Qualitative estimates of medial temporal atrophy as a predictor of progression from mild cognitive impairment to dementia

Diagnostic utility of brain MRI volumetry in comparing traumatic brain injury, Alzheimer disease and behavioral variant frontotemporal dementia

BACKGROUND

Brain MRI with volumetric quantification, MRI volumetry, can improve diagnostic delineation of patients with neurocognitive disorders by identifying brain atrophy that may not be evident on visual assessments.

OBJECTIVE

To investigate diagnostic utility of MRI volumetry in traumatic brain injury (TBI), early-onset Alzheimer disease (EOAD), late-onset Alzheimer disease, and behavioral variant frontotemporal dementia (bvFTD).

METHOD

We utilized 137 participants of TBI (n = 40), EOAD (n = 45), LOAD (n = 32), and bvFTD (n = 20). Participants had 3D T1 brain MRI imaging amendable to MRI volumetry. Scan volumes were analyzed with Neuroreader. One-way ANOVA compared brain volumes across diagnostic groups. Discriminant analysis was done with leave-one-out cross validation on Neuroreader metrics to determine diagnostic delineation across groups.

RESULT

LOAD was the oldest compared to other groups (F = 27.5, p < .001). There were no statistically significant differences in sex (p = .58) with women comprising 54.7% of the entire cohort. EOAD and LOAD had the lowest Mini-Mental State Exam (MMSE) scores compared to TBI (p = .04 for EOAD and p = .01 for LOAD). LOAD had lowest hippocampal volumes (Left Hippocampus F = 13.1, Right Hippocampus F = 7.3, p < .001), low white matter volume in TBI (F = 5.9, p < .001), lower left parietal lobe volume in EOAD (F = 9.4, p < .001), and lower total gray matter volume in bvFTD (F = 32.8, p < .001) and caudate atrophy (F = 1737.5, p < .001). Areas under the curve ranged from 92.3 to 100%, sensitivity between 82.2 and 100%, specificity of 78.1-100%. TBI was the most accurately delineated diagnosis. Predictive features included caudate, frontal, parietal, temporal lobar and total white matter volumes.

CONCLUSION

We identified the diagnostic utility of regional volumetric differences across multiple neurocognitive disorders. Brain MRI volumetry is widely available and can be applied in distinguishing these disorders.

Using eZIS to Predict Progression from MCI to Dementia in Three Years

(1) Background: Mild cognitive impairment (MCI) due to Alzheimer's disease (AD) progresses to dementia at a higher annual rate, while other MCIs may remain stable or even improve over time. Discriminating progressive from non-progressive cases of MCI is crucial and challenging. (2) Methods: A retrospective study of individuals with MCI was conducted at a university hospital located in southern Taiwan. The researchers collected demographic data, comorbidities, the scores of cognitive tests, three easy Z-score imaging system (eZIS) indicators (severity, extent, and ratio), Fazekas scale scores, mesial temporal atrophy (MTA) scores, clinical outcomes including deterioration of Cognitive Abilities Screening Instrument, Mini-mental State Examination, Clinical Dementia Rating Sum of Box scores, and the conversion from MCI to dementia. Those who converted to dementia in three years and non-converters were compared by the three eZIS indicators to test the predictive utility, and the clinical outcomes were evaluated by regression and ROC curve analysis. (3) Results: The three eZIS indicators were significantly higher in the group of progressive MCI than in stable MCI. eZIS severity is positively correlated with a deterioration in the scores of the Cognitive Abilities Screening Instrument and Clinical Dementia Rating Sum of Box. eZIS severity is also positively correlated with conversion from MCI to dementia. The AUC for severity is 0.719, and the optimal cutoff value of severity for predicting conversion is 1.22. (4) Conclusions: During three years of follow-up, MCI individuals with greater eZIS severity were significantly associated with worse cognitive assessment scores and a higher conversion rate to dementia.

Mesial temporal atrophy in preoperative MRI rather than steep Trendelenburg position is associated with postoperative delirium in patients undergoing a major urologic surgery

PURPOSE

To investigate whether steep Trendelenburg in a major urologic surgery is associated with postoperative delirium, and to examine other potential clinical and radiologic factors predictive of postoperative delirium.

METHODS

182 patients who received a major urologic surgery and underwent a 3.0-T brain MRI scan within 1 year prior to the date of surgery were retrospectively enrolled. Preoperative brain MRIs were used to analyze features related to small vessel disease burden and mesial temporal atrophy. Presence of a significant mesial temporal atrophy was defined as Scheltens' scale ≥ 2. Patients' clinico-demographic data and MRI features were used to identify significant predictors of postoperative delirium using the logistic regression analysis. Independent predictors found significant in the univariate analysis were further evaluated in the multivariate analysis.

RESULTS

Incidence of postoperative delirium was 6.0%. Patients with postoperative delirium had lower body mass index (21.3 vs. 25.0 kg/m2, P = 0.003), prolonged duration of anesthesia (362.7 vs. 224.7 min, P < 0.001) and surgery (302.2 vs. 174.5 min, P < 0.001), and had more significant mesial temporal atrophy (64% vs. 30%, P = 0.046). In the univariate analysis, female sex, type of surgery (radical prostatectomy over cystectomy), prolonged duration of anesthesia (≥ 6 h), and presence of a significant mesial temporal atrophy were significant predictors (all P-values < 0.050), but only the presence of significant mesial temporal atrophy was significant in the multivariate analysis [odds ratio (OR), 3.69; 95% CI 0.99-13.75; P = 0.046].

CONCLUSION

Steep Trendelenburg was not associated with postoperative delirium. Significant mesial temporal atrophy (Scheltens' scale ≥ 2) in preoperative brain MRI was predictive of postoperative delirium.

TRIAL REGISTRATION

Not applicable.

Structural But Not Functional Connectivity Differences within Default Mode Network Indicate Conversion to Dementia

BACKGROUND

Malfunctioning of the default mode network (DMN) has been consistently related to mild cognitive impairment (MCI) and Alzheimer's disease (AD). However, evidence on differences in this network between MCI converters (MCI-c) and non-converters (MCI-nc), which could mark progression to AD, is still inconsistent.

OBJECTIVE

To multimodally investigate the DMN in the AD continuum.

METHODS

We measured gray matter (GM) volume, white matter (WM) integrity, and functional connectivity (FC) at rest in healthy elderly controls, MCI-c, MCI-nc, and AD patients, matched on sociodemographic variables.

RESULTS

Significant differences between AD patients and controls were found in the structure of most of the regions of the DMN. MCI-c only differed from MCI-nc in GM volume of the left parahippocampus and bilateral hippocampi and middle frontal gyri, as well as in WM integrity of the parahippocampal cingulum connecting the left hippocampus and precuneus. We found significant correlations between integrity in some of those regions and global neuropsychological status, as well as an excellent discrimination ability between converters and non-converters for the sum of GM volume of left parahippocampus, bilateral hippocampi, and middle frontal gyri, and WM integrity of left parahippocampal cingulum. However, we found no significant differences in FC.

CONCLUSION

These results further support the relationship between abnormalities in the DMN and AD, and suggest that structural measures could be more accurate than resting-state estimates as markers of conversion from MCI to AD.

Functional connectivity and mild behavioral impairment in dementia-free elderly

Background

Mild behavioral impairment (MBI) is a syndrome that uses later-life emergent and persistent neuropsychiatric symptoms (NPS) to identify a group at high risk for incident dementia. MBI is associated with neurodegenerative disease markers in advance of syndromic dementia. Functional connectivity (FC) correlates of MBI are understudied and could provide further insights into mechanisms early in the disease course. We used resting-state functional magnetic resonance imaging (rs-fMRI) to test the hypothesis that FC within the default mode network (DMN) and salience network (SN) of persons with MBI (MBI+) is reduced, relative to those without (MBI-).

