Medial temporal lobe atrophy and white matter hyperintensities are associated with mild cognitive deficits in non-disabled elderly people: the LADIS study

Integrating Demographics and Imaging Features for Various Stages of Dementia Classification: Feed Forward Neural Network Multi-Class Approach

BACKGROUND

MRI magnetization-prepared rapid acquisition (MPRAGE) is an easily available imaging modality for dementia diagnosis. Previous studies suggested that volumetric analysis plays a crucial role in various stages of dementia classification. In this study, volumetry, radiomics and demographics were integrated as inputs to develop an artificial intelligence model for various stages, including Alzheimer's disease (AD), mild cognitive decline (MCI) and cognitive normal (CN) dementia classifications.

METHOD

The Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset was separated into training and testing groups, and the Open Access Series of Imaging Studies (OASIS) dataset was used as the second testing group. The MRI MPRAGE image was reoriented via statistical parametric mapping (SPM12). Freesurfer was employed for brain segmentation, and 45 regional brain volumes were retrieved. The 3D Slicer software was employed for 107 radiomics feature extractions from within the whole brain. Data on patient demographics were collected from the datasets. The feed-forward neural network (FFNN) and the other most common artificial intelligence algorithms, including support vector machine (SVM), ensemble classifier (EC) and decision tree (DT), were used to build the models using various features.

RESULTS

The integration of brain regional volumes, radiomics and patient demographics attained the highest overall accuracy at 76.57% and 73.14% in ADNI and OASIS testing, respectively. The subclass accuracies in MCI, AD and CN were 78.29%, 89.71% and 85.14%, respectively, in ADNI testing, as well as 74.86%, 88% and 83.43% in OASIS testing. Balanced sensitivity and specificity were obtained for all subclass classifications in MCI, AD and CN.

CONCLUSION

The FFNN yielded good overall accuracy for MCI, AD and CN categorization, with balanced subclass accuracy, sensitivity and specificity. The proposed FFNN model is simple, and it may support the triage of patients for further confirmation of the diagnosis.

National and international collaborations to advance research into vascular contributions to cognitive decline

Cerebrovascular disease is the second most common cause of cognitive disorders, usually referred to as vascular contributions to cognitive impairment and dementia (VCID) and makes some contribution to about 70 % of all dementias. Despite its importance, research into VCID has lagged as compared to cognitive impairment due to Alzheimer's disease. There is an increasing appreciation that closing this gap requires large national and international collaborations. This paper highlights 24 notable large-scale national and international efforts to advance research into VCID (MarkVCID, DiverseVCID, DISCOVERY, COMPASS-ND, HBC, RHU SHIVA, UK DRI Vascular Theme, STROKOG, Meta VCI Map, ISGC, ENIGMA-Stroke Recovery, CHARGE, SVDs@target, BRIDGET, CADASIL Consortium, CADREA, AusCADASIL, DPUK, DPAU, STRIVE, HARNESS, FINESSE, VICCCS, VCD-CRE Delphi). These collaborations aim to investigate the effects on cognition from cerebrovascular disease or impaired cerebral blood flow, the mechanisms of action, means of prevention and avenues for treatment. Consensus groups have been developed to harmonise global approaches to VCID, standardise terminology and inform management and treatment, and data sharing is becoming the norm. VCID research is increasingly a global collaborative enterprise which bodes well for rapid advances in this field.

A novel cross-layer dual encoding-shared decoding network framework with spatial self-attention mechanism for hippocampus segmentation

Accurate segmentation of the hippocampus from the brain magnetic resonance images (MRIs) is a crucial task in the neuroimaging research, since its structural integrity is strongly related to several neurodegenerative disorders, such as Alzheimer's disease (AD). Automatic segmentation of the hippocampus structures is challenging due to the small volume, complex shape, low contrast and discontinuous boundaries of hippocampus. Although some methods have been developed for the hippocampus segmentation, most of them paid too much attention to the hippocampus shape and volume instead of considering the spatial information. Additionally, the extracted features are independent of each other, ignoring the correlation between the global and local information. In view of this, here we proposed a novel cross-layer dual Encoding-Shared Decoding network framework with Spatial self-Attention mechanism (called ESDSA) for hippocampus segmentation in human brains. Considering that the hippocampus is a relatively small part in MRI, we introduced the spatial self-attention mechanism in ESDSA to capture the spatial information of hippocampus for improving the segmentation accuracy. We also designed a cross-layer dual encoding-shared decoding network to effectively extract the global information of MRIs and the spatial information of hippocampus. The spatial features of hippocampus and the features extracted from the MRIs were combined to realize the hippocampus segmentation. Results on the baseline T1-weighted structural MRI data show that the performance of our ESDSA is superior to other state-of-the-art methods, and the dice similarity coefficient of ESDSA achieves 89.37%. In addition, the dice similarity coefficient of the Spatial Self-Attention mechanism (SSA) strategy and the dual Encoding-Shared Decoding (ESD) strategy is 9.47%, 5.35% higher than that of the baseline U-net, respectively, indicating that the strategies of SSA and ESD can effectively enhance the segmentation accuracy of human brain hippocampus.

GIRUS-net: A Multimodal Deep Learning Model Identifying Imaging and Genetic Biomarkers Linked to Alzheimer's Disease Severity

We introduce an explainable deep neural architecture that combines brain structure with genetic influence to improve disease severity prediction in Alzheimer's disease. Our framework consists of an encoder, a decoder, and a rank-consistent ordinal regression module. The encoder projects neural imaging and genetics data into a low-dimensional latent space regularized by the decoder. The ordinal regression module guides the feature embedding process to find discriminative patterns representative of disease severity. We also add a learnable dropout layer that learns feature importance and extracts explainable biomarkers from the data. We evaluate our model using structural MRI (sMRI) and Single Nucleotide Polymorphism (SNP) data provided by the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. In 2-class severity classification comparison, our model has a median F-score of 0.86 (baseline median F-score range: 0.57-0.81). In 3-class classification comparison, our model's median F-score is 0.50 (baseline range: 0.17 - 0.41). In 4-class classification comparison, our model's median F-score is 0.40 (baseline range: 0.14 - 0.39). We demonstrate that our model provides improved disease diagnosis alongside sparse and clinically relevant biomarkers.Clinical relevance-This study provides a deep-learning model that can predict Alzheimer's disease severity levels while identifying consistent and clinically relevant biomarkers.

Circulating cardiac biomarkers, structural brain changes, and dementia: Emerging insights and perspectives

Diseases of the heart and brain are strongly linked to each other, and cardiac dysfunction is associated with cognitive decline and dementia. This link between cardiovascular disease and dementia offers opportunities for dementia prevention through prevention and treatment of cardiovascular risk factors and heart disease. Increasing evidence suggests the clinical utility of cardiac biomarkers as risk markers for structural brain changes and cognitive impairment. We propose the hypothesis that structural brain changes are the link between impaired cardiac function, as captured by blood-based cardiac biomarkers, and cognitive impairment. This review provides an overview of the literature and illustrates emerging insights into the association of markers of hemodynamic stress (natriuretic peptides) and markers of myocardial injury (cardiac troponins) with imaging findings of brain damage and cognitive impairment or dementia. Based on these findings, we discuss potential pathophysiological mechanisms underlying the association of cardiac biomarkers with structural brain changes and dementia. We suggest testable hypotheses and a research plan to close the gaps in understanding the mechanisms linking vascular damage and neurodegeneration, and to pave the way for targeted effective interventions for dementia prevention. From a clinical perspective, cardiac biomarkers open the window for early identification of patients at risk of dementia, who represent a target population for preventive interventions targeting modifiable cardiovascular risk factors to avert cognitive decline and dementia.

