In vivo microstructural heterogeneity of white matter and cognitive correlates in aging using tissue compositional analysis of diffusion magnetic resonance imaging.

BACKGROUND: Age-related cognitive decline is linked to changes in the brain, particularly the deterioration of white matter (WM) microstructure that accelerates after the age of 60. WM deterioration is associated with mild cognitive impairment and dementia, but the origin and role of white matter signal abnormalities (WMSA) seen in standard MRI remain debated due to their heterogeneity. This study explores the potential of single-shell 3-tissue constrained spherical deconvolution (SS3T-CSD), a novel technique that models diffusion data in terms of gray matter (TG ), white matter (Tw ), and cerebrospinal fluid (TC ), to differentiate WMSA from normal-appearing white matter and better understand the interplay between changes in WM microstructure and decline in cognition. METHODS: A total of 189 individuals from the GENIC cohort were included. MRI data, including T1-weighted and diffusion images, were obtained. Preprocessing steps were performed on the diffusion MRI data, followed by the SS3T-CSD. WMSA were segmented using FreeSurfer. Statistical analyses were conducted to assess the association between age, WMSA volume, 3-tissue signal fractions (Tw , TG , and TC ), and neuropsychological variables. RESULTS: Participants above 60 years old showed worse cognitive performance and processing speed compared to those below 60 (p < .001). Age was negatively associated with Tw in normal-appearing white matter (p < .001) and positively associated with TG in both WMSA (p < .01) and normal-appearing white matter (p < .001). Age was also significantly associated with WMSA volume (p < .001). Higher processing speed was associated with lower Tw and higher TG , in normal-appearing white matter (p < .01 and p < .001, respectively), as well as increased WMSA volume (p < .001). Similarly, lower MMSE scores correlated with lower Tw and higher TG in normal-appearing white matter (p < .05). High cholesterol and hypertension were associated with higher WMSA volume (p < .05). CONCLUSION: The microstructural heterogeneity within normal-appearing white matter and WMSA is associated with increasing age and cognitive variation, in cognitively unimpaired individuals. Furthermore, the 3-tissue signal fractions are more specific to potential white matter alterations than conventional MRI measures such as WMSA volume. These findings also support the view that the WMSA volumes may be more influenced by vascular risk factors than the 3-tissue metrics. Finally, the 3-tissue metrics were able to capture associations with cognitive tests and therefore capable of capturing subtle pathological changes in the brain in individuals who are still within the normal range of cognitive performance.

Cholinergic white matter pathways make a stronger contribution to attention and memory in normal aging than cerebrovascular health and nucleus basalis of Meynert.

The integrity of the cholinergic system plays a central role in cognitive decline both in normal aging and neurological disorders including Alzheimer's disease and vascular cognitive impairment. Most of the previous neuroimaging research has focused on the integrity of the cholinergic basal forebrain, or its sub-region the nucleus basalis of Meynert (NBM). Tractography using diffusion tensor imaging data may enable modelling of the NBM white matter projections. We investigated the contribution of NBM volume, NBM white matter projections, small vessel disease (SVD), and age to performance in attention and memory in 262 cognitively normal individuals (39-77 years of age, 53% female). We developed a multimodal MRI pipeline for NBM segmentation and diffusion-based tracking of NBM white matter projections, and computed white matter hypointensities (WM-hypo) as a marker of SVD. We successfully tracked pathways that closely resemble the spatial layout of the cholinergic system as seen in previous post-mortem and DTI tractography studies. We found that high WM-hypo load was associated with older age, male sex, and lower performance in attention and memory. A high WM-hypo load was also associated with lower integrity of the cholinergic system above and beyond the effect of age. In a multivariate model, age and integrity of NBM white matter projections were stronger contributors than WM-hypo load and NBM volume to performance in attention and memory. We conclude that the integrity of NBM white matter projections plays a fundamental role in cognitive aging. This and other modern neuroimaging methods offer new opportunities to re-evaluate the cholinergic hypothesis of cognitive aging.

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.

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.

Measuring Global Brain Atrophy with the Brain Volume/Cerebrospinal Fluid Index: Normative Values, Cut-Offs and Clinical Associations.

