reliable segmentation of hippocampus.

Pathogenesis of cerebral microbleeds: In vivo imaging of amyloid and subcortical ischemic small vessel disease in 226 individuals with cognitive impairment.

OBJECTIVE: Cerebral microbleeds (CMBs) are a neuroimaging marker of small vessel disease (SVD) with relevance for understanding disease mechanisms in cerebrovascular disease, cognitive impairment, and normal aging. It is hypothesized that lobar CMBs are due to cerebral amyloid angiopathy (CAA) and deep CMBs are due to subcortical ischemic SVD. We tested this hypothesis using structural magnetic resonance imaging (MRI) markers of subcortical SVD and in vivo imaging of amyloid in patients with cognitive impairment. METHODS: We included 226 patients: 89 with Alzheimer disease-related cognitive impairment (ADCI) and 137 with subcortical vascular cognitive impairment (SVCI). All subjects underwent amyloid imaging with [(11) C] Pittsburgh compound B (PiB) positron emission tomography, and MRI to detect CMBs and markers of subcortical SVD, including the volume of white matter hyperintensities (WMH) and the number of lacunes. RESULTS: Parietal and occipital lobar CMBs counts were higher in PiB(+) ADCI with moderate WMH than PiB(+) ADCI with minimal WMH, whereas PiB(-) patients with SVCI (ie, "pure" SVCI) showed both lobar and deep CMBs. In multivariate analyses of the whole cohort, WMH volume and lacuna counts were positively associated with both lobar and deep CMBs, whereas amyloid burden (PiB) was only associated with lobar CMBs. There was an interaction between lacuna burden and PiB retention on lobar (but not deep) CMBs (p<0.001). INTERPRETATION: Our findings suggest that although deep CMBs are mainly linked to subcortical SVD, both subcortical SVD and amyloid-related pathologies (eg, CAA) contribute to the pathogenesis of lobar CMBs, at least in subjects with mixed lobar and deep CMBs. Furthermore, subcortical SVD and amyloid-related pathologies interact to increase the risk of lobar CMBs.

Cerebellar atrophy in patients with subcortical-type vascular cognitive impairment.

Recent studies suggest that the role of the cerebellum extends into cognitive regulation and that subcortical vascular dementia (SVaD) can result in cerebellar atrophy. However, there has been no evaluation of the cerebellar volume in the preclinical stage of SVaD. We aimed to compare cerebellar volume among patients with amnestic mild cognitive impairment (aMCI) and subcortical vascular mild cognitive impairment (svMCI) and evaluate which factors could have contributed to the cerebellar volume. Participants were composed of 355 patients with aMCI, svMCI, Alzheimer's disease (AD), and SVaD. Cerebellar volumes were measured using automated methods. A direct comparison of the cerebellar volume in SVaD and AD groups showed that the SVaD group had a statistically smaller cerebellar volume than the AD group. Additionally, the svMCI group had a smaller cerebellar volume than the aMCI group, with the number of lacunes (especially in the supratentorial regions) being associated with cerebellar volume. Cerebellar volumes were associated with some neuropsychological tests, digit span backward and ideomotor apraxia. These findings suggest that cerebellar atrophy may be useful in differentiating subtypes of dementia and the cerebellum plays a potential role in cognition.