Association of white matter lesions and brain atrophy with the development of dementia in a community: the Hisayama Study

AIM

To investigate the association of white matter lesions volume (WMLV) levels with dementia risk and the association between dementia risk and the combined measures of WMLV and either total brain atrophy or dementia-related gray matter atrophy in a general older population.

METHODS

One thousand one hundred fifty-eight Japanese dementia-free community-residents aged ≥65 years who underwent brain magnetic resonance imaging were followed for 5.0 years. WMLV were segmented using the Lesion Segmentation Toolbox. Total brain volume (TBV) and regional gray matter volume were estimated by voxel-based morphometry. The WMLV-to-intracranial brain volume ratio (WMLV/ICV) was calculated, and its association with dementia risk was estimated using Cox proportional hazard models. Total brain atrophy, defined as the TBV-to-ICV ratio (TBV/ICV), and dementia-related regional brain atrophy defined based on our previous report were calculated. The association between dementia risk and the combined measures of WMLV/ICV and either total brain atrophy or the number of atrophied regions was also tested.

RESULTS

During the follow-up, 113 participants developed dementia. The risks of dementia increased significantly with higher WMLV/ICV levels. In addition, dementia risk increased additively both in participants with higher WMLV/ICV levels and lower TBV/ICV levels and in those with higher WMLV/ICV levels and a higher number of dementia-related brain regional atrophy.

CONCLUSION

The risk of dementia increased significantly with higher WMLV/ICV levels. An additive increment in dementia risk was observed with higher WMLV/ICV levels and lower TBV/ICV levels or a higher number of dementia-related brain regional atrophy, suggesting the importance of prevention or control of cardiovascular risk factors.

Association of homocysteine-related subcortical brain atrophy with white matter lesion volume and cognition in healthy aging

Homocysteine (Hcy) is a vascular risk factor associated with cognitive impairment and cerebrovascular disease but has also been implicated in Alzheimer's disease (AD). Using multivariate Scaled Subprofile Model (SSM) analysis, we sought to identify a network pattern in structural neuroimaging reflecting the regionally distributed association of plasma Hcy with subcortical gray matter (SGM) volumes and its relation to other health risk factors and cognition in 160 healthy older adults, ages 50-89. We identified an SSM Hcy-SGM pattern that was characterized by bilateral hippocampal and nucleus accumbens volume reductions with relative volume increases in bilateral caudate, pallidum, and putamen. Greater Hcy-SGM pattern expression was associated with greater white matter hyperintensity (WMH) volume, older age, and male sex, but not with other vascular and AD-related risk factors. Mediation analyses revealed that age predicted WMH volume, which predicted Hcy-SGM pattern expression, which, in turn, predicted cognitive processing speed performance. These findings suggest that the multivariate SSM Hcy-SGM pattern may be indicative of cognitive aging, reflecting a potential link between vascular health and cognitive dysfunction in healthy older adults.

Association of Serum GFAP with Functional and Neurocognitive Outcome in Sporadic Small Vessel Disease

Cerebrospinal fluid (CSF) and serum biomarkers are critical for clinical decision making in neurological diseases. In cerebral small vessel disease (CSVD), white matter hyperintensities (WMH) are an important neuroimaging biomarker, but more blood-based biomarkers capturing different aspects of CSVD pathology are needed. In 42 sporadic CSVD patients, we prospectively analysed WMH on magnetic resonance imaging (MRI) and the biomarkers neurofilament light chain (NfL), glial fibrillary acidic protein (GFAP), chitinase3-like protein 1 (CHI3L1), Tau and Aβ1-42 in CSF and NfL and GFAP in serum. GFAP and CHI3L1 expression was studied in post-mortem brain tissue in additional cases. CSVD cases with higher serum NfL and GFAP levels had a higher modified Rankin Scale (mRS) and NIHSS score and lower CSF Aβ1-42 levels, whereas the CSF NfL and CHI3L1 levels were positively correlated with the WMH load. Moreover, the serum GFAP levels significantly correlated with the neurocognitive functions. Pathological analyses in CSVD revealed a high density of GFAP-immunoreactive fibrillary astrocytic processes in the periventricular white matter and clusters of CHI3L1-immunoreactive astrocytes in the basal ganglia and thalamus. Thus, besides NfL, serum GFAP is a highly promising fluid biomarker of sporadic CSVD, because it does not only correlate with the clinical severity but also correlates with the cognitive function in patients.

Prediction of White Matter Hyperintensity in Brain MRI Using Fundus Photographs via Deep Learning

Purpose: We investigated whether a deep learning algorithm applied to retinal fundoscopic images could predict cerebral white matter hyperintensity (WMH), as represented by a modified Fazekas scale (FS), on brain magnetic resonance imaging (MRI). Methods: Participants who had undergone brain MRI and health-screening fundus photography at Hallym University Sacred Heart Hospital between 2010 and 2020 were consecutively included. The subjects were divided based on the presence of WMH, then classified into three groups according to the FS grade (0 vs. 1 vs. 2+) using age matching. Two pre-trained convolutional neural networks were fine-tuned and evaluated for prediction performance using 10-fold cross-validation. Results: A total of 3726 fundus photographs from 1892 subjects were included, of which 905 fundus photographs from 462 subjects were included in the age-matched balanced dataset. In predicting the presence of WMH, the mean area under the receiver operating characteristic curve was 0.736 ± 0.030 for DenseNet-201 and 0.724 ± 0.026 for EfficientNet-B7. For the prediction of FS grade, the mean accuracies reached 41.4 ± 5.7% with DenseNet-201 and 39.6 ± 5.6% with EfficientNet-B7. The deep learning models focused on the macula and retinal vasculature to detect an FS of 2+. Conclusions: Cerebral WMH might be partially predicted by non-invasive fundus photography via deep learning, which may suggest an eye–brain association.

Functional Imaging for Neurodegenerative Diseases

Diagnosis and monitoring of neurodegenerative diseases has changed profoundly over the past twenty years. Biomarkers are now included in most diagnostic procedures as well as in clinical trials. Neuroimaging biomarkers provide access to brain structure and function over the course of neurodegenerative diseases. They have brought new insights into a wide range of neurodegenerative diseases and have made it possible to describe some of the imaging challenges in clinical populations. MRI mainly explores brain structure while molecular imaging, functional MRI and electro- and magnetoencephalography examine brain function. In this paper, we describe and analyse the current and potential contribution of MRI and molecular imaging in the field of neurodegenerative diseases.

Leukoaraiosis and Gray Matter Volume Alteration in Older Adults: The PROOF Study

Background and Purpose: Leukoaraiosis, also called white matter hyperintensities (WMH), is frequently encountered in the brain of older adults. During aging, gray matter structure is also highly affected. WMH or gray matter defects are commonly associated with a higher prevalence of mild cognitive impairment. However, little is known about the relationship between WMH and gray matter. Our aim was thus to explore the relationship between leukoaraiosis severity and gray matter volume in a cohort of healthy older adults.

Methods: Leukoaraiosis was rated in participants from the PROOF cohort using the Fazekas scale. Voxel-based morphometry was performed on brain scans to examine the potential link between WMH and changes of local brain volume. A neuropsychological evaluation including attentional, executive, and memory tests was also performed to explore cognition.

Results: Out of 315 75-year-old subjects, 228 had punctuate foci of leukoaraiosis and 62 had begun the confluence of foci. Leukoaraiosis was associated with a decrease of gray matter in the middle temporal gyrus, in the right medial frontal gyrus, and in the left parahippocampal gyrus. It was also associated with decreased performances in memory recall, executive functioning, and depression.

Conclusion: In a population of healthy older adults, leukoaraiosis was associated with gray matter defects and reduced cognitive performance. Controlling vascular risk factors and detecting early cerebrovascular disease may prevent, at least in part, dementia onset and progression.