BACKGROUND: Global brain atrophy is present in normal aging and different neurodegenerative disorders such as Alzheimer's disease (AD) and is becoming widely used to monitor disease progression. SUMMARY: The brain volume/cerebrospinal fluid index (BV/CSF index) is validated in this study as a measurement of global brain atrophy. We tested the ability of the BV/CSF index to detect global brain atrophy, investigated the influence of confounders, provided normative values and cut-offs for mild, moderate and severe brain atrophy, and studied associations with different outcome variables. A total of 1,009 individuals were included [324 healthy controls, 408 patients with mild cognitive impairment (MCI) and 277 patients with AD]. Magnetic resonance images were segmented using FreeSurfer, and the BV/CSF index was calculated and studied both cross-sectionally and longitudinally (1-year follow-up). Both AD patients and MCI patients who progressed to AD showed greater global brain atrophy compared to stable MCI patients and controls. Atrophy was associated with older age, larger intracranial volume, less education and presence of the ApoE ε4 allele. Significant correlations were found with clinical variables, CSF biomarkers and several cognitive tests. KEY MESSAGES: The BV/CSF index may be useful for staging individuals according to the degree of global brain atrophy, and for monitoring disease progression. It also shows potential for predicting clinical changes and for being used in the clinical routine.

Alterations in cortical thickness development in preterm-born individuals: Implications for high-order cognitive functions.

Very preterm birth (gestational age <33 weeks) is associated with alterations in cortical thickness and with neuropsychological/behavioural impairments. Here we studied cortical thickness in very preterm born individuals and controls in mid-adolescence (mean age 15 years) and beginning of adulthood (mean age 20 years), as well as longitudinal changes between the two time points. Using univariate approaches, we showed both increases and decreases in cortical thickness in very preterm born individuals compared to controls. Specifically (1) very preterm born adolescents displayed extensive areas of greater cortical thickness, especially in occipitotemporal and prefrontal cortices, differences which decreased substantially by early adulthood; (2) at both time points, very preterm-born participants showed smaller cortical thickness, especially in parahippocampal and insular regions. We then employed a multivariate approach (support vector machine) to study spatially discriminating features between the two groups, which achieved a mean accuracy of 86.5%. The spatially distributed regions in which cortical thickness best discriminated between the groups (top 5%) included temporal, occipitotemporal, parietal and prefrontal cortices. Within these spatially distributed regions (top 1%), longitudinal changes in cortical thickness in left temporal pole, right occipitotemporal gyrus and left superior parietal lobe were significantly associated with scores on language-based tests of executive function. These results describe alterations in cortical thickness development in preterm-born individuals in their second decade of life, with implications for high-order cognitive processing.

Automated Hippocampal Subfield Measures as Predictors of Conversion from Mild Cognitive Impairment to Alzheimer's Disease in Two Independent Cohorts.

Previous studies have shown that hippocampal subfields may be differentially affected by Alzheimer's disease (AD). This study used an automated analysis technique and two large cohorts to (1) investigate patterns of subfield volume loss in mild cognitive impairment (MCI) and AD, (2) determine the pattern of subfield volume loss due to age, gender, education, APOE ε4 genotype, and neuropsychological test scores, (3) compare combined subfield volumes to hippocampal volume alone at discriminating between AD and healthy controls (HC), and predicting future MCI conversion to AD at 12 months. 1,069 subjects were selected from the AddNeuroMed and Alzheimer's disease neuroimaging initiative (ADNI) cohorts. Freesurfer was used for automated segmentation of the hippocampus and hippocampal subfields. Orthogonal partial least squares to latent structures (OPLS) was used to train models on AD and HC subjects using one cohort for training and the other for testing and the combined cohort was used to predict MCI conversion. MANCOVA and linear regression analyses showed multiple subfield volumes including Cornu Ammonis 1 (CA1), subiculum and presubiculum were atrophied in AD and MCI and were related to age, gender, education, APOE ε4 genotype, and neuropsychological test scores. For classifying AD from HC, combined subfield volumes achieved comparable classification accuracy (81.7%) to total hippocampal (80.7%), subiculum (81.2%) and presubiculum (80.6%) volume. For predicting MCI conversion to AD combined subfield volumes and presubiculum volume were more accurate (81.1%) than total hippocampal volume. (76.7%).