Methods

From two harmonized dementia-free cohort studies, using a score of ≥6 on the MBI Checklist to define MBI status, 32 MBI+ and 63 MBI- individuals were identified (mean age: 71.7 years; 54.7% female). Seed-based connectivity analysis was implemented in each MBI group using the CONN fMRI toolbox (v20.b), with the posterior cingulate cortex (PCC) as the seed region within the DMN and anterior cingulate cortex (ACC) as the seed within the SN. The average time series from the PCC and ACC were used to determine FC with other regions within the DMN (medial prefrontal cortex, lateral inferior parietal cortex) and SN (anterior insula, supramarginal gyrus, rostral prefrontal cortex), respectively. Age, sex, years of education, and Montreal Cognitive Assessment scores were included as model covariates. The false discovery rate approach was used to correct for multiple comparisons, with a p-value of .05 considered significant.

Results

For the DMN, MBI+ individuals exhibited reduced FC between the PCC and the medial prefrontal cortex, compared to MBI-. For the SN, MBI+ individuals exhibited reduced FC between the ACC and left anterior insula.

Conclusion

MBI in dementia-free older adults is associated with reduced FC in networks known to be disrupted in dementia. Our results complement the evidence linking MBI with Alzheimer's disease biomarkers.

Highlights

Resting-state functional magnetic resonance imaging was completed in 95 dementia-free persons from FAVR and COMPASS-ND studies.Participants were stratified by informant-rated Mild Behavioral Impairment Checklist (MBI-C) score, ≥6 for MBI+.MBI+ participants showed reduced functional connectivity (FC) within the default mode network and salience network.These FC changes are consistent with those seen in early-stage Alzheimer's disease.MBI may help identify persons with early-stage neurodegenerative disease.

Tai Chi enhances cognitive training effects on delaying cognitive decline in mild cognitive impairment

INTRODUCTION

Cognitive training and physical exercise have shown positive effects on delaying progression of mild cognitive impairment (MCI) to dementia.

METHODS

We explored the enhancing effect from Tai Chi when it was provided with cognitive training for MCI. In the first 12 months, the cognitive training group (CT) had cognitive training, and the mixed group (MixT) had additional Tai Chi training. In the second 12 months, training was only provided for a subgroup of MixT.

RESULTS

In the first 12 months, MixT and CT groups were benefited from training. Compared to the CT group, MixT had additional positive effects with reference to baseline. In addition, Compared to short-time training, prolonged mixed training further delayed decline in global cognition and memory. Functional magnetic resonance imaging showed more increased regional activity in both CT and MixT.

DISCUSSION

Tai Chi enhanced cognitive training effects in MCI. Moreover, Tai Chi and cognitive mixed training showed effects on delaying cognitive decline.

Can hippocampal subfield measures supply information that could be used to improve the diagnosis of Alzheimer's disease?

The diagnosis of Alzheimer's disease (AD) needs to be improved. We investigated if hippocampal subfield volume measured by structural imaging, could supply information, so that the diagnosis of AD could be improved. In this study, subjects were classified based on clinical, neuropsychological, and amyloid positivity or negativity using PET scans. Data from 478 elderly Korean subjects grouped as cognitively unimpaired β-amyloid-negative (NC), cognitively unimpaired β-amyloid-positive (aAD), mild cognitively impaired β-amyloid-positive (pAD), mild cognitively impaired-specific variations not due to dementia β-amyloid-negative (CIND), severe cognitive impairment β-amyloid-positive (ADD+) and severe cognitive impairment β-amyloid-negative (ADD-) were used. NC and aAD groups did not show significant volume differences in any subfields. The CIND did not show significant volume differences when compared with either the NC or the aAD (except L-HATA). However, pAD showed significant volume differences in Sub, PrS, ML, Tail, GCMLDG, CA1, CA4, HATA, and CA3 when compared with the NC and aAD. The pAD group also showed significant differences in the hippocampal tail, CA1, CA4, molecular layer, granule cells/molecular layer/dentate gyrus, and CA3 when compared with the CIND group. The ADD- group had significantly larger volumes than the ADD+ group in the bilateral tail, SUB, PrS, and left ML. The results suggest that early amyloid depositions in cognitive normal stages are not accompanied by significant bilateral subfield volume atrophy. There might be intense and accelerated subfield volume atrophy in the later stages associated with the cognitive impairment in the pAD stage, which subsequently could drive the progression to AD dementia. Early subfield volume atrophy associated with the β-amyloid burden may be characterized by more symmetrical atrophy in CA regions than in other subfields. We conclude that the hippocampal subfield volumetric differences from structural imaging show promise for improving the diagnosis of Alzheimer's disease.

Neuroimaging Markers for Determining Former American Football Players at Risk for Alzheimer's Disease

NFL players, by virtue of their exposure to traumatic brain injury (TBI), are at higher risk of developing dementia and Alzheimer's disease (AD) than the general population. Early recognition and intervention before the onset of clinical symptoms could potentially avert/delay the long-term consequences of these diseases. Given that AD is thought to have a long pre-clinical incubation period, the aim of the current research was to determine whether former NFL players show evidence of incipient dementia in their structural imaging before diagnosis of AD. To identify neuroimaging markers of AD, against which former NFL players would be compared, we conducted a whole-brain volumetric analysis using a cohort of AD patients (ADNI clinical database) to produce a set of brain regions demonstrating sensitivity to early AD pathology (i.e., the “AD fingerprint”). A group of 46 former NFL players' brain magnetic resonance images were then interrogated using the AD fingerprint, that is, the former NFL subjects were compared volumetrically to AD patients using a T1-weighted magnetization-prepared rapid gradient echo sequence. The FreeSurfer image analysis suite (version 6.0) was used to obtain volumetric and cortical thickness data. The Automated Neuropsychological Assessment Metric-Version 4 was used to assess current cognitive functioning. A total of 55 brain regions demonstrated significant atrophy or ex vacuo dilatation bilaterally in AD patients versus controls. Of the 46 former NFL players, 41% demonstrated a greater than expected number of atrophied/dilated AD regions compared with age-matched controls, presumably reflecting AD pathology.

Predictive Scale for Amyloid PET Positivity Based on Clinical and MRI Variables in Patients with Amnestic Mild Cognitive Impairment

The presence of amyloid-β (Aβ) deposition is considered important in patients with amnestic mild cognitive impairment (aMCI), since they can progress to Alzheimer’s disease dementia. Amyloid positron emission tomography (PET) has been used for detecting Aβ deposition, but its high cost is a significant barrier for clinical usage. Therefore, we aimed to develop a new predictive scale for amyloid PET positivity using easily accessible tools. Overall, 161 aMCI patients were recruited from six memory clinics and underwent neuropsychological tests, brain magnetic resonance imaging (MRI), apolipoprotein E (APOE) genotype testing, and amyloid PET. Among the potential predictors, verbal and visual memory tests, medial temporal lobe atrophy, APOE genotype, and age showed significant differences between the Aβ-positive and Aβ-negative groups and were combined to make a model for predicting amyloid PET positivity with the area under the curve (AUC) of 0.856. Based on the best model, we developed the new predictive scale comprising integers, which had an optimal cutoff score ≥ 3. The new predictive scale was validated in another cohort of 98 participants and showed a good performance with AUC of 0.835. This new predictive scale with accessible variables may be useful for predicting Aβ positivity in aMCI patients in clinical practice.

