Effects of current alcohol use on brain volume among older adults in the Gothenburg H70 Birth Cohort study 2014-16.

Brain gray- and white matter changes is well described in alcohol-dependent elderly subjects; however, the effect of lower levels of alcohol consumption on the brain is poorly understood. We investigated the impact of different amounts of weekly alcohol consumption on brain structure in a population-based sample of 70-year-olds living in Gothenburg, Sweden. Cross-sectional data from 676 participants from The Gothenburg H70 Birth Cohort Study 2014-16 were included. Current alcohol consumers were divided into seven groups based on self-reported weekly amounts of alcohol consumption in grams (g) (0-50 g/week, used as reference group, 51-100 g/week, 101-150 g/week, 151-200 g/week, 201-250 g/week, 251-300 g/week, and > 300 g/week). Subcortical volumes and cortical thickness were assessed on T1-weighted structural magnetic resonance images using FreeSurfer 5.3, and white matter integrity assessed on diffusion tensor images, using tract-based statistics in FSL. General linear models were carried out to estimate associations between alcohol consumption and gray- and white matter changes in the brain. Self-reported consumption above 250 g/week was associated with thinning in the bilateral superior frontal gyrus, the right precentral gyrus, and the right lateral occipital cortex, in addition to reduced fractional anisotropy (FA) and increased mean diffusivity (MD) diffusively spread in many tracts all over the brain. No changes were found in subcortical gray matter structures. These results suggest that there is a non-linear relationship between alcohol consumption and structural brain changes, in which loss of cortical thickness only occur in non-demented 70-year-olds who consume more than 250 g/week.

CT-based volumetric measures obtained through deep learning: Association with biomarkers of neurodegeneration.

INTRODUCTION: Cranial computed tomography (CT) is an affordable and widely available imaging modality that is used to assess structural abnormalities, but not to quantify neurodegeneration. Previously we developed a deep-learning-based model that produced accurate and robust cranial CT tissue classification. MATERIALS AND METHODS: We analyzed 917 CT and 744 magnetic resonance (MR) scans from the Gothenburg H70 Birth Cohort, and 204 CT and 241 MR scans from participants of the Memory Clinic Cohort, Singapore. We tested associations between six CT-based volumetric measures (CTVMs) and existing clinical diagnoses, fluid and imaging biomarkers, and measures of cognition. RESULTS: CTVMs differentiated cognitively healthy individuals from dementia and prodromal dementia patients with high accuracy levels comparable to MR-based measures. CTVMs were significantly associated with measures of cognition and biochemical markers of neurodegeneration. DISCUSSION: These findings suggest the potential future use of CT-based volumetric measures as an informative first-line examination tool for neurodegenerative disease diagnostics after further validation. HIGHLIGHTS: Computed tomography (CT)-based volumetric measures can distinguish between patients with neurodegenerative disease and healthy controls, as well as between patients with prodromal dementia and controls. CT-based volumetric measures associate well with relevant cognitive, biochemical, and neuroimaging markers of neurodegenerative diseases. Model performance, in terms of brain tissue classification, was consistent across two cohorts of diverse nature. Intermodality agreement between our automated CT-based and established magnetic resonance (MR)-based image segmentations was stronger than the agreement between visual CT and MR imaging assessment.

(Dis-)Connected Dots in Dementia with Lewy Bodies-A Systematic Review of Connectivity Studies.

Studies on dementia with Lewy bodies (DLB) have mainly focused on the degeneration of distinct cortical and subcortical regions related to the deposition of Lewy bodies. In view of the proposed trans-synaptic spread of the α-synuclein pathology, investigating the disease only in this segregated fashion would be detrimental to our understanding of its progression. In this systematic review, we summarize findings on structural and functional brain connectivity in DLB, as connectivity measures may offer better insights on how the brain is affected by the spread of the pathology. Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, we searched Web of Science, PubMed, and SCOPUS for relevant articles published up to November 1, 2021. Of 1215 identified records, we selected and systematically reviewed 53 articles that compared connectivity features between patients with DLB and healthy controls. Structural and functional magnetic resonance imaging, positron emission tomography, single-positron emission computer tomography, and electroencephalography assessments of patients revealed widespread abnormalities within and across brain networks in DLB. Frontoparietal, default mode, and visual networks and their connections to other brain regions featured the most consistent disruptions, which were also associated with core clinical features and cognitive impairments. Furthermore, graph theoretical measures revealed disease-related decreases in local and global network efficiency. This systematic review shows that structural and functional connectivity characteristics in DLB may be particularly valuable at early stages, before overt brain atrophy can be observed. This knowledge may help improve the diagnosis and prognosis in DLB as well as pinpoint targets for future disease-modifying treatments. © 2022 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Prenatal gyrification pattern affects age at onset in frontotemporal dementia.