Regional magnetic resonance imaging measures for multivariate analysis in Alzheimer's disease and mild cognitive impairment.

Automated structural magnetic resonance imaging (MRI) processing pipelines are gaining popularity for Alzheimer's disease (AD) research. They generate regional volumes, cortical thickness measures and other measures, which can be used as input for multivariate analysis. It is not clear which combination of measures and normalization approach are most useful for AD classification and to predict mild cognitive impairment (MCI) conversion. The current study includes MRI scans from 699 subjects [AD, MCI and controls (CTL)] from the Alzheimer's disease Neuroimaging Initiative (ADNI). The Freesurfer pipeline was used to generate regional volume, cortical thickness, gray matter volume, surface area, mean curvature, gaussian curvature, folding index and curvature index measures. 259 variables were used for orthogonal partial least square to latent structures (OPLS) multivariate analysis. Normalisation approaches were explored and the optimal combination of measures determined. Results indicate that cortical thickness measures should not be normalized, while volumes should probably be normalized by intracranial volume (ICV). Combining regional cortical thickness measures (not normalized) with cortical and subcortical volumes (normalized with ICV) using OPLS gave a prediction accuracy of 91.5 % when distinguishing AD versus CTL. This model prospectively predicted future decline from MCI to AD with 75.9 % of converters correctly classified. Normalization strategy did not have a significant effect on the accuracies of multivariate models containing multiple MRI measures for this large dataset. The appropriate choice of input for multivariate analysis in AD and MCI is of great importance. The results support the use of un-normalised cortical thickness measures and volumes normalised by ICV.

Comparing three neuropsychological subgrouping approaches in subjective and mild cognitive impairment from a naturalistic multicenter study.

Subjective cognitive impairment (SCI) and mild cognitive impairment (MCI) are two clinical groups with an increased risk to develop dementia, but they are highly heterogeneous. This study compared three different approaches to subgroup SCI and MCI patients and investigated their capacity to disentangle cognitive and biomarker heterogeneity. We included 792 patients from the MemClin-cohort (142 SCI and 650 MCI). Biomarkers included cerebrospinal fluid measures of beta-amyloid-42 and phosphorylated tau, as well as visual ratings of medial temporal lobe atrophy and white matter hyperintensities on magnetic resonance imaging. We found that a more inclusive approach identified individuals with a positive beta-amyloid-42 biomarker; a less inclusive approach captured individuals with higher medial temporal lobe atrophy; and a data-driven approach captured individuals with high white matter hyperintensities burden. The three approaches also captured some neuropsychological differences. We conclude that choice of approach may differ depending on the purpose. This study helps to advance our current understanding of the clinical and biological heterogeneity within SCI and MCI, particularly in the unselected memory clinic setting.

A deep learning model for brain age prediction using minimally preprocessed T1w images as input.

Introduction: In the last few years, several models trying to calculate the biological brain age have been proposed based on structural magnetic resonance imaging scans (T1-weighted MRIs, T1w) using multivariate methods and machine learning. We developed and validated a convolutional neural network (CNN)-based biological brain age prediction model that uses one T1w MRI preprocessing step when applying the model to external datasets to simplify implementation and increase accessibility in research settings. Our model only requires rigid image registration to the MNI space, which is an advantage compared to previous methods that require more preprocessing steps, such as feature extraction. Methods: We used a multicohort dataset of cognitively healthy individuals (age range = 32.0-95.7 years) comprising 17,296 MRIs for training and evaluation. We compared our model using hold-out (CNN1) and cross-validation (CNN2-4) approaches. To verify generalisability, we used two external datasets with different populations and MRI scan characteristics to evaluate the model. To demonstrate its usability, we included the external dataset's images in the cross-validation training (CNN3). To ensure that our model used only the brain signal on the image, we also predicted brain age using skull-stripped images (CNN4). Results: The trained models achieved a mean absolute error of 2.99, 2.67, 2.67, and 3.08 years for CNN1-4, respectively. The model's performance in the external dataset was in the typical range of mean absolute error (MAE) found in the literature for testing sets. Adding the external dataset to the training set (CNN3), overall, MAE is unaffected, but individual cohort MAE improves (5.63-2.25 years). Salience maps of predictions reveal that periventricular, temporal, and insular regions are the most important for age prediction. Discussion: We provide indicators for using biological (predicted) brain age as a metric for age correction in neuroimaging studies as an alternative to the traditional chronological age. In conclusion, using different approaches, our CNN-based model showed good performance using one T1w brain MRI preprocessing step. The proposed CNN model is made publicly available for the research community to be easily implemented and used to study ageing and age-related disorders.