Automatized FACEmemory® scoring is related to Alzheimer's disease phenotype and biomarkers in early-onset mild cognitive impairment: the BIOFACE cohort

Background

FACEmemory® is the first computerized, self-administered verbal episodic memory test with voice recognition. It can be conducted under minimal supervision and contains an automatic scoring system to avoid administrator errors. Moreover, it is suitable for discriminating between cognitively healthy and amnestic mild cognitive impairment (MCI) individuals, and it is associated with Alzheimer’s disease (AD) cerebrospinal fluid (CSF) biomarkers. This study aimed to determine whether FACEmemory scoring is related to performance on classical memory tests and to AD biomarkers of brain magnetic resonance imaging (MRI) and CSF in patients with early-onset MCI (EOMCI).

Methods

Ninety-four patients with EOMCI from the BIOFACE study completed FACEmemory, classical memory tests (the Spanish version of the Word Free and Cued Selective Reminding Test -FCSRT-, the Word List from the Wechsler Memory Scale, third edition, and the Spanish version of the Rey–Osterrieth Complex Figure Test), and a brain MRI. Eighty-two individuals also underwent a lumbar puncture.

Results

FACEmemory scoring was moderately correlated with FCSRT scoring. With regard to neuroimaging MRI results, worse execution on FACEmemory was associated with lower cortical volume in the right prefrontal and inferior parietal areas, along with the left temporal and associative occipital areas. Moreover, the total FACEmemory score correlated with CSF AD biomarkers (Aβ1-42/Aβ1-40 ratio, p181-tau, and Aβ1-42/p181-tau ratio). When performance on FACEmemory was compared among the ATN classification groups, significant differences between the AD group and normal and SNAP groups were found.

Conclusions

FACEmemory is a promising tool for detecting memory deficits sensitive to early-onset AD, but it also allows the detection of memory-impaired cases due to other etiologies. Our findings suggest that FACEmemory scoring can detect the AD endophenotype and that it is also associated with AD-related changes in MRI and CSF in patients with EOMCI. The computerized FACEmemory tool might be an opportunity to facilitate early detection of MCI in younger people than 65, who have a growing interest in new technologies.

Modeling Brain Volume Using Deep Learning-Based Physical Activity Features in Patients With Dementia

There is a proven correlation between the severity of dementia and reduced brain volumes. Several studies have attempted to use activity data to estimate brain volume as a means of detecting reduction early; however, raw activity data are not directly interpretable and are unstructured, making them challenging to utilize. Furthermore, in the previous research, brain volume estimates were limited to total brain volume and the investigators were unable to detect reductions in specific regions of the brain that are typically used to characterize disease progression. We aimed to evaluate volume prediction of 116 brain regions through activity data obtained combining time-frequency domain- and unsupervised deep learning-based feature extraction methods. We developed a feature extraction model based on unsupervised deep learning using activity data from the National Health and Nutrition Examination Survey (NHANES) dataset (n = 14,482). Then, we applied the model and the time-frequency domain feature extraction method to the activity data of the Biobank Innovations for chronic Cerebrovascular disease With ALZheimer's disease Study (BICWALZS) datasets (n = 177) to extract activity features. Brain volumes were calculated from the brain magnetic resonance imaging of the BICWALZS dataset and anatomically subdivided into 116 regions. Finally, we fitted linear regression models to estimate each regional volume of the 116 brain areas based on the extracted activity features. Regression models were statistically significant for each region, with an average correlation coefficient of 0.990 ± 0.006. In all brain regions, the correlation was > 0.964. Particularly, regions of the temporal lobe that exhibit characteristic atrophy in the early stages of Alzheimer's disease showed the highest correlation (0.995). Through a combined deep learning-time-frequency domain feature extraction method, we could extract activity features based solely on the activity dataset, without including clinical variables. The findings of this study indicate the possibility of using activity data for the detection of neurological disorders such as Alzheimer's disease.

Fiber Ball White Matter Modeling Reveals Microstructural Alterations in Healthy Brain Aging

Age-related white matter degeneration is characterized by myelin breakdown and neuronal fiber loss that preferentially occur in regions that myelinate later in development. Conventional diffusion MRI (dMRI) has demonstrated age-related increases in diffusivity but provide limited information regarding the tissue-specific changes driving these effects. A recently developed dMRI biophysical modeling technique, Fiber Ball White Matter (FBWM) modeling, offers enhanced biological interpretability by estimating microstructural properties specific to the intra-axonal and extra-axonal spaces. We used FBWM to illustrate the biological mechanisms underlying changes throughout white matter in healthy aging using data from 63 cognitively unimpaired adults ages 45-85 with no radiological evidence of neurodegeneration or incipient Alzheimer's disease. Conventional dMRI and FBWM metrics were computed for two late-myelinating (genu of the corpus callosum and association tracts) and two early-myelinating regions (splenium of the corpus callosum and projection tracts). We examined the associations between age and these metrics in each region and tested whether age was differentially associated with these metrics in late- vs. early-myelinating regions. We found that conventional metrics replicated patterns of age-related increases in diffusivity in late-myelinating regions. FBWM additionally revealed specific intra- and extra-axonal changes suggestive of myelin breakdown and preferential loss of smaller-diameter axons, yielding in vivo corroboration of findings from histopathological studies of aged brains. These results demonstrate that advanced biophysical modeling approaches, such as FBWM, offer novel information about the microstructure-specific alterations contributing to white matter changes in healthy aging. These tools hold promise as sensitive indicators of early pathological changes related to neurodegenerative disease.

Microbleeds and Medial Temporal Atrophy Determine Cognitive Trajectories in Normal Aging: A Longitudinal PET-MRI Study

BACKGROUND

The cognitive trajectories in normal aging may be affected by medial temporal atrophy (MTA) and amyloid burden, as well as vascular pathologies such as cortical microbleeds (CMB) and white matter hyperintensities (WMH).

OBJECTIVE

We addressed here the role of imaging markers in their prediction in a real-world situation.

METHODS

We performed a 4.5-year longitudinal study in 90 older community-dwellers coupling two neuropsychological assessments, MTA estimated with the Schelten's scale, number of CMB, and WMH evaluated with the Fazekas score at inclusion and follow-up, visual rating of amyloid PET and glucose hypometabolism at follow-up, and APOE genotyping. Regression models were built to explore the association between the continuous cognitive score (CCS) and imaging parameters.

RESULTS

The number of strictly lobar CMB at baseline (4 or more) was related to a 5.5-fold increase of the risk of cognitive decrement. This association persisted in multivariable models explaining 10.6% of the CCS decrease variance. MTA, and Fazekas score at baseline and amyloid positivity or abnormal FDG PET, were not related to the cognitive outcome. The increase of right MTA at follow-up was the only correlate of CCS decrease both in univariate and multivariable models explaining 9.2% of its variance.

CONCLUSION

The present data show that the accumulation of more than four CMB is associated with significant cognitive decrement over time in highly educated elderly persons. They also reveal that the progressive deterioration of cognitive performance within the age-adjusted norms is also related to the increase of visually assessed MTA.