The paracingulate sulcus is a tertiary sulcus formed during the third trimester. In healthy individuals paracingulate sulcation is more prevalent in the left hemisphere. The anterior cingulate and paracingulate gyri are focal points of neurodegeneration in behavioral variant frontotemporal dementia (bvFTD). This study aims to determine the prevalence and impact of paracingulate sulcation in bvFTD. Structural magnetic resonance images of individuals with bvFTD (n = 105, mean age 66.9 years), Alzheimer's disease (n = 92, 73.3), and healthy controls (n = 110, 62.4) were evaluated using standard protocol for hemispheric paracingulate sulcal presence. No difference in left hemisphere paracingulate sulcal frequency was observed between groups; 0.72, 0.79, and 0.70, respectively, in the bvFTD, Alzheimer's disease, and healthy control groups, (P = 0.3). A significant impact of right (but not left) hemispheric paracingulate sulcation on age at disease onset was identified in bvFTD (mean 60.4 years where absent vs. 63.8 where present [P = 0.04, Cohen's d = 0.42]). This relationship was not observed in Alzheimer's disease. These findings demonstrate a relationship between prenatal neuronal development and the expression of a neurodegenerative disease providing a gross morphological example of brain reserve.

The protective gene dose effect of the APOE ε2 allele on gray matter volume in cognitively unimpaired individuals.

INTRODUCTION: Harboring two copies of the apolipoprotein E (APOE) ε2 allele strongly protects against Alzheimer's disease (AD). However, the effect of this genotype on gray matter (GM) volume in cognitively unimpaired individuals has not yet been described. METHODS: Multicenter brain magnetic resonance images (MRIs) from cognitively unimpaired ε2 homozygotes were matched (1:1) against all other APOE genotypes for relevant confounders (n = 223). GM volumes of ε2 genotypic groups were compared to each other and to the reference group (APOE ε3/ε3). RESULTS: Carrying at least one ε2 allele was associated with larger GM volumes in brain areas typically affected by AD and also in areas associated with cognitive resilience. APOE ε2 homozygotes, but not APOE ε2 heterozygotes, showed larger GM volumes in areas related to successful aging. DISCUSSION: In addition to the known resistance against amyloid-ß deposition, the larger GM volumes in key brain regions may confer APOE ε2 homozygotes additional protection against AD-related cognitive decline.

Deep learning from MRI-derived labels enables automatic brain tissue classification on human brain CT.

Automatic methods for feature extraction, volumetry, and morphometric analysis in clinical neuroscience typically operate on images obtained with magnetic resonance (MR) imaging equipment. Although CT scans are less expensive to acquire and more widely available than MR scans, their application is currently limited to the visual assessment of brain integrity and the exclusion of co-pathologies. CT has rarely been used for tissue classification because the contrast between grey matter and white matter was considered insufficient. In this study, we propose an automatic method for segmenting grey matter (GM), white matter (WM), cerebrospinal fluid (CSF), and intracranial volume (ICV) from head CT images. A U-Net deep learning model was trained and validated on CT images with MRI-derived segmentation labels. We used data from 744 participants of the Gothenburg H70 Birth Cohort Studies for whom CT and T1-weighted MR images had been acquired on the same day. Our proposed model predicted brain tissue classes accurately from unseen CT images (Dice coefficients of 0.79, 0.82, 0.75, 0.93 and 0.98 for GM, WM, CSF, brain volume and ICV, respectively). To contextualize these results, we generated benchmarks based on established MR-based methods and intentional image degradation. Our findings demonstrate that CT-derived segmentations can be used to delineate and quantify brain tissues, opening new possibilities for the use of CT in clinical practice and research.