Quantitative Gradient Echo MRI Identifies Dark Matter as a New Imaging Biomarker of Neurodegeneration that Precedes Tisssue Atrophy in Early Alzheimer's Disease

BACKGROUND

Currently, brain tissue atrophy serves as an in vivo MRI biomarker of neurodegeneration in Alzheimer's disease (AD). However, postmortem histopathological studies show that neuronal loss in AD exceeds volumetric loss of tissue and that loss of memory in AD begins when neurons and synapses are lost. Therefore, in vivo detection of neuronal loss prior to detectable atrophy in MRI is essential for early AD diagnosis.

OBJECTIVE

To apply a recently developed quantitative Gradient Recalled Echo (qGRE) MRI technique for in vivo evaluation of neuronal loss in human hippocampus.

METHODS

Seventy participants were recruited from the Knight Alzheimer Disease Research Center, representing three groups: Healthy controls [Clinical Dementia Rating® (CDR®) = 0, amyloid β (Aβ)-negative, n = 34]; Preclinical AD (CDR = 0, Aβ-positive, n = 19); and mild AD (CDR = 0.5 or 1, Aβ-positive, n = 17).

RESULTS

In hippocampal tissue, qGRE identified two types of regions: one, practically devoid of neurons, we designate as "Dark Matter", and the other, with relatively preserved neurons, "Viable Tissue". Data showed a greater loss of neurons than defined by atrophy in the mild AD group compared with the healthy control group; neuronal loss ranged between 31% and 43%, while volume loss ranged only between 10% and 19%. The concept of Dark Matter was confirmed with histopathological study of one participant who underwent in vivo qGRE 14 months prior to expiration.

CONCLUSION

In vivo qGRE method identifies neuronal loss that is associated with impaired AD-related cognition but is not recognized by MRI measurements of tissue atrophy, therefore providing new biomarkers for early AD detection.

Total Cerebral Small Vessel Disease Burden on MRI Correlates With Cognitive Impairment in Outpatients With Amnestic Disorders

Objectives: The main markers of cerebral small vessel disease (cSVD) on MRI may be entered into a scoring system, with the total score representing the overall burden of cSVD. An association between total cSVD score and cognitive dysfunction has been reported in several cohorts. The present study aimed to investigate this association in outpatients with amnestic disorders.

Materials and Methods: Outpatients with amnestic complaints in a memory clinic (n = 289) were recruited retrospectively. All the patients had undergone clinical and cognitive evaluation at first presentation. Cognitive function was assessed by Montreal Cognitive Assessment (MoCA) scale. The total cSVD score was based on the following markers on MRI: lacune; white matter hyperintensities, microbleed, and enlarged perivascular spaces. The association between total cSVD score and MoCA score was tested via Spearman's analysis and a linear regression model.

Results: Among the 289 patients, rates for 0–4 cSVD markers respectively ranged from 30.4 to 2.8%. A multiple linear regression model revealed an inverse correlation between the total cSVD score and MoCA score. The association remained significant after adjusting for gender, age, education, levels of medial temporal lobe atrophy, and classical vascular risk factors [β = −0.729, 95% CI (−1.244, −0.213); P = 0.006]. When individual markers were individually analyzed after adjusting for the same factors, only microbleed associated with MoCA score [β = −3.007, 95% CI (−4.533, −1.480), P < 0.001].

Conclusions: A significant association was demonstrated between total cSVD score and cognitive performance in the outpatients with amnestic disorders.

Total Cerebral Small Vessel Disease Burden on MRI Correlates With Medial Temporal Lobe Atrophy and Cognitive Performance in Patients of a Memory Clinic

Background: Cerebral small vessel disease (cSVD) and neurodegeneration are the two main causes of dementia and are considered distinct pathological processes, while studies have shown overlaps and interactions between the two pathological pathways. Medial temporal atrophy (MTA) is considered a classic marker of neurodegeneration. We aimed to investigate the relationship of total cSVD burden and MTA on MRI using a total cSVD score and to explore the impact of the two MRI features on cognition. Methods: Patients in a memory clinic were enrolled, who underwent brain MRI scan and cognitive evaluation within 7 days after the first visit. MTA and total cSVD score were rated using validated visual scales. Cognitive function was assessed by using Mini-Mental State Examination (MMSE) and Montreal Cognitive Assessment (MoCA) scales. Spearman's correlation and regression models were used to test (i) the association between MTA and total cSVD score as well as each cSVD marker and (ii) the correlation of the MRI features and cognitive status. Results: A total of 312 patients were finally enrolled, with a median age of 75.0 (66.0-80.0) years and 40.7% (127/312) males. All of them finished MRI and MMSE, and 293 subjects finished MoCA. Of note, 71.8% (224/312) of the patients had at least one of the cSVD markers, and 48.7% (152/312) of them had moderate-severe MTA. The total cSVD score was independently associated with MTA levels, after adjusting for age, gender, years of education, and other vascular risk factors (OR 1.191, 95% CI 1.071-1.324, P = 0.001). In regard to individual markers, a significant association existed only between white matter hyperintensities and MTA after adjusting for the factors mentioned above (OR 1.338, 95% CI 1.050-1.704, P = 0.018). Both MTA and total cSVD score were independent risk factors for MMSE ≤ 26 (MTA: OR 1.877, 95% CI 1.407-2.503, P < 0.001; total cSVD score: OR 1.474, 95% CI 1.132-1.921, P = 0.004), and MoCA < 26 (MTA: OR 1.629, 95% CI 1.112-2.388, P = 0.012; total cSVD score: OR 1.520, 95% CI 1.068-2.162, P = 0.020). Among all the cSVD markers, microbleed was found significantly associated with MMSE ≤ 26, while no marker was demonstrated a relationship with MoCA < 26. Conclusion: Cerebral small vessel disease was related to MTA in patients of a memory clinic, and both the MRI features had a significant association with cognitive impairment.

Role of White Matter Hyperintensities and Related Risk Factors in Vascular Cognitive Impairment: A Review

White matter hyperintensities (WMHs) of presumed vascular origin are one of the imaging markers of cerebral small-vessel disease, which is prevalent in older individuals and closely associated with the occurrence and development of cognitive impairment. The heterogeneous nature of the imaging manifestations of WMHs creates difficulties for early detection and diagnosis of vascular cognitive impairment (VCI) associated with WMHs. Because the underlying pathological processes and biomarkers of WMHs and their development in cognitive impairment remain uncertain, progress in prevention and treatment is lagging. For this reason, this paper reviews the status of research on the features of WMHs related to VCI, as well as mediators associated with both WMHs and VCI, and summarizes potential treatment strategies for the prevention and intervention in WMHs associated with VCI.

Bridging Scales in Alzheimer's Disease: Biological Framework for Brain Simulation With The Virtual Brain

Despite the acceleration of knowledge and data accumulation in neuroscience over the last years, the highly prevalent neurodegenerative disease of AD remains a growing problem. Alzheimer's Disease (AD) is the most common cause of dementia and represents the most prevalent neurodegenerative disease. For AD, disease-modifying treatments are presently lacking, and the understanding of disease mechanisms continues to be incomplete. In the present review, we discuss candidate contributing factors leading to AD, and evaluate novel computational brain simulation methods to further disentangle their potential roles. We first present an overview of existing computational models for AD that aim to provide a mechanistic understanding of the disease. Next, we outline the potential to link molecular aspects of neurodegeneration in AD with large-scale brain network modeling using The Virtual Brain (www.thevirtualbrain.org), an open-source, multiscale, whole-brain simulation neuroinformatics platform. Finally, we discuss how this methodological approach may contribute to the understanding, improved diagnostics, and treatment optimization of AD.