Improving wellbeing of community-dwelling people with mild cognitive impairment: the SENIOR (SystEm of Nudge theory based ICT applications for OldeR citizens) project

Population aging with longer life expectancy represents one of the most relevant challenges of the next future, also because of a significant proportion of older adult people may present with dementia. Motivating senior citizens with mild cognitive impairment to maintain their independence and functional abilities, improve health status and quality of life as well as social interactions, constitutes the main target of preventive medicine. According to a nudge theoretical approach, the SENIOR (SystEm of Nudge theory based ICT applications for OldeR citizens) project- developed thanks to the collaboration among Catholic University of the Sacred Heart, Bicocca University and IRCCS Auxiologico Institute in Milan (Italy) - has been designed to adopt an advanced information and communication technology coaching system able to collect and integrate physiological, psychological and behavioral data, with the final aim of interacting with community-dwelling elderly people suffering from mild cognitive impairment and of providing them personalized feedback on lifestyle management. The SENIOR project proposes to use a smart-watch app for alerting family doctors, sharing information with family members in specific cases and monitoring patients at higher risk in hospital Units, in order to ameliorate health of senior citizens with mild cognitive impairment.

Ultrasonic Assessment of the Medial Temporal Lobe Tissue Displacements in Alzheimer’s Disease

We aim to estimate brain tissue displacements in the medial temporal lobe (MTL) using backscattered ultrasound radiofrequency (US RF) signals, and to assess the diagnostic ability of brain tissue displacement parameters for the differentiation of patients with Alzheimer’s disease (AD) from healthy controls (HC). Standard neuropsychological evaluation and transcranial sonography (TCS) for endogenous brain tissue motion data collection are performed for 20 patients with AD and for 20 age- and sex-matched HC in a prospective manner. Essential modifications of our previous method in US waveform parametrization, raising the confidence of micrometer-range displacement signals in the presence of noise, are done. Four logistic regression models are constructed, and receiver operating characteristic (ROC) curve analyses are applied. All models have cut-offs from 61.0 to 68.5% and separate AD patients from HC with a sensitivity of 89.5% and a specificity of 100%. The area under a ROC curve of predicted probability in all models is excellent (from 95.2 to 95.7%). According to our models, AD patients can be differentiated from HC by a sharper morphology of some individual MTL spatial point displacements (i.e., by spreading the spectrum of displacements to the high-end frequencies with higher variability across spatial points within a region), by lower displacement amplitude differences between adjacent spatial points (i.e., lower strain), and by a higher interaction of these attributes.

One-year Outcome of Shanghai Mild Cognitive Impairment Cohort Study

BACKGROUND & OBJECTIVE

The purpose of this study is to identify the risk factors associated with the conversion from Mild Cognitive Impairment (MCI) to Alzheimer's Disease (AD) dementia for the early detection of AD.

METHODS

The study comprised a prospective cohort study that included 400 MCI subjects with annual follow-ups for 3 years.

RESULTS

During the first 12 months' follow-up, 42 subjects converted to Alzheimer's dementia (21 probable AD and 21 possible AD), two subjects converted to other types of dementia and 56 subjects lost follow. The factors associated with a greater risk of conversion from MCI to AD included gender, whole brain volume, and right hippocampal volume (rt. HV), as well as scores on the Revised Chinese version of the Alzheimer's Disease Assessment Scale-Cognitive subscale 13 (ADAS-Cog-C), Clock Drawing Test (CDT), Symbol Digit Modalities Test (SDMT), and Rey-Osterrieth Complex Figure Test (ROCFT). The risk classification of the combined ADAS-Cog-C and Alzheimer Cognitive Composite (ACC) score with the rt. HV and left Entorhinal Cortex Volume (lt. ECV) showed a conversion difference among the groups.

CONCLUSION

Early detection of AD and potential selection for clinical trial design should utilize the rt. HV, as well as neuropsychological test scores, including those of the ADAS-Cog-C and ACC.

Multi-View Based Multi-Model Learning for MCI Diagnosis

Mild cognitive impairment (MCI) is the early stage of Alzheimer’s disease (AD). Automatic diagnosis of MCI by magnetic resonance imaging (MRI) images has been the focus of research in recent years. Furthermore, deep learning models based on 2D view and 3D view have been widely used in the diagnosis of MCI. The deep learning architecture can capture anatomical changes in the brain from MRI scans to extract the underlying features of brain disease. In this paper, we propose a multi-view based multi-model (MVMM) learning framework, which effectively combines the local information of 2D images with the global information of 3D images. First, we select some 2D slices from MRI images and extract the features representing 2D local information. Then, we combine them with the features representing 3D global information learned from 3D images to train the MVMM learning framework. We evaluate our model on the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. The experimental results show that our proposed model can effectively recognize MCI through MRI images (accuracy of 87.50% for MCI/HC and accuracy of 83.18% for MCI/AD).

Frontal-executive dysfunction affects dementia conversion in patients with amnestic mild cognitive impairment

Among mild cognitive impairment (MCI) patients, those with memory impairment (amnestic MCI, aMCI) are at a high risk of dementia. However, the precise cognitive domain, beside memory, that predicts dementia conversion is unclear. Therefore, we investigated the cognitive domain that predicts dementia conversion in a longitudinal aMCI cohort. We collected data of 482 aMCI patients who underwent neuropsychological tests and magnetic resonance imaging at baseline and were followed for at least 1 year. The patients were categorized according to number (1-4) and type of impaired cognitive domains (memory, language, visuospatial, and frontal-executive function). We evaluated dementia conversion risk in each group when compared to single-domain aMCI after controlling for age, education, diabetes and dyslipidemia. Baseline cortical thickness of each group was compared to that of 410 cognitively normal controls (NCs) after controlling for age, intracranial volume, diabetes and dyslipidemia. Compared to single-domain aMCI, aMCI patients with frontal-executive dysfunction at baseline had a higher risk of dementia conversion than aMCI patients with visuospatial or language dysfunction. Compared to NCs, aMCI patients with frontal-executive dysfunction had overall cortical thinning including frontal areas. Our findings suggest that aMCI patients with frontal-executive dysfunction have poor prognosis and,thus, should be considered for intervention therapy with a higher priority among aMCI patients.

Viewpoint on the role of tissue maintenance in ageing: focus on biomarkers of bone, cartilage, muscle, and brain tissue maintenance

Specific hallmarks are thought to underlie the ageing process and age-related functional decline. In this viewpoint, we put forward the hypothesis that disturbances in the process of tissue maintenance are an important common denominator that may lie in between specific hallmarks of ageing (i.e. damage and responses to damage) and their ultimate (patho)physiological consequences (i.e. functional decline and age-related disease). As a first step towards verifying or falsifying this hypothesis, it will be important to measure biomarkers of tissue maintenance in future studies in different study populations. The main aim of the current paper is to discuss potential biomarkers of tissue maintenance that could be used in such future studies. Among the many tissues that could have been chosen to explore our hypothesis, to keep the paper manageable, we chose to focus on a selected number of tissues, namely bone, cartilage, muscle, and the brain, which are important for mobility and cognition and affected in several common age-related diseases, including osteoporosis, osteoarthritis, sarcopenia, and neurodegenerative diseases. Furthermore, we discuss the advantages and limitations of potential biomarkers for use in (pre)clinical studies. The proposed biomarkers should be validated in future research, for example by measuring these in humans with different rates of ageing.

Association of Bilateral Salpingo-Oophorectomy Before Menopause Onset With Medial Temporal Lobe Neurodegeneration

Question

Do women who underwent bilateral salpingo-oophorectomy before menopause show greater medial temporal lobe structural changes, β-amyloid accumulation, and white matter lesion load on neuroimaging later in life compared with a control group?

Findings

In this case-control study, women with bilateral salpingo-oophorectomy before menopause had smaller amygdala volumes, thinner parahippocampal-entorhinal cortices, and lower entorhinal white matter fractional anisotropy values compared with control participants.