Demographic and Clinical Characteristics of Individuals with Mild Cognitive Impairment Related to Grade of Alcohol Consumption.

INTRODUCTION: While alcohol overconsumption is regarded as a risk factor for Alzheimer's disease, the specific relationship between alcohol consumption and cognitive impairment remains unclear and poorly understood. Our primary objective is to investigate whether alcohol consumption is associated with lower cognitive performance at an early phase of the development of cognitive impairment (mild cognitive impairment [MCI] stage) and second to present the clinical and demographic characteristics depending on the grade of alcohol consumption. METHODS: This is a cross-sectional observational study, including 251 subjects with the diagnosis MCI, having caregiving contact with Memory Clinic, Karolinska University Hospital, under year 2015. We compared subgroups with different levels of alcohol consumption, concerning social parameters, cognitive, radiological, laboratory profile as well as comorbidities and burden of drugs. RESULTS: Mini-mental State Examination score was not associated with alcohol consumption. Light to moderate drinkers were significantly higher educated. There were significantly more subjects using antianxiety medications among heavy drinkers in comparison with light to moderate drinkers. Finally, never/rare drinkers had significantly lower levels of erythrocyte mean corpuscular volume in their blood tests. DISCUSSION/CONCLUSION: Alcohol consumption was not correlated with a more pronounced cognitive deficit or a distinct clinical severity at an early stage of cognitive impairment apart from higher usage of antianxiety medications. We are planning to follow up all individuals to ascertain if heavy drinkers have a different outcome compared with the other groups.

Global Burden of Small Vessel Disease-Related Brain Changes on MRI Predicts Cognitive and Functional Decline.

Background and Purpose- Cerebral small vessel disease is characterized by a wide range of focal and global brain changes. We used a magnetic resonance imaging segmentation tool to quantify multiple types of small vessel disease-related brain changes and examined their individual and combined predictive value on cognitive and functional abilities. Methods- Magnetic resonance imaging scans of 560 older individuals from LADIS (Leukoaraiosis and Disability Study) were analyzed using automated atlas- and convolutional neural network-based segmentation methods yielding volumetric measures of white matter hyperintensities, lacunes, enlarged perivascular spaces, chronic cortical infarcts, and global and regional brain atrophy. The subjects were followed up with annual neuropsychological examinations for 3 years and evaluation of instrumental activities of daily living for 7 years. Results- The strongest predictors of cognitive performance and functional outcome over time were the total volumes of white matter hyperintensities, gray matter, and hippocampi (P<0.001 for global cognitive function, processing speed, executive functions, and memory and P<0.001 for poor functional outcome). Volumes of lacunes, enlarged perivascular spaces, and cortical infarcts were significantly associated with part of the outcome measures, but their contribution was weaker. In a multivariable linear mixed model, volumes of white matter hyperintensities, lacunes, gray matter, and hippocampi remained as independent predictors of cognitive impairment. A combined measure of these markers based on Z scores strongly predicted cognitive and functional outcomes (P<0.001) even above the contribution of the individual brain changes. Conclusions- Global burden of small vessel disease-related brain changes as quantified by an image segmentation tool is a powerful predictor of long-term cognitive decline and functional disability. A combined measure of white matter hyperintensities, lacunar, gray matter, and hippocampal volumes could be used as an imaging marker associated with vascular cognitive impairment.

The Power of EEG to Predict Conversion from Mild Cognitive Impairment and Subjective Cognitive Decline to Dementia.