Distinct Hippocampal Subfields Atrophy in Older People With Vascular Brain Injuries

BACKGROUND AND PURPOSE

Many neurological or psychiatric diseases affect the hippocampus during aging. The study of hippocampal regional vulnerability may provide important insights into the pathophysiological mechanisms underlying these processes; however, little is known about the specific impact of vascular brain damage on hippocampal subfields atrophy.

METHODS

To analyze the effect of vascular injuries independently of other pathological conditions, we studied a population-based cohort of nondemented older adults, after the exclusion of people who were diagnosed with neurodegenerative diseases during the 14-year clinical follow-up period. Using an automated segmentation pipeline, 1.5T-magnetic resonance imaging at inclusion and 4 years later were assessed to measure both white matter hyperintensities and hippocampal subfields volume. Annualized rates of white matter hyperintensity progression and annualized rates of hippocampal subfields atrophy were then estimated in each participant.

RESULTS

We included 249 participants in our analyses (58% women, mean age 71.8, median Mini-Mental State Evaluation 29). The volume of the subiculum at baseline was the only hippocampal subfield volume associated with total, deep/subcortical, and periventricular white matter hyperintensity volumes, independently of demographic variables and vascular risk factors (β=-0.17, P=0.011; β=-0.25, P=0.020 and β=-0.14, P=0.029, respectively). In longitudinal measures, the annualized rate of subiculum atrophy was significantly higher in people with the highest rate of deep/subcortical white matter hyperintensity progression, independently of confounding factors (β=-0.32, P=0.014).

CONCLUSIONS

These cross-sectional and longitudinal findings highlight the links between vascular brain injuries and a differential vulnerability of the subiculum within the hippocampal loop, unbiased of the effect of neurodegenerative diseases, and particularly when vascular injuries affect deep/subcortical structures.

Regional Quantitative Magnetic Resonance Imaging Data Improve Screening Accuracy of Subjective Memory Complaints and Informant Reports of Cognitive Decline

OBJECTIVE

We investigated whether the addition of Alzheimer's disease-signature region cortical thickness (AD-Ct) and hippocampal volume (Hv) obtained from brain MRI to subjective memory complaints and informant-reports of cognitive decline enhances the screening accuracy for cognitive disorders in a memory clinic setting.

METHODS

120 participants (40 cognitively normal, 40 MCI, 40 dementia) underwent clinical evaluation, neuropsychological assessment, and brain MRI. The Subjective Memory Complaints Questionnaire (SMCQ) and Seoul Informant-Report Questionnaire for Dementia (SIRQD) were applied to assess subjective memory complaints and informant-reports of cognitive decline respectively. Logistic regression and ROC curve analyses were conducted to compare the screening abilities of SMCQ+SIRQD, SMCQ+SIRQD+Hv, and SMCQ+SIRQD+AD-Ct models for cognitive disorders.

RESULTS

SMCQ+SIRQD+Hv model indicated better screening accuracy for MCI and overall cognitive disorder (CDall) than SMCQ+ SIRQD model. SMCQ+SIRQD+AD-Ct model had superior screening accuracy for dementia in comparison to SMCQ+SIRQD model. ROC curve analyses revealed that SMCQ+SIRQD+Hv model had the greatest area under the curve (AUC) for screening MCI and CDall (AUC: 0.941 and 0.957), while SMCQ+SIRQD+AD-Ct model had the greatest AUC for screening dementia (AUC: 0.966).

CONCLUSION

Our results suggest that the addition of regional quantitative MRI data enhances the screening ability of subjective memory complaints and informant-reports of cognitive decline for MCI and dementia.

Automated White Matter Hyperintensity Segmentation Using Bayesian Model Selection: Assessment and Correlations with Cognitive Change

Accurate, automated white matter hyperintensity (WMH) segmentations are needed for large-scale studies to understand contributions of WMH to neurological diseases. We evaluated Bayesian Model Selection (BaMoS), a hierarchical fully-unsupervised model selection framework for WMH segmentation. We compared BaMoS segmentations to semi-automated segmentations, and assessed whether they predicted longitudinal cognitive change in control, early Mild Cognitive Impairment (EMCI), late Mild Cognitive Impairment (LMCI), subjective/significant memory concern (SMC) and Alzheimer's (AD) participants. Data were downloaded from the Alzheimer's disease Neuroimaging Initiative (ADNI). Magnetic resonance images from 30 control and 30 AD participants were selected to incorporate multiple scanners, and were semi-automatically segmented by 4 raters and BaMoS. Segmentations were assessed using volume correlation, Dice score, and other spatial metrics. Linear mixed-effect models were fitted to 180 control, 107 SMC, 320 EMCI, 171 LMCI and 151 AD participants separately in each group, with the outcomes being cognitive change (e.g. mini-mental state examination; MMSE), and BaMoS WMH, age, sex, race and education used as predictors. There was a high level of agreement between BaMoS' WMH segmentation volumes and a consensus of rater segmentations, with a median Dice score of 0.74 and correlation coefficient of 0.96. BaMoS WMH predicted cognitive change in: control, EMCI, and SMC groups using MMSE; LMCI using clinical dementia rating scale; and EMCI using Alzheimer's disease assessment scale-cognitive subscale (p < 0.05, all tests). BaMoS compares well to semi-automated segmentation, is robust to different WMH loads and scanners, and can generate volumes which predict decline. BaMoS can be applicable to further large-scale studies.

White Matter Hyperintensities and Hippocampal Atrophy in Relation to Cognition: The 90+ Study

OBJECTIVES

To study the interactive effect of white matter hyperintensities (WMH) and hippocampal atrophy on cognition in the oldest old.

DESIGN

Ongoing longitudinal study.

SETTING

In Southern California, brain magnetic resonance imaging (MRI) scans were conducted between May 2014 and December 2017.

PARTICIPANTS

Individuals from The 90+ Study with a valid brain MRI scan (N = 141; 94 cognitively normal and 47 with cognitive impairment).

MEASUREMENTS

Cognitive testing was performed every 6 months with a mean follow-up of 2 years and included these tests: Mini-Mental State Examination (MMSE), modified MMSE (3MS), California Verbal Learning Test (CVLT) immediate recall over four trials and delayed recall, Digit Span Backward, Animal Fluency, and Trail Making Test (TMT) A, B, and C. We used one linear mixed model for each cognitive test to study the baseline and longitudinal association of WMH and hippocampal volume (HV) with cognition. Models were adjusted for age, sex, and education.

RESULTS

Mean age was 94.3 years (standard deviation [SD] = 3.2 y). At baseline, higher WMH volumes were associated with worse scores on the 3MS, CVLT immediate and delayed recall, and TMT B. Lower HVs were associated with worse baseline scores on all cognitive tests, except for the Digit Span Backward. Longitudinally, higher WMH and lower HVs were associated with faster decline in the 3MS and MMSE, and lower HV was also associated with faster decline in the CVLT immediate recall. No association was observed between WMH and HV and no interaction between WMH and HV in their association with baseline cognition or cognitive decline.

CONCLUSION

We show that WMH and hippocampal atrophy have an independent, negative effect on cognition that make these biomarkers relevant to evaluate in the diagnostic work-up of the oldest-old individuals with cognitive complaints. However, the predictive value of WMH for cognitive decline seems to be less evident in the oldest-old compared with a younger group of older adults. J Am Geriatr Soc 67:1827-1834, 2019.