Meaning

Abrupt hormonal changes associated with bilateral salpingo-oophorectomy in premenopausal women may lead to medial temporal lobe structural abnormalities later in life; because alterations in structural imaging biomarkers of the medial temporal lobe neurodegeneration may precede clinical symptoms of dementia, longitudinal follow-up of this cohort with cognitive testing is necessary.

Importance

There is an increased risk of cognitive impairment or dementia in women who undergo bilateral salpingo-oophorectomy (BSO) before menopause. However, data are lacking on the association of BSO before menopause with imaging biomarkers that indicate medial temporal lobe neurodegeneration and Alzheimer disease pathophysiology.

Objective

To investigate medial temporal lobe structure, white matter lesion load, and β-amyloid deposition in women who underwent BSO before age 50 years and before reaching natural menopause.

Design, Setting, and Participants

This nested case-control study of women in the population-based Mayo Clinic Cohort Study of Oophorectomy and Aging-2 (MOA-2) and in the Mayo Clinic Study of Aging (MCSA) in Olmsted County, Minnesota, included women who underwent BSO from 1988 through 2007 and a control group from the intersection of the 2 cohorts. Women who underwent BSO and control participants who underwent a neuropsychological evaluation, magnetic resonance imaging (MRI), and Pittsburgh compound B positron emission tomography (PiB-PET) were included in the analysis. Data analysis was performed from November 2017 to August 2018.

Exposure

Bilateral salpingo-oophorectomy in premenopausal women who were younger than 50 years.

Main Outcomes and Measures

Cortical β-amyloid deposition on PiB-PET scan was calculated using the standard uptake value ratio. White matter hyperintensity volume and biomarkers for medial temporal lobe neurodegeneration (eg, amygdala volume, hippocampal volume, and parahippocampal-entorhinal cortical thickness) on structural MRI and entorhinal white matter fractional anisotropy on diffusion tensor MRI were also measured.

Results

Forty-one women who underwent BSO and 49 control participants were recruited. One woman was excluded from the BSO group after diagnosis of an ovarian malignant condition, and 6 women were excluded from the control group after undergoing BSO after enrollment. Twenty control participants and 23 women who had undergone BSO completed all examinations. The median (interquartile range [IQR]) age at imaging was 65 (62-68) years in the BSO group and 63 (60-66) years in the control group. Amygdala volume was smaller in the BSO group (median [IQR], 1.74 [1.59-1.91] cm³) than the control group (2.15 [2.05-2.37] cm³; P < .001). The parahippocampal-entorhinal cortex was thinner in the BSO group (median [IQR], 3.91 [3.64-4.00] mm) than the control group (3.97 [3.89-4.28] mm; P = .046). Entorhinal white matter fractional anisotropy was lower in the BSO group (median [IQR], 0.19 [0.18-0.22]) than the control group (0.22 [0.20-0.23]; P = .03). Women were treated with estrogen in both groups (BSO, n = 22 of 23 [96%]; control, n = 10 of 19 [53%]). Global cognitive status test results did not differ between the groups.

Conclusions and Relevance

Abrupt hormonal changes associated with BSO in premenopausal women may lead to medial temporal lobe structural abnormalities later in life. Longitudinal evaluation is needed to determine whether cognitive decline follows.

Low-dose ladostigil for mild cognitive impairment: A phase 2 placebo-controlled clinical trial

OBJECTIVE

Ladostigil reduces oxidative stress and microglial activation in aging rats. We assessed its safety and potential efficacy in a 3-year, randomized, double-blind, placebo-controlled phase 2 clinical trial in patients with mild cognitive impairment (MCI) and medial temporal lobe atrophy.

METHODS

Patients 55 to 85 years of age with MCI, Clinical Dementia Rating (CDR) score of 0.5, Mini-Mental State Examination (MMSE) score >24, Wechsler Memory Scale-Revised Verbal Paired Associates I score ≤18, and Medial Temporal Lobe Atrophy Scale score >1 were stratified by APOE ε4 genotype and randomly assigned (1:1) to ladostigil 10 mg/d or placebo. Primary outcomes were safety and onset of Alzheimer disease dementia. Secondary endpoints were Neuropsychological Test Battery (NTB) composite, Disability Assessment in Dementia (DAD), and Geriatric Depression Scale (GDS) scores. Exploratory outcomes were NTB component, CDR, and MMSE scores. Biomarkers included MRI-derived whole-brain, hippocampus, and entorhinal cortex volumes.

RESULTS

Two hundred ten patients from 15 sites in Austria, Germany, and Israel were randomly allocated to placebo (107 patients) or ladostigil (103 patients). After 36 months, 21 of 103 patients on placebo and 14 of 99 patients receiving ladostigil progressed to Alzheimer disease (log-rank test p = 0.162). There were no significant effects on the NTB composite, DAD, or GDS score. Whole-brain and hippocampus volumes decreased more in the placebo than in the ladostigil group (whole brain, p = 0.025, Cohen d = 0.43; hippocampus, p = 0.043, d = 0.43). Serious adverse events were reported by 28 of 107 patients treated with placebo and 26 of 103 with ladostigil.

CONCLUSION

Ladostigil was safe and well tolerated but did not delay progression to dementia. Its association with reduced brain and hippocampus volume loss suggests a potential effect on atrophy.

CLINICALTRIALSGOV IDENTIFIER

NCT01429623.

CLASSIFICATION OF EVIDENCE

This study provides Class II evidence that for patients with MCI and medial temporal lobe atrophy, ladostigil did not significantly decrease the risk of the development of Alzheimer disease.

Medial temporal lobe atrophy and posterior atrophy scales normative values

OBJECTIVES

The medial temporal lobe atrophy (MTA) and the posterior atrophy (PA) scales allow to assess the degree hippocampal and parietal atrophy from magnetic resonance imaging (MRI) scans. Despite reliable, easy and widespread employment, appropriate normative values are still missing. We aim to provide norms for the Italian population.

METHODS

Two independent raters assigned the highest MTA and PA score between hemispheres, based on 3D T1-weighted MRI of 936 Italian Brain Normative Archive subjects (age: mean ± SD: 50.2 ± 14.7, range: 20-84; MMSE>26 or CDR = 0). The inter-rater agreement was assessed with the absolute intraclass correlation coefficient (aICC). We assessed the association between MTA and PA scores and sociodemographic features and APOE status, and normative data were established by age decade based on percentile distributions.

RESULTS

Raters agreed in 90% of cases for MTA (aICC = 0.86; 95% CI = 0.69-0.98) and in 86% for PA (aICC = 0.82; 95% CI = 0.58-0.98). For both rating scales, score distribution was skewed, with MTA = 0 in 38% of the population and PA = 0 in 52%, while a score ≥ 2 was only observed in 12% for MTA and in 10% for PA. Median denoted overall hippocampal (MTA: median = 1, IQR = 0-1) and parietal (PA: median = 0, IQR = 0-1) integrity. The 90th percentile of the age-specific distributions increased from 1 (at age 20-59) for both scales, to 2 for PA over age 60, and up to 4 for MTA over age 80. Gender, education and APOE status did not significantly affect the percentile distributions in the whole sample, nor in the subset over age 60.

CONCLUSIONS

Our normative data for the MTA and PA scales are consistent with previous studies and overcome their main limitations (in particular uneven representation of ages and missing percentile distributions), defining the age-specific norms to be considered for proper brain atrophy assessment.