INTRODUCTION: The aim of this study was to examine if quantitative electroencephalography (qEEG) using the statistical pattern recognition (SPR) method could predict conversion to dementia in patients with subjective cognitive decline (SCD) and mild cognitive impairment (MCI). METHODS: From 5 Nordic memory clinics, we included 47 SCD patients, 99 MCI patients, and 67 healthy controls. EEGs analyzed with the SPR method together with clinical data recorded at baseline were evaluated. The patients were followed up for a mean of 62.5 (SD 17.6) months and reexamined. RESULTS: Of 200 participants with valid clinical information, 70 had converted to dementia, and 52 had developed Alzheimer's disease. Receiver-operating characteristic analysis of the EEG results as defined by a dementia index (DI) ranging from 0 to 100 revealed that the area under the curve was 0.78 (95% CI 0.70-0.85), corresponding to a sensitivity of 71%, specificity of 69%, and accuracy of 69%. A logistic regression analysis showed that by adding results of a cognitive test at baseline to the EEG DI, accuracy could improve. CONCLUSION: We conclude that applying qEEG using the automated SPR method can be helpful in identifying patients with SCD and MCI that have a high risk of converting to dementia over a 5-year period. As the discriminant power of the method is of moderate degree, it should be used in addition to routine diagnostic methods.

Evaluating severity of white matter lesions from computed tomography images with convolutional neural network.

PURPOSE: Severity of white matter lesion (WML) is typically evaluated on magnetic resonance images (MRI), yet the more accessible, faster, and less expensive method is computed tomography (CT). Our objective was to study whether WML can be automatically segmented from CT images using a convolutional neural network (CNN). The second aim was to compare CT segmentation with MRI segmentation. METHODS: The brain images from the Helsinki University Hospital clinical image archive were systematically screened to make CT-MRI image pairs. Selection criteria for the study were that both CT and MRI images were acquired within 6 weeks. In total, 147 image pairs were included. We used CNN to segment WML from CT images. Training and testing of CNN for CT was performed using 10-fold cross-validation, and the segmentation results were compared with the corresponding segmentations from MRI. RESULTS: A Pearson correlation of 0.94 was obtained between the automatic WML volumes of MRI and CT segmentations. The average Dice similarity index validating the overlap between CT and FLAIR segmentations was 0.68 for the Fazekas 3 group. CONCLUSION: CNN-based segmentation of CT images may provide a means to evaluate the severity of WML and establish a link between CT WML patterns and the current standard MRI-based visual rating scale.

The MemClin project: a prospective multi memory clinics study targeting early stages of cognitive impairment.

BACKGROUND: There remains a lack of large-scale clinical studies of cognitive impairment that aim to increase diagnostic and prognostic accuracy as well as validate previous research findings. The MemClin project will amass large quantities of cross-disciplinary data allowing for the construction of robust models to improve diagnostic accuracy, expand our knowledge on differential diagnostics, strengthen longitudinal prognosis, and harmonise examination protocols across centres. The current article describes the Memory Clinic (MemClin) project's study-design, materials and methods, and patient characteristics. In addition, we present preliminary descriptive data from the ongoing data collection. METHODS: Nine out of ten memory clinics in the greater Stockholm area, which largely use the same examination methods, are included. The data collection of patients with different stages of cognitive impairment and dementia is coordinated centrally allowing for efficient and secure large-scale database construction. The MemClin project rest directly on the memory clinics examinations with cognitive measures, health parameters, and biomarkers. RESULTS: Currently, the MemClin project has informed consent from 1543 patients. Herein, we present preliminary data from 835 patients with confirmed cognitive diagnosis and neuropsychological test data available. Of those, 239 had dementia, 487 mild cognitive impairment (MCI), and 104 subjective cognitive impairment (SCI). In addition, we present descriptive data on visual ratings of brain atrophy and cerebrospinal fluid markers. CONCLUSIONS: Based on our current progress and preliminary data, the MemClin project has a high potential to provide a large-scale database of 1200-1500 new patients annually. This coordinated data collection will allow for the construction of improved diagnostic and prognostic models for neurodegenerative disorders and other cognitive conditions in their naturalistic setting.

Brain Atrophy Subtypes and the ATN Classification Scheme in Alzheimer's Disease.