Combining MRI and CSF measures for classification of Alzheimer's disease and prediction of mild cognitive impairment conversion.

The suggested revision of the NINCDS-ADRDA criterion for the diagnosis of Alzheimer's disease (AD) includes at least one abnormal biomarker among magnetic resonance imaging (MRI), positron emission tomography (PET) and cerebrospinal fluid (CSF). We aimed to investigate if the combination of baseline MRI and CSF could enhance the classification of AD compared to using either alone and predict mild cognitive impairment (MCI) conversion at multiple future time points. 369 subjects from the Alzheimer's disease Neuroimaging Initiative (ADNI) were included in the study (AD=96, MCI=162 and CTL=111). Freesurfer was used to generate regional subcortical volumes and cortical thickness measures. A total of 60 variables were used for orthogonal partial least squares to latent structures (OPLS) multivariate analysis (57 MRI measures and 3 CSF measures: Aß(42), t-tau and p-tau). Combining MRI and CSF gave the best results for distinguishing AD vs. CTL. We found an accuracy of 91.8% for the combined model at baseline compared to 81.6% for CSF measures and 87.0% for MRI measures alone. The combined model also gave the best accuracy when distinguishing between MCI vs. CTL (77.6%) at baseline. MCI subjects who converted to AD by 12 and 18month follow-up were accurately predicted at baseline using an AD vs. CTL model (82.9% and 86.4% respectively), with lower prediction accuracies for those MCI subjects converting by 24 and 36month follow up (75.4% and 68.0% respectively). The overall prediction accuracies for converters and non-converters ranged from 58.6% to 66.4% at different time points. Combining MRI and CSF measures in a multivariate model at baseline gave better accuracy for discriminating between AD and CTL, between MCI and CTL and for predicting future conversion from MCI to AD, than using either MRI or CSF separately.

Neuropathologic Features of Antemortem Atrophy-Based Subtypes of Alzheimer Disease.

OBJECTIVES: To investigate whether antemortem MRI-based atrophy subtypes of Alzheimer's disease (AD) differ in neuropathological features and comorbid non-AD pathologies at postmortem. METHODS: From the ADNI cohort, we included individuals with: antemortem MRI evaluating brain atrophy within 2y before death; antemortem diagnosis of AD dementia/mild cognitive impairment; postmortem-confirmed AD neuropathologic change. Antemortem atrophy subtypes were modeled as continuous phenomena based on a recent conceptual framework: typicality (spanning limbic-predominant AD to hippocampal-sparing AD) and severity (spanning typical AD to minimal atrophy AD). Postmortem neuropathological evaluation included AD hallmarks, amyloid-beta and tau as well as non-AD pathologies, alpha-synuclein and TAR DNA-binding protein-43 (TDP-43). We also investigated the overall concomitance across these pathologies. Partial correlations assessed the associations between antemortem atrophy subtypes and postmortem neuropathological outcomes. RESULTS: In 31 individuals (26 AD dementia/5 mild cognitive impaired, mean age=80y, 26% females), antemortem typicality was significantly negatively associated with neuropathological features, including amyloid-beta (rho=-0.39 overall), tau (rho=-0.38 regionally), alpha-synuclein (rho=-0.39 regionally), TDP-43 (rho=-0.49 overall), and concomitance of pathologies (rho=-0.59 regionally). Limbic-predominant AD was associated with higher Thal phase, neuritic plaque density, and presence of TDP-43 compared to hippocampal-sparing AD. Regionally, limbic-predominant AD showed higher presence of tau and alpha-synuclein pathologies in medial temporal structures, higher presence of TDP-43 and concomitance of pathologies subcortically/cortically compared to hippocampal-sparing AD. Antemortem severity was significantly negatively associated with concomitance of pathologies (rho=-0.43 regionally), such that typical AD showed higher concomitance of pathologies than minimal atrophy AD. DISCUSSION: We provide a direct antemortem-to-postmortem validation, highlighting the importance of understanding atrophy-based heterogeneity in AD relative to AD and non-AD pathologies. We suggest that: (a) typicality and severity in atrophy reflect differential aspects of susceptibility of the brain to AD and non-AD pathologies; (b) limbic-predominant AD and typical AD subtypes share similar biological pathways, making them more vulnerable to AD and non-AD pathologies compared to hippocampal-sparing AD, which may follow a different biological pathway. Our findings provide a deeper understanding of associations of atrophy subtypes in AD with different pathologies, enhancing prevailing knowledge of biological heterogeneity in AD and could contribute towards tracking disease progression and designing clinical trials in the future.