A comparative study: visual rating scores and the voxel-based specific regional analysis system for Alzheimer's disease on magnetic resonance imaging among subjects with Alzheimer's disease, mild cognitive impairment, and normal cognition

AIM

Hippocampal atrophy shown on magnetic resonance imaging can differentiate Alzheimer's disease (AD) patients from subjects with normal cognition (NC). Simplified automated methods that use volumetric analysis, such as as the voxel-based specific regional analysis system for AD, have become widely used in Japan. However, the diagnostic value of the voxel-based specific regional analysis system compared with visual rating scores for clinical diagnosis is unclear.

METHODS

Study participants consisted of 37 AD patients, 29 mild cognitive impairment (MCI) patients, and 21 NC subjects. All participants underwent neuropsychological testing and magnetic resonance imaging. The imaging was scored visually for regional brain atrophy by two raters based on a newly developed visual rating score. The voxel-based specific regional analysis system for AD scores were calculated with the analysis system's advanced software. We analyzed whether these scores aid in discriminating among AD, MCI, and NC.

RESULTS

The AD group had significantly different visual rating scores, regional analysis scores, and all neuropsychological test scores than the NC group. The AD group had significantly different visual rating scores than the MCI group, and a significant difference was observed between the MCI and NC groups on regional analysis scores. Both the visual rating and regional analysis scores showed equivalent correlations with the neuropsychological test scores.

CONCLUSIONS

Both the visual rating and regional analysis scores are clinically useful tools for differentiating among AD, MCI, and NC.

Protective Effects of Indian Spice Curcumin Against Amyloid-β in Alzheimer's Disease

The purpose of our article is to assess the current understanding of Indian spice, curcumin, against amyloid-β (Aβ)-induced toxicity in Alzheimer's disease (AD) pathogenesis. Natural products, such as ginger, curcumin, and gingko biloba have been used as diets and dietary supplements to treat human diseases, including cancer, cardiovascular, respiratory, infectious, diabetes, obesity, metabolic syndromes, and neurological disorders. Products derived from plants are known to have protective effects, including anti-inflammatory, antioxidant, anti-arthritis, pro-healing, and boosting memory cognitive functions. In the last decade, several groups have designed and synthesized curcumin and its derivatives and extensively tested using cell and mouse models of AD. Recent research on Aβ and curcumin has revealed that curcumin prevents Aβ aggregation and crosses the blood-brain barrier, reach brain cells, and protect neurons from various toxic insults of aging and Aβ in humans. Recent research has also reported that curcumin ameliorates cognitive decline and improves synaptic functions in mouse models of AD. Further, recent groups have initiated studies on elderly individuals and patients with AD and the outcome of these studies is currently being assessed. This article highlights the beneficial effects of curcumin on AD. This article also critically assesses the current limitations of curcumin's bioavailability and urgent need for new formulations to increase its brain levels to treat patients with AD.

Predictors of neurodegeneration differ between cognitively normal and subsequently impaired older adults

Effects of Alzheimer's disease (AD) risk factors on brain volume changes may partly explain what happens during the preclinical AD stage in people who develop subsequent cognitive impairment (SI). We investigated predictors of neurodegeneration, measured by MRI-based volume loss, in older adults before diagnosis of cognitive impairment. There were 623 cognitively normal and 65 SI Baltimore Longitudinal Study of Aging participants (age 55-92 years) enrolled in the neuroimaging substudy from 1994 to 2015. Mixed-effects regression was used to assess the associations of AD risk factors (age, APOE e4 carrier status, diabetes, hypertension, obesity, current smoking, and elevated cholesterol) with brain regional volume change among the overall sample and by diagnostic status. Older age, APOE e4 carrier status, hypertension, and HDL cholesterol were predictors of volumetric change. Among SI participants only, hypertension, obesity, and APOE e4 carrier status were associated with greater declines in selected brain regions. SI individuals in the preclinical AD stage are vulnerable to risk factors that have either a protective or null effect in cognitively normal individuals.

Multi-Objective Genetic Algorithms to Find Most Relevant Volumes of the Brain Related to Alzheimer's Disease and Mild Cognitive Impairment

Computer-Aided Diagnosis (CAD) represents a relevant instrument to automatically classify between patients with and without Alzheimer's Disease (AD) using several actual imaging techniques. This study analyzes the optimization of volumes of interest (VOIs) to extract three-dimensional (3D) textures from Magnetic Resonance Image (MRI) in order to diagnose AD, Mild Cognitive Impairment converter (MCIc), Mild Cognitive Impairment nonconverter (MCInc) and Normal subjects. A relevant feature of the proposed approach is the use of 3D features instead of traditional two-dimensional (2D) features, by using 3D discrete wavelet transform (3D-DWT) approach for performing feature extraction from T-1 weighted MRI. Due to the high number of coefficients when applying 3D-DWT to each of the VOIs, a feature selection algorithm based on mutual information is used, as is the minimum Redundancy Maximum Relevance (mRMR) algorithm. Region optimization has been performed in order to discover the most relevant regions (VOIs) in the brain with the use of Multi-Objective Genetic Algorithms, being one of the objectives to be optimize the accuracy of the system. The error index of the system is computed by the confusion matrix obtained by the multi-class support vector machine (SVM) classifier. Principal Component Analysis (PCA) is used with the purpose of reducing the number of features to the classifier. The cohort of subjects used in the study consisted of 296 different patients. A first group of 206 patients was used to optimize VOI selection and another group of 90 independent subjects (that did not belong to the first group) was used to test the solutions yielded by the genetic algorithm. The proposed methodology obtains excellent results in multi-class classification achieving accuracies of 94.4% and also extracting significant information on the location of the most relevant points of the brain. This suggests that the proposed method could aid in the research of other neurodegenerative diseases, improving the accuracy of the diagnosis and finding the most relevant regions of the brain associated with them.

Visual Evaluation of Medial Temporal Lobe Atrophy as a Clinical Marker of Conversion from Mild Cognitive Impairment to Dementia and for Predicting Progression in Patients with Mild Cognitive Impairment and Mild Alzheimer's Disease

BACKGROUND/AIMS

To evaluate whether visual assessment of medial temporal lobe atrophy (vaMTA) can predict 2-year conversion from mild cognitive impairment (MCI) to dementia and progression of MCI and Alzheimer's disease dementia as measured by the Clinical Dementia Rating Scale Sum of Boxes score (CDR-SB).

METHODS

vaMTA was performed in 94 patients with MCI according to the Winblad criteria and in 124 patients with AD according to ICD-10 and NINCDS-ADRDA criteria. Demographic data, the Consortium to Establish a Registry for Alzheimer's Disease 10-word delayed recall, APOE ɛ4 status, Cornell Scale for Depression in Dementia, and comorbid hypertension were used as covariates.

RESULTS

vaMTA was associated with MCI conversion in an unadjusted model but not in an adjusted model (p = 0.075), where delayed recall and APOE ɛ4 status were significant predictors. With CDR-SB change as the outcome, an interaction between vaMTA and diagnosis was found, but in the adjusted model only delayed recall and age were significant predictors. For vaMTA below 2, the association between vaMTA and CDR-SB change differed between diagnostic groups. Similar results were found based on a trajectory analysis.

CONCLUSION

In adjusted models, memory function, APOE ɛ4 status and age were significant predictors of disease progression, not vaMTA. The association between vaMTA and CDR-SB change was different in patients with MCI and Alzheimer's disease dementia.