INTRODUCTION: We investigated the association between atrophy subtypes of Alzheimer's disease (AD), the ATN classification scheme, and key demographic and clinical factors in 2 cohorts with different source characteristics (a highly selective research-oriented cohort, the Alzheimer's Disease Neuroimaging Initiative [ADNI]; and a naturalistic heterogeneous clinically oriented cohort, Karolinska Imaging Dementia Study [KIDS]). METHODS: A total of 382 AD patients were included. Factorial analysis of mixed data was used to investigate associations between AD subtypes based on brain atrophy patterns, ATN profiles based on cerebrospinal fluid biomarkers, and age, sex, Mini Mental State Examination (MMSE), cerebrovascular disease (burden of white matter signal abnormalities, WMSAs), and APOE genotype. RESULTS: Older patients with high WMSA burden, belonging to the typical AD subtype and showing A+T+N+ or A+T+N- profiles clustered together and were mainly from ADNI. Younger patients with low WMSA burden, limbic-predominant or minimal atrophy AD subtypes, and A+T-N- or A+T-N+ profiles clustered together and were mainly from KIDS. APOE ε4 carriers more frequently showed the A+T-N- and A+T+N- profiles. CONCLUSIONS: Our findings align with the recent framework for biological subtypes of AD: the combination of risk factors, protective factors, and brain pathologies determines belonging of AD patients to distinct subtypes.

Androgen deprivation therapy for prostate cancer and risk of dementia.

OBJECTIVES: To study whether androgen deprivation therapy (ADT), the mainstay treatment for advanced and disseminated prostate cancer, is associated with risk of dementia. METHODS: Risk of dementia in men with prostate cancer primarily managed with ADT or watchful waiting (WW) in the Prostate Cancer Database Sweden, PCBaSe, was compared with that in prostate cancer-free men, matched on birth year and county of residency. We used Cox regression to calculate the hazard ratios (HRs) for Alzheimer's and non-Alzheimer's dementia (vascular dementia, dementia secondary to other diseases or unspecified dementias) for different types and duration of ADT and oral antiandrogens (AAs) as well as for men managed with WW. RESULTS: A total of 25 967 men with prostate cancer and 121 018 prostate cancer-free men were followed for a median of 4 years. In both groups 6% of the men were diagnosed with dementia. In men with prostate cancer, gonadotropin-releasing hormone agonist treatment ( HR 1.15, 95% confidence interval [CI] 1.07-1.23) and orchiectomy (HR 1.60, 95% CI 1.32-1.93) were associated with an increased risk of dementia, as compared to no treatment in prostate cancer-free men; however, this increase in risk was only observed for non-Alzheimer's dementia and occurred from year 1-4 after start of ADT. No increase in risk for any type of dementia was observed for men treated with AAs or for men on WW. CONCLUSION: This population-based cohort study does not support previous observations of an increased risk of Alzheimer's dementia for men on ADT; however, there was a small increase in risk of non-Alzheimer's dementia.

A signature pattern of cortical atrophy in dementia with Lewy bodies: A study on 333 patients from the European DLB consortium.

INTRODUCTION: We explored regional brain atrophy patterns and their clinical correlates in dementia with Lewy bodies (DLB). METHODS: In this multicentre study, we included a total of 333 patients with DLB, 352 patients with Alzheimer's disease (AD), and 233 normal controls and used medial temporal lobe atrophy, posterior atrophy, and frontal atrophy (GCA-F) visual rating scales. Patients were classified according to four atrophy patterns. RESULTS: Patients with DLB had higher scores on all the three atrophy scales than normal controls but had less medial temporal lobe atrophy than those with AD (all P values < .001). A signature hippocampal-sparing pattern of regional atrophy was observed in DLB. The magnetic resonance imaging measures showed 65% ability to discriminate between DLB and AD and marginally contributed to the discrimination over and above the core clinical features. DISCUSSION: The most common pattern of atrophy of DLB was hippocampal-sparing. Future studies should explore whether comorbid AD pathology underlies the atrophy patterns seen in DLB.

Inflammatory biomarkers in Alzheimer's disease plasma.