Multi-cohort and longitudinal Bayesian clustering study of stage and subtype in Alzheimer's disease.

Understanding Alzheimer's disease (AD) heterogeneity is important for understanding the underlying pathophysiological mechanisms of AD. However, AD atrophy subtypes may reflect different disease stages or biologically distinct subtypes. Here we use longitudinal magnetic resonance imaging data (891 participants with AD dementia, 305 healthy control participants) from four international cohorts, and longitudinal clustering to estimate differential atrophy trajectories from the age of clinical disease onset. Our findings (in amyloid-ß positive AD patients) show five distinct longitudinal patterns of atrophy with different demographical and cognitive characteristics. Some previously reported atrophy subtypes may reflect disease stages rather than distinct subtypes. The heterogeneity in atrophy rates and cognitive decline within the five longitudinal atrophy patterns, potentially expresses a complex combination of protective/risk factors and concomitant non-AD pathologies. By alternating between the cross-sectional and longitudinal understanding of AD subtypes these analyses may allow better understanding of disease heterogeneity.

Association between brain volume and disability over time in multiple sclerosis.

Background: Most previous multiple sclerosis (MS) brain atrophy studies using MS impact scale 29 (MSIS-29) or symbol digit modalities test (SDMT) have been cross-sectional with limited sets of clinical outcomes. Objectives: To investigate which brain and lesion volume metrics show the strongest long-term associations with the expanded disability status scale (EDSS), SDMT, and MSIS-29, and whether MRI-clinical associations vary with age. Methods: We acquired MRI and clinical data from a real-world Swedish MS cohort. FreeSurfer and SPM Lesion Segmentation Tool were used to obtain brain parenchymal, cortical and subcortical grey matter, thalamic and white matter fractions as well as T1- and T2-lesion volumes. Mixed-effects and rolling regression models were used in the statistical analyses. Results: We included 989 persons with MS followed for a median of 9.3 (EDSS), 10.1 (SDMT), and 9.3 (MSIS-29) years, respectively. In a cross-sectional analysis, the strength of the associations of the MRI metrics with the EDSS and MSIS-29 was found to drastically increase after 40-50 years of age. Low baseline regional grey matter fractions were associated with longitudinal increase of EDSS and physical MSIS-29 scores and decrease in SDMT scores and these atrophy measures were stronger predictors than the lesion volumes. Conclusions: The strength of MRI-clinical associations increase with age. Grey matter volume fractions are stronger predictors of long-term disability measures than lesion volumes.

Multivariate analysis of MRI data for Alzheimer's disease, mild cognitive impairment and healthy controls.

We have used multivariate data analysis, more specifically orthogonal partial least squares to latent structures (OPLS) analysis, to discriminate between Alzheimer's disease (AD), mild cognitive impairment (MCI) and elderly control subjects combining both regional and global magnetic resonance imaging (MRI) volumetric measures. In this study, 117 AD patients, 122 MCI patients and 112 control subjects (from the AddNeuroMed study) were included. High-resolution sagittal 3D MP-RAGE datasets were acquired from each subject. Automated regional segmentation and manual outlining of the hippocampus were performed for each image. Altogether this yielded volumes of 24 different anatomically defined structures which were used for OPLS analysis. 17 randomly selected AD patients, 12 randomly selected control subjects and the 22 MCI subjects who converted to AD at 1-year follow up were excluded from the initial OPLS analysis to provide a small external test set for model validation. Comparing AD with controls we found a sensitivity of 87% and a specificity of 90% using hippocampal measures alone. Combining both global and regional measures resulted in a sensitivity of 90% and a specificity of 94%. This increase in sensitivity and specificity resulted in an increase of the positive likelihood ratio from 9 to 15. From the external test set, the model predicted 82% of the AD patients and 83% of the control subjects correctly. Finally, 73% of the MCI subjects which converted to AD at 1 year follow-up were shown to resemble AD patients more closely than controls. This method shows potential for distinguishing between different patient groups. Combining the different MRI measures together resulted in a significantly better classification than using them separately. OPLS also shows potential for predicting conversion from MCI to AD.