Cerebral small vessel disease: from a focal to a global perspective

Cerebral small vessel disease (SVD) is commonly observed on neuroimaging among elderly individuals and is recognized as a major vascular contributor to dementia, cognitive decline, gait impairment, mood disturbance and stroke. However, clinical symptoms are often highly inconsistent in nature and severity among patients with similar degrees of SVD on brain imaging. Here, we provide a new framework based on new advances in structural and functional neuroimaging that aims to explain the remarkable clinical variation in SVD. First, we discuss the heterogeneous pathology present in SVD lesions despite an identical appearance on imaging and the perilesional and remote effects of these lesions. We review effects of SVD on structural and functional connectivity in the brain, and we discuss how network disruption by SVD can lead to clinical deficits. We address reserve and compensatory mechanisms in SVD and discuss the part played by other age-related pathologies. Finally, we conclude that SVD should be considered a global rather than a focal disease, as the classically recognized focal lesions affect remote brain structures and structural and functional network connections. The large variability in clinical symptoms among patients with SVD can probably be understood by taking into account the heterogeneity of SVD lesions, the effects of SVD beyond the focal lesions, the contribution of neurodegenerative pathologies other than SVD, and the interaction with reserve mechanisms and compensatory mechanisms.

Memory decline in elderly with cerebral small vessel disease explained by temporal interactions between white matter hyperintensities and hippocampal atrophy

White matter hyperintensities (WMH) constitute the visible spectrum of cerebral small vessel disease (SVD) markers and are associated with cognitive decline, although they do not fully account for memory decline observed in individuals with SVD. We hypothesize that WMH might exert their effect on memory decline indirectly by affecting remote brain structures such as the hippocampus. We investigated the temporal interactions between WMH, hippocampal atrophy and memory decline in older adults with SVD. Five hundred and three participants of the RUNDMC study underwent neuroimaging and cognitive assessments up to 3 times over 8.7 years. We assessed WMH volumes semi-automatically and calculated hippocampal volumes (HV) using FreeSurfer. We used linear mixed effects models and causal mediation analyses to assess both interaction and mediation effects of hippocampal atrophy in the associations between WMH and memory decline, separately for working memory (WM) and episodic memory (EM). Linear mixed effect models revealed that the interaction between WMH and hippocampal volumes explained memory decline (WM: β = .067; 95%CI[.024-0.111]; p < .01; EM: β = .061; 95%CI[.025-.098]; p < .01), with better model fit when the WMH*HV interaction term was added to the model, for both WM (likelihood ratio test, χ² [1] = 9.3, p < .01) and for EM (likelihood ratio test, χ² [1] = 10.7, p < .01). Mediation models showed that both baseline WMH volume (β = -.170; p = .001) and hippocampal atrophy (β = 0.126; p = .009) were independently related to EM decline, but the effect of baseline WMH on EM decline was not mediated by hippocampal atrophy (p value indirect effect: 0.572). Memory decline in elderly with SVD was best explained by the interaction of WMH and hippocampal volumes. The relationship between WMH and memory was not causally mediated by hippocampal atrophy, suggesting that memory decline during aging is a heterogeneous condition in which different pathologies contribute to the memory decline observed in elderly with SVD.

Modified Visual Magnetic Resonance Rating Scale for Evaluation of Patients with Forgetfulness

BACKGROUND

As cognitive impairment increases with age, sulcal atrophy (SA) and the enlargement of the ventricles also increase. Considering the measurements on the previously proposed visual scales, a new scale is proposed in this study that allows us to evaluate the atrophy, white matter hyperintensities (WMHs), basal ganglia infarct (BGI), and infratentorial infarct (ITI) together. Our aim of this study is to propose a practical and standardized MRI for the clinicians to be used in daily practice.

METHODS

A total of 97 patients older than 60 years and diagnosed with depression or Alzheimer's disease (AD) are included. Cranial MRI, Mini Mental State Examination (MMSE), detailed neuropsychometric tests, and depression scales are applied to all patients. The SA, ventricular atrophy (VA), medial temporal lobe atrophy (MTA), periventricular WMH (PWMH), subcortical WMH (SCWMH), BGI, and ITI are scored according to the scale. The total score is also recorded.

RESULTS

The average age of the patients was 74.53, and the mean MMSE score was 22.7 in the degenerative group and 27.8 in the non-degenerative group. Among the patients, 50 were diagnosed with AD. All parameters significantly increased with age. In the degenerative group, SA, VA, MTA, PWMH, SCWMH, and total scores were found to be significantly higher. Sensitivities of VA, PWMH, SCWMH, and total scores, as well as both sensitivity and specificities of MTA score, were observed to be high. When they were combined, sensitivities and specificities were found to be high.

CONCLUSION

The scale is observed to be predictive in discriminating degenerative and non-degenerative processes. This discrimination is important, particularly in depressive patients complaining of forgetfulness.

Fluid and imaging biomarkers for Alzheimer's disease: Where we stand and where to head to

There is increasing evidence that a number of potentially informative biomarkers for Alzheimer disease (AD) can improve the accuracy of diagnosing this form of dementia, especially when used as a panel of diagnostic assays and interpreted in the context of neuroimaging and clinical data. Moreover, by combining the power of CSF biomarkers with neuroimaging techniques to visualize Aβ deposits (or neurodegenerative lesions), it might be possible to better identify individuals at greatest risk for developing MCI and converting to AD. The objective of this article was to review recent progress in selected imaging and chemical biomarkers for prediction, early diagnosis and progression of AD. We present our view point of a scenario that places CSF and imaging markers on the verge of general utility based on accuracy levels that already match (or even surpass) current clinical precision.

Validation of the Total Cerebrovascular Disease Burden Scale in a Community Sample

BACKGROUND

A total cerebrovascular disease (CeVD) burden scale was previously constructed and an inverse association of CeVD burden and cognition was found. However, the generalizability of the CeVD scale has not been examined.

OBJECTIVE

The objective was to validate the previously constructed total CeVD burden scale by establishing its association with cognitive function and dementia diagnosis in a community sample.

METHODS

Eligible participants were assessed on an extensive neuropsychological battery and underwent MRI scans. The total CeVD scale, comprising markers of both small- and large-vessel diseases, was derived according to previously described criteria. Association of total CeVD burden with global and domain-based cognitive performance and dementia diagnostic utility of the scale was established.

RESULTS

A total of 863 participants were included in the analysis. A stepwise association of CeVD burden score with global and domain-specific cognitive function was found. Per score increase on the total CeVD burden scale was associated with 3.6 (95% CI = 2.1-6.4) times higher odds of dementia compared to dementia-free.

DISCUSSION

The total CeVD burden scale is associated with cognition and dementia in a community sample. Longitudinal studies are required to establish the predictive ability of this scale.

Diffusion tensor tractography in cerebral small vessel disease: correlation with cognitive function

Background Patients with cerebral small vessel disease may suffer from varying levels of cognitive deficit and may progress on to vascular dementia. The extent of involvement, as seen on conventional magnetic resonance (MR) measures, correlates poorly with the level of cognitive decline. The purpose of this study was to investigate the utility of diffusion tensor imaging (DTI) as a marker for white matter damage in small vessel disease and to assess its correlation with cognitive function. Methods Thirty consecutive patients with cerebral small vessel disease underwent conventional MR imaging, DTI, and neuropsychological assessment. Results On tractographic analysis, fractional anisotropy was significantly reduced while mean diffusivity significantly increased in several white matter tracts. The alteration in DTI indices correlated well with cognitive function. No significant correlation was identified between T2 lesion load and cognitive performance. Conclusions Tractographic analysis of white matter integrity is a useful measure of disease severity and correlates well with cognitive function. It may have a significant potential in monitoring disease progression and may serve as a surrogate marker for treatment trials.