INTRODUCTION: Plasma biomarkers for Alzheimer's disease (AD) diagnosis/stratification are a "Holy Grail" of AD research and intensively sought; however, there are no well-established plasma markers. METHODS: A hypothesis-led plasma biomarker search was conducted in the context of international multicenter studies. The discovery phase measured 53 inflammatory proteins in elderly control (CTL; 259), mild cognitive impairment (MCI; 199), and AD (262) subjects from AddNeuroMed. RESULTS: Ten analytes showed significant intergroup differences. Logistic regression identified five (FB, FH, sCR1, MCP-1, eotaxin-1) that, age/APOε4 adjusted, optimally differentiated AD and CTL (AUC: 0.79), and three (sCR1, MCP-1, eotaxin-1) that optimally differentiated AD and MCI (AUC: 0.74). These models replicated in an independent cohort (EMIF; AUC 0.81 and 0.67). Two analytes (FB, FH) plus age predicted MCI progression to AD (AUC: 0.71). DISCUSSION: Plasma markers of inflammation and complement dysregulation support diagnosis and outcome prediction in AD and MCI. Further replication is needed before clinical translation.

Cognitive medicine - a new approach in health care science.

BACKGROUND: The challenges of today's society call for more knowledge about how to maintain all aspects of cognitive health, such as speed/attention, memory/learning, visuospatial ability, language, executive capacity and social cognition during the life course. MAIN TEXT: Medical advances have improved treatments of numerous diseases, but the cognitive implications have not been sufficiently addressed. Disability induced by cognitive dysfunction is also a major issue in groups of patients not suffering from Alzheimer's disease or related disorders. Recent studies indicate that several negative lifestyle factors can contribute to the development of cognitive impairment, but intervention and prevention strategies have not been implemented. Disability due to cognitive failure among the workforce has become a major challenge. Globally, the changing aging pyramid results in increased prevalence of cognitive disorders, and the diversity of cultures influences the expression, manifestation and consequences of cognitive dysfunction. CONCLUSIONS: Major tasks in the field of cognitive medicine are basic neuroscience research to uncover diverse disease mechanisms, determinations of the prevalence of cognitive dysfunction, health-economical evaluations, and intervention studies. Raising awareness for cognitive medicine as a clinical topic would also highlight the importance of specialized health care units for an integrative approach to the treatment of cognitive dysfunctions.

The interactive effect of demographic and clinical factors on hippocampal volume: A multicohort study on 1958 cognitively normal individuals.

Alzheimer's disease is characterized by hippocampal atrophy. Other factors also influence the hippocampal volume, but their interactive effect has not been investigated before in cognitively healthy individuals. The aim of this study is to evaluate the interactive effect of key demographic and clinical factors on hippocampal volume, in contrast to previous studies frequently investigating these factors in a separate manner. Also, to investigate how comparable the control groups from ADNI, AIBL, and AddNeuroMed are with five population-based cohorts. In this study, 1958 participants were included (100 AddNeuroMed, 226 ADNI, 155 AIBL, 59 BRC, 295 GENIC, 279 BioFiNDER, 398 PIVUS, and 446 SNAC-K). ANOVA and random forest were used for testing between-cohort differences in demographic-clinical variables. Multiple regression was used to study the influence of demographic-clinical variables on hippocampal volume. ANCOVA was used to analyze whether between-cohort differences in demographic-clinical variables explained between-cohort differences in hippocampal volume. Age and global brain atrophy were the most important variables in explaining variability in hippocampal volume. These variables were not only important themselves but also in interaction with gender, education, MMSE, and total intracranial volume. AddNeuroMed, ADNI, and AIBL differed from the population-based cohorts in several demographic-clinical variables that had a significant effect on hippocampal volume. Variability in hippocampal volume in individuals with normal cognition is high. Differences that previously tended to be related to disease mechanisms could also be partly explained by demographic and clinical factors independent from the disease. Furthermore, cognitively normal individuals especially from ADNI and AIBL are not representative of the general population. These findings may have important implications for future research and clinical trials, translating imaging biomarkers to the general population, and validating current diagnostic criteria for Alzheimer's disease and predementia stages.

Predicting Cognitive Decline across Four Decades in Mutation Carriers and Non-carriers in Autosomal-Dominant Alzheimer's Disease.