AddNeuroMed and ADNI: similar patterns of Alzheimer's atrophy and automated MRI classification accuracy in Europe and North America.

The European Union AddNeuroMed program and the US-based Alzheimer Disease Neuroimaging Initiative (ADNI) are two large multi-center initiatives designed to collect and validate biomarker data for Alzheimer's disease (AD). Both initiatives use the same MRI data acquisition scheme. The current study aims to compare and combine magnetic resonance imaging (MRI) data from the two study cohorts using an automated image analysis pipeline and a multivariate data analysis approach. We hypothesized that the two cohorts would show similar patterns of atrophy, despite demographic differences and could therefore be combined. MRI scans were analyzed from a total of 1074 subjects (AD=295, MCI=444 and controls=335) using Freesurfer, an automated segmentation scheme which generates regional volume and regional cortical thickness measures which were subsequently used for multivariate analysis (orthogonal partial least squares to latent structures (OPLS)). OPLS models were created for the individual cohorts and for the combined cohort to discriminate between AD patients and controls. The ADNI cohort was used as a replication dataset to validate the model created for the AddNeuroMed cohort and vice versa. The combined cohort model was used to predict conversion to AD at baseline of MCI subjects at 1 year follow-up. The AddNeuroMed, the ADNI and the combined cohort showed similar patterns of atrophy and the predictive power was similar (between 80 and 90%). The combined model also showed potential in predicting conversion from MCI to AD, resulting in 71% of the MCI converters (MCI-c) from both cohorts classified as AD-like and 60% of the stable MCI subjects (MCI-s) classified as control-like. This demonstrates that the methods used are robust and that large data sets can be combined if MRI imaging protocols are carefully aligned.

The interplay between gray matter and white matter neurodegeneration in subjective cognitive decline.

AIMS: To investigate the interplay between gray matter (GM) and white matter (WM) neurodegeneration in subjective cognitive decline (SCD), including thickness across the whole cortical mantle, hippocampal volume, and integrity across the whole WM. METHODS: We included 225 cognitively unimpaired individuals from a community-based cohort. Subjective cognitive complaints were assessed through 9 questions covering amnestic and non-amnestic cognitive domains. In our cohort, 123 individuals endorsed from one to six subjective cognitive complaints (i.e. they fulfilled the diagnostic criteria for SCD), while 102 individuals reported zero complaints. GM neurodegeneration was assessed through measures of cortical thickness across the whole mantle and hippocampal volume. WM neurodegeneration was assessed through measures of mean diffusivity (MD) across the whole WM skeleton. Mediation analysis and multiple linear regression were conducted to investigate the interplay between the measures of GM and WM neurodegeneration. RESULTS: A higher number of complaints was associated with reduced hippocampal volume, cortical thinning in several frontal and temporal areas and the insula, and higher MD across the WM skeleton, with a tendency to spare the occipital lobe. SCD-related cortical thinning and increased MD were associated with each other and jointly contributed to complaints, but the contribution of cortical thinning to the number of complaints was stronger. CONCLUSIONS: Neurodegeneration processes affecting the GM and WM seem to be associated with each other in SCD and include brain areas other than those typically targeted by Alzheimer's disease. Our findings suggest that SCD may be a sensitive behavioral marker of heterogeneous brain pathologies in individuals recruited from the community.

Functional Connectivity and Compensation of Phonemic Fluency in Aging.