Classification of Alzheimer's Disease Using Whole Brain Hierarchical Network

Regions of interest (ROIs) based classification has been widely investigated for analysis of brain magnetic resonance imaging (MRI) images to assist the diagnosis of Alzheimer's disease (AD) including its early warning and developing stages, e.g., mild cognitive impairment (MCI) including MCI converted to AD (MCIc) and MCI not converted to AD (MCInc). Since an ROI representation of brain structures is obtained either by pre-definition or by adaptive parcellation, the corresponding ROI in different brains can be measured. However, due to noise and small sample size of MRI images, representations generated from single or multiple ROIs may not be sufficient to reveal the underlying anatomical differences between the groups of disease-affected patients and health controls (HC). In this paper, we employ a whole brain hierarchical network (WBHN) to represent each subject. The whole brain of each subject is divided into 90, 54, 14, and 1 regions based on Automated Anatomical Labeling (AAL) atlas. The connectivity between each pair of regions is computed in terms of Pearson's correlation coefficient and used as classification feature. Then, to reduce the dimensionality of features, we select the features with higher scores. Finally, we use multiple kernel boosting (MKBoost) algorithm to perform the classification. Our proposed method is evaluated on MRI images of 710 subjects (200 AD, 120 MCIc, 160 MCInc, and 230 HC) from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. The experimental results show that our proposed method achieves an accuracy of 94.65 percent and an area under the receiver operating characteristic (ROC) curve (AUC) of 0.954 for AD/HC classification, an accuracy of 89.63 percent and an AUC of 0.907 for AD/MCI classification, an accuracy of 85.79 percent and an AUC of 0.826 for MCI/HC classification, and an accuracy of 72.08 percent and an AUC of 0.716 for MCIc/MCInc classification, respectively. Our results demonstrate that our proposed method is efficient and promising for clinical applications for the diagnosis of AD via MRI images.

Magnetic Resonance Imaging and Anatomical Correlation of Human Temporal Lobe Landmarks, in 3D Euclidean Space: A Study of Control and Alzheimer's Disease Subjects

BACKGROUND

The medial temporal lobe (MTL), and in particular the hippocampal formation, is essential in the processing and consolidation of declarative memory. The 3D environment of the anatomical structures contained in the MTL is an important issue.

OBJECTIVE

Our aim was to explore the spatial relationship of the anatomical structures of the MTL and changes in aging and/or Alzheimer's disease (AD).

METHODS

MTL anatomical landmarks are identified and registered to create a 3D network. The brain network is quantitatively described as a plane, rostrocaudally-oriented, and presenting Euclidean/real distances. Correspondence between 1.5T RM, 3T RM, and histological sections were assessed to determine the most important recognizable changes in AD, based on statistical significance.

RESULTS

In both 1.5T and 3T RM images and histology, inter-rater reliability was high. Sex and hemisphere had no influence on network pattern. Minor changes were found in relation to aging. Distances from the temporal pole to the dentate gyrus showed the most significant differences when comparing control and AD groups. The best discriminative distance between control and AD cases was found in the temporal pole/dentate gyrus rostrocaudal length in histological sections. Moreover, more distances between landmarks were required to obtain 100% discrimination between control (divided into <65 years or >65 years) and AD cases.

DISCUSSION

Changes in the distance between MTL anatomical landmarks can successfully be detected by using measurements of 3D network patterns in control and AD cases.

Clinical validity of medial temporal atrophy as a biomarker for Alzheimer's disease in the context of a structured 5-phase development framework

Research criteria for Alzheimer's disease recommend the use of biomarkers for diagnosis, but whether biomarkers improve the diagnosis in clinical routine has not been systematically assessed. The aim is to evaluate the evidence for use of medial temporal lobe atrophy (MTA) as a biomarker for Alzheimer's disease at the mild cognitive impairment stage in routine clinical practice, with an adapted version of the 5-phase oncology framework for biomarker development. A literature review on visual assessment of MTA and hippocampal volumetry was conducted with other biomarkers addressed in parallel reviews. Ample evidence is available for phase 1 (rationale for use) and phase 2 (discriminative ability between diseased and control subjects). Phase 3 (early detection ability) is partly achieved: most evidence is derived from research cohorts or clinical populations with short follow-up, but validation in clinical mild cognitive impairment cohorts is required. In phase 4, only the practical feasibility has been addressed for visual rating of MTA. The rest of phase 4 and phase 5 have not yet been addressed.

The effect of hippocampal function, volume and connectivity on posterior cingulate cortex functioning during episodic memory fMRI in mild cognitive impairment

Objectives

Diminished function of the posterior cingulate cortex (PCC) is a typical finding in early Alzheimer’s disease (AD). It is hypothesized that in early stage AD, PCC functioning relates to or reflects hippocampal dysfunction or atrophy. The aim of this study was to examine the relationship between hippocampus function, volume and structural connectivity, and PCC activation during an episodic memory task-related fMRI study in mild cognitive impairment (MCI).

Method

MCI patients (n = 27) underwent episodic memory task-related fMRI, 3D-T1w MRI, 2D T2-FLAIR MRI and diffusion tensor imaging. Stepwise linear regression analysis was performed to examine the relationship between PCC activation and hippocampal activation, hippocampal volume and diffusion measures within the cingulum along the hippocampus.

Results

We found a significant relationship between PCC and hippocampus activation during successful episodic memory encoding and correct recognition in MCI patients. We found no relationship between the PCC and structural hippocampal predictors.

Conclusions

Our results indicate a relationship between PCC and hippocampus activation during episodic memory engagement in MCI. This may suggest that during episodic memory, functional network deterioration is the most important predictor of PCC functioning in MCI.

Key Points

• PCC functioning during episodic memory relates to hippocampal functioning in MCI.

• PCC functioning during episodic memory does not relate to hippocampal structure in MCI.

• Functional network changes are an important predictor of PCC functioning in MCI.

Electronic supplementary material

The online version of this article (doi:10.1007/s00330-017-4768-1) contains supplementary material, which is available to authorized users.

Association between naturally occurring anti-amyloid β autoantibodies and medial temporal lobe atrophy in Alzheimer's disease

BACKGROUND

Naturally occurring autoantibodies against amyloid β (Aβ) peptide exist in the serum and cerebrospinal fluid (CSF) of healthy individuals. Recently, it was reported that administration of intravenous immunoglobulin at the mild cognitive impairment (MCI) stage of Alzheimer's disease (AD) reduces brain atrophy.

OBJECTIVE

To examine the association between naturally occurring anti-Aβ autoantibodies and brain atrophy in patients with cognitive impairment.

METHODS

Serum and CSF levels of anti-Aβ autoantibodies and CSF biomarkers were evaluated in 68 patients with cognitive impairment, comprising 44 patients with AD, 19 patients with amnestic MCI and five patients with non-Alzheimer's dementia. The degree of brain atrophy was assessed using the voxel-based specific regional analysis system for AD, which targets the volume of interest (VOI) in medial temporal structures, including the whole hippocampus, entorhinal cortex and amygdala.

RESULTS

CSF levels of anti-Aβ autoantibodies were inversely correlated with the extent and severity of VOI atrophy, and the ratio of VOI/grey matter atrophy in patients with AD, but not in MCI or non-AD patients. Serum levels of anti-Aβ autoantibodies were not associated with these parameters in any of the patient groups.

CONCLUSIONS

These results indicate that CSF levels of naturally occurring anti-Aβ autoantibodies are inversely associated with the degree of the VOI atrophy in patients with AD. Although the mechanism is unclear, CSF levels of naturally occurring anti-Aβ autoantibodies may be implicated in the progression of atrophy of the whole hippocampus, entorhinal cortex and amygdala, in AD.