Magnetic resonance imaging and positron emission tomography in the diagnosis of neurodegenerative dementias

Neuroimaging, both with magnetic resonance imaging (MRI) and positron emission tomography (PET), has gained a pivotal role in the diagnosis of primary neurodegenerative diseases. These two techniques are used as biomarkers of both pathology and progression of Alzheimer's disease (AD) and to differentiate AD from other neurodegenerative diseases. MRI is able to identify structural changes including patterns of atrophy characterizing neurodegenerative diseases, and to distinguish these from other causes of cognitive impairment, e.g. infarcts, space-occupying lesions and hydrocephalus. PET is widely used to identify regional patterns of glucose utilization, since distinct patterns of distribution of cerebral glucose metabolism are related to different subtypes of neurodegenerative dementia. The use of PET in mild cognitive impairment, though controversial, is deemed helpful for predicting conversion to dementia and the dementia clinical subtype. Recently, new radiopharmaceuticals for the in vivo imaging of amyloid burden have been licensed and more tracers are being developed for the assessment of tauopathies and inflammatory processes, which may underlie the onset of the amyloid cascade. At present, the cerebral amyloid burden, imaged with PET, may help to exclude the presence of AD as well as forecast its possible onset. Finally PET imaging may be particularly useful in ongoing clinical trials for the development of dementia treatments. In the near future, the use of the above methods, in accordance with specific guidelines, along with the use of effective treatments will likely lead to more timely and successful treatment of neurodegenerative dementias.

Impact of cerebral white matter changes on functionality in older adults: An overview of the LADIS Study results and future directions

The evidence on the clinical significance of cerebral white matter changes (WMC) has mounted over the past few decades. WMC are recognized as one of the neuroimaging features of cerebral small vessel disease, and are associated with various disturbances and a poor prognosis. The Leukoaraiosis and Disability (LADIS) Study has contributed substantially to this body of knowledge. LADIS is a European multicenter collaboration aimed at assessing the role of WMC as an independent predictor of the transition to disability in initially non-disabled patients aged 65-84 years. Besides the demonstration that severe WMC cause a more than double risk of transition from an autonomous to a dependent status after 3 years of follow-up, the LADIS Study has also provided evidence on the role of WMC in relation to the decline of cognitive and motor performances, depressive symptoms associated with aging and cerebrovascular diseases, the presence of urinary disturbances, and various neurological abnormalities. The possible role of other lesions (lacunar infarcts, cerebral atrophy, corpus callosum morphology) and microstructural abnormalities (diffusion-weighted imaging changes in normal appearing brain tissue and in WMC) has also been investigated. In the present article, we review the main results of the LADIS Study and offer some considerations for future developments in the field, paying attention to the potential use of WMC progression as a surrogate marker in intervention trials in cerebral small vessel diseases. We also discuss some therapeutic perspectives regarding the beneficial impact of physical activity on the risk of vascular cognitive impairment in patients with WMC.

Brain atrophy in Alzheimer's Disease and aging

Thanks to its safety and accessibility, magnetic resonance imaging (MRI) is extensively used in clinical routine and research field, largely contributing to our understanding of the pathophysiology of neurodegenerative disorders such as Alzheimer's disease (AD). This review aims to provide a comprehensive overview of the main findings in AD and normal aging over the past twenty years, focusing on the patterns of gray and white matter changes assessed in vivo using MRI. Major progresses in the field concern the segmentation of the hippocampus with novel manual and automatic segmentation approaches, which might soon enable to assess also hippocampal subfields. Advancements in quantification of hippocampal volumetry might pave the way to its broader use as outcome marker in AD clinical trials. Patterns of cortical atrophy have been shown to accurately track disease progression and seem promising in distinguishing among AD subtypes. Disease progression has also been associated with changes in white matter tracts. Recent studies have investigated two areas often overlooked in AD, such as the striatum and basal forebrain, reporting significant atrophy, although the impact of these changes on cognition is still unclear. Future integration of different MRI modalities may further advance the field by providing more powerful biomarkers of disease onset and progression.

Correlating Cognitive Decline with White Matter Lesion and Brain Atrophy Magnetic Resonance Imaging Measurements in Alzheimer's Disease

BACKGROUND

Vascular risk factors are increasingly recognized as risks factors for Alzheimer's disease (AD) and early conversion from mild cognitive impairment (MCI) to dementia. While neuroimaging research in AD has focused on brain atrophy, metabolic function, or amyloid deposition, little attention has been paid to the effect of cerebrovascular disease to cognitive decline.

OBJECTIVE

To investigate the correlation of brain atrophy and white matter lesions with cognitive decline in AD, MCI, and control subjects.

METHODS

Patients with AD and MCI, and healthy subjects were included in this study. Subjects had a baseline MRI scan, and baseline and follow-up neuropsychological battery (CERAD). Regional volumes were measured, and white matter lesion segmentation was performed. Correlations between rate of CERAD score decline and white matter lesion load and brain structure volume were evaluated. In addition, voxel-based correlations between baseline CERAD scores and atrophy and white matter lesion measures were computed.

RESULTS

CERAD rate of decline was most significantly associated with lesion loads located in the fornices. Several temporal lobe ROI volumes were significantly associated with CERAD decline. Voxel-based analysis demonstrated strong correlation between baseline CERAD scores and atrophy measures in the anterior temporal lobes. Correlation of baseline CERAD scores with white matter lesion volumes achieved significance in multilobar subcortical white matter.

CONCLUSION

Both baseline and declines in CERAD scores correlate with white matter lesion load and gray matter atrophy. Results of this study highlight the dominant effect of volume loss, and underscore the importance of small vessel disease as a contributor to cognitive decline in the elderly.

White matter hyperintensities and imaging patterns of brain ageing in the general population

White matter hyperintensities are associated with increased risk of dementia and cognitive decline. The current study investigates the relationship between white matter hyperintensities burden and patterns of brain atrophy associated with brain ageing and Alzheimer's disease in a large populatison-based sample (n = 2367) encompassing a wide age range (20-90 years), from the Study of Health in Pomerania. We quantified white matter hyperintensities using automated segmentation and summarized atrophy patterns using machine learning methods resulting in two indices: the SPARE-BA index (capturing age-related brain atrophy), and the SPARE-AD index (previously developed to capture patterns of atrophy found in patients with Alzheimer's disease). A characteristic pattern of age-related accumulation of white matter hyperintensities in both periventricular and deep white matter areas was found. Individuals with high white matter hyperintensities burden showed significantly (P < 0.0001) lower SPARE-BA and higher SPARE-AD values compared to those with low white matter hyperintensities burden, indicating that the former had more patterns of atrophy in brain regions typically affected by ageing and Alzheimer's disease dementia. To investigate a possibly causal role of white matter hyperintensities, structural equation modelling was used to quantify the effect of Framingham cardiovascular disease risk score and white matter hyperintensities burden on SPARE-BA, revealing a statistically significant (P < 0.0001) causal relationship between them. Structural equation modelling showed that the age effect on SPARE-BA was mediated by white matter hyperintensities and cardiovascular risk score each explaining 10.4% and 21.6% of the variance, respectively. The direct age effect explained 70.2% of the SPARE-BA variance. Only white matter hyperintensities significantly mediated the age effect on SPARE-AD explaining 32.8% of the variance. The direct age effect explained 66.0% of the SPARE-AD variance. Multivariable regression showed significant relationship between white matter hyperintensities volume and hypertension (P = 0.001), diabetes mellitus (P = 0.023), smoking (P = 0.002) and education level (P = 0.003). The only significant association with cognitive tests was with the immediate recall of the California verbal and learning memory test. No significant association was present with the APOE genotype. These results support the hypothesis that white matter hyperintensities contribute to patterns of brain atrophy found in beyond-normal brain ageing in the general population. White matter hyperintensities also contribute to brain atrophy patterns in regions related to Alzheimer's disease dementia, in agreement with their known additive role to the likelihood of dementia. Preventive strategies reducing the odds to develop cardiovascular disease and white matter hyperintensities could decrease the incidence or delay the onset of dementia.