OBJECTIVES: The aim of this study was to investigate cognitive performance including preclinical and clinical disease course in carriers and non-carriers of autosomal-dominant Alzheimer's disease (adAD) in relation to multiple predictors, that is, linear and non-linear estimates of years to expected clinical onset of disease, years of education and age. METHODS: Participants from five families with early-onset autosomal-dominant mutations (Swedish and Arctic APP, PSEN1 M146V, H163Y, and I143T) included 35 carriers (28 without dementia and 7 with) and 44 non-carriers. All participants underwent a comprehensive clinical evaluation, including neuropsychological assessment at the Memory Clinic, Karolinska University Hospital at Huddinge, Stockholm, Sweden. The time span of disease course covered four decades of the preclinical and clinical stages of dementia. Neuropsychological tests were used to assess premorbid and current global cognition, verbal and visuospatial functions, short-term and episodic memory, attention, and executive function. RESULTS: In carriers, the time-related curvilinear trajectory of cognitive function across disease stages was best fitted to a formulae with three predictors: years to expected clinical onset (linear and curvilinear components), and years of education. In non-carriers, the change was minimal and best predicted by two predictors: education and age. The trajectories for carriers and non-carriers began to diverge approximately 10 years before the expected clinical onset in episodic memory, executive function, and visuospatial function. CONCLUSIONS: The curvilinear trajectory of cognitive functions across disease stages was mimicked by three predictors in carriers. In episodic memory, executive and visuospatial functions, the point of diverging trajectories occurred approximately 10 years ahead of the clinical onset compared to non-carriers. (JINS, 2017, 23, 195-203).

Disrupted Network Topology in Patients with Stable and Progressive Mild Cognitive Impairment and Alzheimer's Disease.

Recent findings suggest that Alzheimer's disease (AD) is a disconnection syndrome characterized by abnormalities in large-scale networks. However, the alterations that occur in network topology during the prodromal stages of AD, particularly in patients with stable mild cognitive impairment (MCI) and those that show a slow or faster progression to dementia, are still poorly understood. In this study, we used graph theory to assess the organization of structural MRI networks in stable MCI (sMCI) subjects, late MCI converters (lMCIc), early MCI converters (eMCIc), and AD patients from 2 large multicenter cohorts: ADNI and AddNeuroMed. Our findings showed an abnormal global network organization in all patient groups, as reflected by an increased path length, reduced transitivity, and increased modularity compared with controls. In addition, lMCIc, eMCIc, and AD patients showed a decreased path length and mean clustering compared with the sMCI group. At the local level, there were nodal clustering decreases mostly in AD patients, while the nodal closeness centrality detected abnormalities across all patient groups, showing overlapping changes in the hippocampi and amygdala and nonoverlapping changes in parietal, entorhinal, and orbitofrontal regions. These findings suggest that the prodromal and clinical stages of AD are associated with an abnormal network topology.

Reproducible segmentation of white matter hyperintensities using a new statistical definition.

OBJECTIVES: We present a method based on a proposed statistical definition of white matter hyperintensities (WMH), which can work with any combination of conventional magnetic resonance (MR) sequences without depending on manually delineated samples. MATERIALS AND METHODS: T1-weighted, T2-weighted, FLAIR, and PD sequences acquired at 1.5 Tesla from 119 subjects from the Kings Health Partners-Dementia Case Register (healthy controls, mild cognitive impairment, Alzheimer's disease) were used. The segmentation was performed using a proposed definition for WMH based on the one-tailed Kolmogorov-Smirnov test. RESULTS: The presented method was verified, given all possible combinations of input sequences, against manual segmentations and a high similarity (Dice 0.85-0.91) was observed. Comparing segmentations with different input sequences to one another also yielded a high similarity (Dice 0.83-0.94) that exceeded intra-rater similarity (Dice 0.75-0.91). We compared the results with those of other available methods and showed that the segmentation based on the proposed definition has better accuracy and reproducibility in the test dataset used. CONCLUSION: Overall, the presented definition is shown to produce accurate results with higher reproducibility than manual delineation. This approach can be an alternative to other manual or automatic methods not only because of its accuracy, but also due to its good reproducibility.