Neural compensatory mechanisms associated with broad cognitive abilities have been studied. However, those associated with specific cognitive subdomains (e.g., verbal fluency) remain to be investigated in healthy aging. Here, we delineate: (a) neural substrates of verbal (phonemic) fluency, and (b) compensatory mechanisms mediating the association between these neural substrates and phonemic fluency. We analyzed resting-state functional magnetic resonance imaging from 133 right-handed, cognitively normal individuals who underwent the Controlled Oral Word Association Test (COWAT) to record their phonemic fluency. We evaluated functional connectivity in an established and extended language network comprising Wernicke, Broca, thalamic and anti-correlated modules. (a) We conducted voxel-wise multiple linear regression to identify the brain areas associated with phonemic fluency. (b) We used mediation effects of cognitive reserve, measured by the Wechsler Adult Intelligence Scale-Information subtest, upon the association between functional connectivity and phonemic fluency tested to investigate compensation. We found that: (a) Greater functional connectivity between the Wernicke module and brain areas within the anti-correlated module was associated with better performance in phonemic fluency, (b) Cognitive reserve was an unlikely mediator in younger adults. In contrast, cognitive reserve was a partial mediator of the association between functional connectivity and phonemic fluency in older adults, likely representing compensation to counter the effect of aging. We conclude that in healthy aging, higher performance in phonemic fluency at older ages could be attributed to greater functional connectivity partially facilitated by higher cognitive reserve, presumably reflecting compensatory mechanisms to minimize the effect of aging.

Cognitive dedifferentiation as a function of cognitive impairment in the ADNI and MemClin cohorts.

The cause of cognitive dedifferentiation has been suggested as specific to late-life abnormal cognitive decline rather than a general feature of aging. This hypothesis was tested in two large cohorts with different characteristics. Individuals (n = 2710) were identified in the Alzheimer's Disease Neuroimaging Initiative (ADNI) research database (n = 1282) in North America, and in the naturalistic multi-site MemClin Project database (n = 1223), the latter recruiting from 9 out of 10 memory clinics in the greater Stockholm catchment area in Sweden. Comprehensive neuropsychological testing informed diagnosis of dementia, mild cognitive impairment (MCI), or subjective cognitive impairment (SCI). Diagnosis was further collapsed into cognitive impairment (CI: MCI or dementia) vs no cognitive impairment (NCI). After matching, loadings on the first principal component were higher in the CI vs NCI group in both ADNI (53.1% versus 38.3%) and MemClin (33.3% vs 30.8%). Correlations of all paired combinations of individual tests by diagnostic group were also stronger in the CI group in both ADNI (mean inter-test r = 0.51 vs r = 0.33, p < 0.001) and MemClin (r = 0.31 vs r = 0.27, p = 0.042). Dedifferentiation was explained by cognitive impairment when controlling for age, sex, and education. This finding replicated across two separate, large cohorts of older individuals. Knowledge that the structure of human cognition becomes less diversified and more dependent on general intelligence as a function of cognitive impairment should inform clinical assessment and care for these patients as their neurodegeneration progresses.

Shape Information Improves the Cross-Cohort Performance of Deep Learning-Based Segmentation of the Hippocampus.

Performing an accurate segmentation of the hippocampus from brain magnetic resonance images is a crucial task in neuroimaging research, since its structural integrity is strongly related to several neurodegenerative disorders, including Alzheimer's disease (AD). Some automatic segmentation tools are already being used, but, in recent years, new deep learning (DL)-based methods have been proven to be much more accurate in various medical image segmentation tasks. In this work, we propose a DL-based hippocampus segmentation framework that embeds statistical shape of the hippocampus as context information into the deep neural network (DNN). The inclusion of shape information is achieved with three main steps: (1) a U-Net-based segmentation, (2) a shape model estimation, and (3) a second U-Net-based segmentation which uses both the original input data and the fitted shape model. The trained DL architectures were tested on image data of three diagnostic groups [AD patients, subjects with mild cognitive impairment (MCI) and controls] from two cohorts (ADNI and AddNeuroMed). Both intra-cohort validation and cross-cohort validation were performed and compared with the conventional U-net architecture and some variations with other types of context information (i.e., autocontext and tissue-class context). Our results suggest that adding shape information can improve the segmentation accuracy in cross-cohort validation, i.e., when DNNs are trained on one cohort and applied to another. However, no significant benefit is observed in intra-cohort validation, i.e., training and testing DNNs on images from the same cohort. Moreover, compared to other types of context information, the use of shape context was shown to be the most successful in increasing the accuracy, while keeping the computational time in the order of a few minutes.