The overlap between vascular disease and Alzheimer's disease--lessons from pathology

Recent epidemiological and clinico-pathological data indicate considerable overlap between cerebrovascular disease (CVD) and Alzheimer's disease (AD) and suggest additive or synergistic effects of both pathologies on cognitive decline. The most frequent vascular pathologies in the aging brain and in AD are cerebral amyloid angiopathy and small vessel disease. Up to 84% of aged subjects show morphological substrates of CVD in addition to AD pathology. AD brains with minor CVD, similar to pure vascular dementia, show subcortical vascular lesions in about two-thirds, while in mixed type dementia (AD plus vascular dementia), multiple larger infarcts are more frequent. Small infarcts in patients with full-blown AD have no impact on cognitive decline but are overwhelmed by the severity of Alzheimer pathology, while in early stages of AD, cerebrovascular lesions may influence and promote cognitive impairment, lowering the threshold for clinically overt dementia. Further studies are warranted to elucidate the many hitherto unanswered questions regarding the overlap between CVD and AD as well as the impact of both CVD and AD pathologies on the development and progression of dementia.

Behavioral and neuropathological consequences of transient global ischemia in APP/PS1 Alzheimer model mice

Alzheimer's disease (AD) typically manifests in elderly people with several co-morbidities, especially cardiovascular, whereas transgenic mouse models of this disease usually employ middle-aged animals that have a good general health status. To assess the combined effect of compromised cerebral blood circulation and brain amyloid pathology we induced transient (17min) global ischemia (TGI) to young adult APPswe/PS1dE9 (APdE9) mice modeling AD amyloid pathology, and assessed the outcome on behavior two weeks and on histopathology five weeks after the ischemic insult. Ischemic injury resulted in reduced motor coordination and impaired spatial learning and memory. Neuropathological examination revealed circumscribed sites of neuronal loss in ischemic mice, including hippocampal CA2, lateral CA3 and medial CA1 pyramidal cell layer, and superficial layers of cortical patches. Notably, Fluoro-Jade staining revealed dying neurons as late as five weeks after the initial insult, and staining for active microglia and astrocytes confirmed the presence of inflammatory reaction. The extent of neuronal loss in CA2 and CA1 correlated significantly with impairment in spatial memory. There was no genotype difference in either behavioral or neuropathological consequences of TGI. However, the post-operative survival of transgenic animals was greatly reduced compared to wild type animals. APdE9 mice at a pre-plaque age appear to be more sensitive than wild-type mice to TGI in terms of post-operative recovery but the surviving APdE9 mice do not display more severe neurological deficits than wild-type mice.

Cognitive function and brain structure in persons with type 2 diabetes mellitus after intensive lowering of blood pressure and lipid levels: a randomized clinical trial

IMPORTANCE

Persons with type 2 diabetes mellitus (T2DM) are at increased risk for decline in cognitive function, reduced brain volume, and increased white matter lesions in the brain. Poor control of blood pressure (BP) and lipid levels are risk factors for T2DM-related cognitive decline, but the effect of intensive treatment on brain function and structure is unknown.

OBJECTIVE

To examine whether intensive therapy for hypertension and combination therapy with a statin plus a fibrate reduces the risk of decline in cognitive function and total brain volume (TBV) in patients with T2DM.

DESIGN, SETTING, AND PARTICIPANTS

A North American multicenter clinical trial including 2977 participants without baseline clinical evidence of cognitive impairment or dementia and with hemoglobin A1c (HbA1c) levels less than 7.5% randomized to a systolic BP goal of less than 120 vs less than 140 mm Hg (n = 1439) or to a fibrate vs placebo in patients with low-density lipoprotein cholesterol levels less than 100 mg/dL (n = 1538). Participants were recruited from August 1, 2003, through October 31, 2005, with the final follow-up visit by June 30, 2009.

MAIN OUTCOME MEASURES

Cognition was assessed at baseline and 20 and 40 months. A subset of 503 participants underwent baseline and 40-month brain magnetic resonance imaging to assess for change in TBV and other structural measures of brain health.

RESULTS

Baseline mean HbA1c level was 8.3%; mean age, 62 years; and mean duration of T2DM, 10 years. At 40 months, no differences in cognitive function were found in the intensive BP-lowering trial or in the fibrate trial. At 40 months, TBV had declined more in the intensive vs standard BP-lowering group (difference, -4.4 [95% CI, -7.8 to -1.1] cm(3); P = .01). Fibrate therapy had no effect on TBV compared with placebo.

CONCLUSIONS AND RELEVANCE

In participants with long-standing T2DM and at high risk for cardiovascular events, intensive BP control and fibrate therapy in the presence of controlled low-density lipoprotein cholesterol levels did not produce a measurable effect on cognitive decline at 40 months of follow-up. Intensive BP control was associated with greater decline in TBV at 40 months relative to standard therapy. TRIAL REGISTRATION clinicaltrials.gov Identifier: NCT00000620.

White matter disease severity of the brain and its association with geriatric syndromes

BACKGROUND AND AIM

White matter disease (WMD) of the brain is considered to be secondary to small vessel ischemia and can be a single unifying risk factor for the development of geriatric syndromes. The aim of our study was to investigate the association of the global and regional severity of WMD in the brain with geriatric syndromes burden.

METHODS

In our retrospective study, consecutive outpatient charts from patients seen between January 2010 and June 2011 at University of Alberta Hospital Seniors Clinic were reviewed. Subjects with brain computed tomography (CT) scans were included in the study. Subjects with incomplete information or with diseases that confounded WMD assessment on CT were excluded. White matter disease was quantified on CT using Wahlund scoring. A multiple linear regression analysis was conducted to determine the association of WMD severity with geriatric syndromes burden after controlling for confounding vascular risk factors.

RESULTS

Of the 505 subjects, 326 (64.6%) were women. Mean age of the study patients was 79.8 years (SD ± 7.04), prevalence of WMD disease was 79.4%, and mean WMD score was 5.1 (SD ± 4.4). In subjects aged < and > 80 years, the mean number of geriatric syndromes was 2.83 (standard error of the mean [SE] 0.08) and 3.22 (SE 0.08), respectively. In the adjusted regression analysis, there was a significant association between WMD severity, globally (regression coefficient (β) = 0.457, SE 0.155; P = 0.003), as well as WMD in specific regions: frontal (P < 0.001), parieto-occipital (P = 0.004), and infratentorial regions (P = 0.04) with geriatric syndromes burden. The association remains even after correcting for age, sex, and all vascular risk factors.

CONCLUSION

In our study, there was a significant association between the severity of global and selected regional WMD of the brain with geriatric syndromes burden, thus raising the possibility of a shared biologic association through vascular pathology of the brain.

Risk factors for the progression of mild cognitive impairment to dementia

The increasing prevalence of cognitive impairment among the older adult population warrants attention to the identification of practices that may minimize the progression of early forms of cognitive impairment, including the transitional stage of mild cognitive impairment (MCI), to permanent stages of dementia. This article identifies both markers of disease progress and risk factors linked to the progression of MCI to dementia. Potentially modifiable risk factors may offer researchers a point of intervention to modify the effect of the risk factor and to minimize the future burden of dementia.

Vascular brain lesions, brain atrophy, and cognitive decline. The Second Manifestations of ARTerial disease--Magnetic Resonance (SMART-MR) study

We examined the association between brain atrophy and vascular brain lesions (i.e., white matter lesions [WMLs] or brain infarcts), alone or in combination, with decline in memory and executive functioning over 4 years of follow-up in 448 patients (57 ± 9.5 years) with symptomatic atherosclerotic disease from the Second Manifestations of ARTerial disease--Magnetic Resonance SMART-MR study. Automated brain segmentation was used to quantify volumes of total brain, ventricles, cortical gray matter, and WMLs on 1.5-T magnetic resonance imaging (MRI). Brain infarcts were rated visually. WML volume was associated with significant decline in z score of executive functioning. No independent associations between MRI measures and memory decline were found. Significant declines in z scores of memory performance and of executive functioning were observed in patients with a combination of severe atrophy (upper quartile) and most vascular brain lesions (upper quartile) compared with those with minimal atrophy (lowest quartile) and fewest vascular brain lesions (lowest quartile). Our findings suggest that in patients with symptomatic atherosclerotic disease, the combination of brain atrophy and WMLs or brain infarcts accelerates cognitive decline over 4 years.

Do cerebral white matter lesions influence the rate of progression from mild cognitive impairment to dementia?

BACKGROUND

Cerebral white matter lesions (WML), evident on CT and MRI brain scans, are histopathologically heterogeneous but associated with vascular risk factors and thought mainly to indicate ischemic damage. There has been disagreement over their clinical prognostic value in predicting conversion from mild cognitive impairment (MCI) to dementia.

METHODS

We scrutinised and rated CT and MRI brain scans for degree of WML in a memory clinic cohort of 129 patients with at least 1 year of follow-up. We examined the relationship between WML severity and time until conversion to dementia for all MCI patients and for amnestic (aMCI) and non-amnestic (naMCI) subgroups separately.

RESULTS

Five-year outcome data were available for 87 (67%) of the 129 patients. The proportion of patients converting to dementia was 25% at 1 year and 76% at 5 years. Patients with aMCI converted to dementia significantly earlier than those with naMCI. WML severity was not associated with time to conversion to dementia for either MCI patients in general or aMCI patients in particular. Among naMCI patients, there was a tendency for those with a low degree of WML to survive without dementia for longer than those with a high degree of WML. However, this was not statistically significant.

CONCLUSIONS

MCI subtype is a significant independent predictor of conversion to dementia, with aMCI patients having higher risk than naMCI for conversion throughout the 5-year follow-up period. WML severity does not influence conversion to dementia for aMCI but might accelerate progression in naMCI.

Dementia and neuroimaging

Early diagnosis of dementing conditions and an accurate monitoring of their progression are important clinical and research goals, especially given the improving prospects of disease-modifying therapies. Neuroimaging has played and is playing an important role in detecting reversible, treatable causes of dementia, and in characterizing the dementia syndromes by demonstrating structural and functional signatures that can aid in their differentiation. Many new imaging techniques and modalities are also available that allow the assessment of specific aspects of brain structure and function, such as positron emission tomography with new ligands, diffusion tensor magnetic resonance imaging (MRI) and functional MRI. In this review, we report the most recent findings from the papers published in the Journal of Neurology that used conventional and advanced neuroimaging techniques for the study of various dementing conditions.

Medial temporal lobe atrophy relates to executive dysfunction in Alzheimer's disease

BACKGROUND

White matter hyperintensities (WMH) have frequently been associated with lower executive function performance. Little is known, however, about the effects of hippocampal atrophy on executive control in Alzheimer's disease (AD). The present study focused on the association of hippocampal atrophy with executive function in AD patients and examined whether a threshold effect is present, indicating that a certain amount of brain damage must be present before cognitive function becomes impaired. Finally, we examined the combined effect of hippocampal atrophy and WMH on cognitive task performance.

METHODS

We retrospectively collected neuropsychological and neuroimaging data of 94 AD patients. These patients completed tasks of general cognitive function, executive function, memory, and processing speed. With magnetic resonance imaging (MRI), hippocampal atrophy was rated as medial temporal lobe atrophy (MTA) and cerebrovascular disease was rated as WMH using validated visual rating scales.

RESULTS

Medial temporal lobe atrophy (MTA) was associated with lower executive function, general cognitive function, and episodic memory performance. A threshold effect was present, indicating that severe to very severe, but not moderate, MTA was associated with lower executive function. WMH were significantly associated with a single executive test only, whereas the interaction between WMH and MTA was not significantly related to any of the cognitive tasks.

CONCLUSIONS

Our findings suggest that AD neuropathology in itself may be responsible for executive dysfunction. Potential explanations for these findings are discussed, focusing on the role of the hippocampus in executive function tests and reduced frontal-posterior connectivity in this patient sample.

Review of cerebral microangiopathy and Alzheimer's disease: relation between white matter hyperintensities and microbleeds

Although Alzheimer's disease (AD) is basically considered to be a neurodegenerative disorder, cerebrovascular disease is also involved. The role of vascular risk factors and vascular disease in the progression of AD remains incompletely understood. With the development of brain MRI, it is now possible to detect small-vessel disease, whose prevalence and severity increase with age. The first types of small-vessel disease to be described were white matter hyperintensities (WMHs). More recently, small areas of signal loss on T(2)*-weighted images, also called microbleeds (MBs), have been reported. Cerebral MBs are focal deposits of hemosiderin that indicate prior microhemorrhages around small vessels, related to either ruptured atherosclerotic microvessels or amyloid angiopathy. Consequently, using brain MRI for the detection of microangiopathy may prove useful to improve our understanding of the impact of the vascular burden in AD pathology. The relationship between microangiopathy and the clinical course of AD or the conversion of mild cognitive impairment to AD remains questionable in terms of cognitive or affective symptoms, particularly if we consider MBs.

Risk and Determinants of Dementia in Patients with Mild Cognitive Impairment and Brain Subcortical Vascular Changes: A Study of Clinical, Neuroimaging, and Biological Markers-The VMCI-Tuscany Study: Rationale, Design, and Methodology

Dementia is one of the most disabling conditions. Alzheimer's disease and vascular dementia (VaD) are the most frequent causes. Subcortical VaD is consequent to deep-brain small vessel disease (SVD) and is the most frequent form of VaD. Its pathological hallmarks are ischemic white matter changes and lacunar infarcts. Degenerative and vascular changes often coexist, but mechanisms of interaction are incompletely understood. The term mild cognitive impairment defines a transitional state between normal ageing and dementia. Pre-dementia stages of VaD are also acknowledged (vascular mild cognitive impairment, VMCI). Progression relates mostly to the subcortical VaD type, but determinants of such transition are unknown. Variability of phenotypic expression is not fully explained by severity grade of lesions, as depicted by conventional MRI that is not sensitive to microstructural and metabolic alterations. Advanced neuroimaging techniques seem able to achieve this. Beside hypoperfusion, blood-brain-barrier dysfunction has been also demonstrated in subcortical VaD. The aim of the Vascular Mild Cognitive Impairment Tuscany Study is to expand knowledge about determinants of transition from mild cognitive impairment to dementia in patients with cerebral SVD. This paper summarizes the main aims and methodological aspects of this multicenter, ongoing, observational study enrolling patients affected by VMCI with SVD.

Hippocampal atrophy in Alzheimer’s disease

SUMMARY New research criteria for Alzheimer’s disease (AD) and mild cognitive impairment (MCI) emphasize the use of imaging biomarkers in clinical diagnosis of these disorders. The volume loss of medial temporal lobe structures, especially hippocampal atrophy, is the best validated marker of AD. Manual tracing on MRI is the present gold standard for evaluating hippocampal volume; however, it is laborious and tracer-dependent. We categorized the most recent full- or semi-automated methods by the nature of the output of the method: size and shape of subcortical structures, cortical thickness, atrophy-rate and voxel- and region-based characteristics. The features of each method are introduced. The findings in structural MRI studies, especially in those studies utilizing the most recent methods, and the accuracies of those new methods in differentiating AD from healthy controls and stable MCI from progressive MCI are reviewed.