Diagnostic Efficacy of Structural MRI in Patients With Mild-to-Moderate Alzheimer Disease: Automated Volumetric Assessment Versus Visual Assessment

OBJECTIVE

The purpose of this study was to compare the diagnostic efficacies of an automated volumetric assessment tool and visual assessment in the evaluation of medial temporal lobar atrophy in mild-to-moderate Alzheimer disease (AD).

MATERIALS AND METHODS

This retrospective study included 30 patients with mild-to-moderate AD and 25 age-matched healthy control subjects undergoing MRI with a 3D fast spoiled gradient recalled-echo sequence at 3 T. The images were processed with fully automated volumetric analysis software. To assess medial temporal lobe (MTL) atrophy, two MTL indexes, which took into account the volumes of the hippocampus and the inferior lateral ventricle, were calculated with the automated volumetric assessment software. In addition, two neuroradiologists assessed MTL atrophy visually using the Scheltens scale. ROC curve analysis was used to compare the diagnostic performances of the two methods. The weighted kappa statistic was used to assess the intrarater and interrater reliability of visual inspection.

RESULTS

The automated volumetric assessment tool had moderate sensitivity (63.3%) and high specificity (100%) in differentiating patients with mild-to-moderate AD from control subjects. Visual inspection showed sensitivity of 63.3% and specificity of 92.0%. The diagnostic performance was not significantly different between the two methods (p = 0.536-0.906). Intraobserver reliability for visual inspection was 0.858 and 0.902 for the two reviewers, and interobserver reliability was 0.692-0.780.

CONCLUSION

Both the automated volumetric assessment tool and visual inspection can be used to evaluate MTL atrophy and differentiate patients with AD from healthy individuals with good diagnostic accuracy. Thus, the automated tool can be a useful and efficient adjunct in clinical practice for evaluating MTL atrophy in the diagnosis of AD.

Magnetic resonance spectroscopy and brain volumetry in mild cognitive impairment. A prospective study

OBJECTIVE

To assess the accuracy of magnetic resonance spectroscopy (1H-MRS) and brain volumetry in mild cognitive impairment (MCI) to predict conversion to probable Alzheimer's disease (AD).

METHODS

Forty-eight patients fulfilling the criteria of amnestic MCI who underwent a conventional magnetic resonance imaging (MRI) followed by MRS, and T1-3D on 1.5 Tesla MR unit. At baseline the patients underwent neuropsychological examination. 1H-MRS of the brain was carried out by exploring the left medial occipital lobe and ventral posterior cingulated cortex (vPCC) using the LCModel software. A high resolution T1-3D sequence was acquired to carry out the volumetric measurement. A cortical and subcortical parcellation strategy was used to obtain the volumes of each area within the brain. The patients were followed up to detect conversion to probable AD.

RESULTS

After a 3-year follow-up, 15 (31.2%) patients converted to AD. The myo-inositol in the occipital cortex and glutamate+glutamine (Glx) in the posterior cingulate cortex predicted conversion to probable AD at 46.1% sensitivity and 90.6% specificity. The positive predictive value was 66.7%, and the negative predictive value was 80.6%, with an overall cross-validated classification accuracy of 77.8%. The volume of the third ventricle, the total white matter and entorhinal cortex predict conversion to probable AD at 46.7% sensitivity and 90.9% specificity. The positive predictive value was 70%, and the negative predictive value was 78.9%, with an overall cross-validated classification accuracy of 77.1%. Combining volumetric measures in addition to the MRS measures the prediction to probable AD has a 38.5% sensitivity and 87.5% specificity, with a positive predictive value of 55.6%, a negative predictive value of 77.8% and an overall accuracy of 73.3%.

CONCLUSION

Either MRS or brain volumetric measures are markers separately of cognitive decline and may serve as a noninvasive tool to monitor cognitive changes and progression to dementia in patients with amnestic MCI, but the results do not support the routine use in the clinical settings.

Does MRI Increase the Diagnostic Confidence of Physicians in an Outpatient Memory Clinic

Background and Aim

Data showing the usefulness of MRI to improve the accuracy of the diagnostic process in cognitive disorders were derived from studies in tertiary referral centers. MRI is widely used as a diagnostic tool in everyday practice, but it is unknown what the actual added value of MRI is. We studied the usefulness of MRI in the diagnostic process by measuring the change of confidence of the physician.

Methods

Physicians indicated confidence in their diagnosis before and after presentation of MR images using a visual analogue scale from 0-100%.

Results

Use of MRI increased the level of confidence by 3% in experienced clinicians and by 9% in inexperienced physicians. In 2/125 cases, MRI showed an unexpected finding.

Conclusion

MRI is a useful diagnostic tool in everyday practice of diagnosing cognitive disorders.

Application of Machine Learning to Arterial Spin Labeling in Mild Cognitive Impairment and Alzheimer Disease

Purpose To investigate whether multivariate pattern recognition analysis of arterial spin labeling (ASL) perfusion maps can be used for classification and single-subject prediction of patients with Alzheimer disease (AD) and mild cognitive impairment (MCI) and subjects with subjective cognitive decline (SCD) after using the W score method to remove confounding effects of sex and age. Materials and Methods Pseudocontinuous 3.0-T ASL images were acquired in 100 patients with probable AD; 60 patients with MCI, of whom 12 remained stable, 12 were converted to a diagnosis of AD, and 36 had no follow-up; 100 subjects with SCD; and 26 healthy control subjects. The AD, MCI, and SCD groups were divided into a sex- and age-matched training set (n = 130) and an independent prediction set (n = 130). Standardized perfusion scores adjusted for age and sex (W scores) were computed per voxel for each participant. Training of a support vector machine classifier was performed with diagnostic status and perfusion maps. Discrimination maps were extracted and used for single-subject classification in the prediction set. Prediction performance was assessed with receiver operating characteristic (ROC) analysis to generate an area under the ROC curve (AUC) and sensitivity and specificity distribution. Results Single-subject diagnosis in the prediction set by using the discrimination maps yielded excellent performance for AD versus SCD (AUC, 0.96; P < .01), good performance for AD versus MCI (AUC, 0.89; P < .01), and poor performance for MCI versus SCD (AUC, 0.63; P = .06). Application of the AD versus SCD discrimination map for prediction of MCI subgroups resulted in good performance for patients with MCI diagnosis converted to AD versus subjects with SCD (AUC, 0.84; P < .01) and fair performance for patients with MCI diagnosis converted to AD versus those with stable MCI (AUC, 0.71; P > .05). Conclusion With automated methods, age- and sex-adjusted ASL perfusion maps can be used to classify and predict diagnosis of AD, conversion of MCI to AD, stable MCI, and SCD with good to excellent accuracy and AUC values. ^© RSNA, 2016.

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.

Six psychotropics for pre-symptomatic & early Alzheimer's (MCI), Parkinson's, and Huntington's disease modification

The quest for neuroprotective drugs to slow the progression of neurodegenerative diseases (NDDs), including Alzheimer's disease (AD), Parkinson's disease (PD), and Huntington's disease (HD), has been largely unrewarding. Preclinical evidence suggests that repurposing quetiapine, lithium, valproate, fluoxetine, donepezil, and memantine for early and pre-symptomatic disease-modification in NDDs may be promising and can spare regulatory barriers. The literature of these psychotropics in early stage and pre-symptomatic AD, PD, and HD is reviewed and propitious findings follow. Mild cognitive impairment (MCI) phase of AD: salutary human randomized controlled trial findings for low-dose lithium and, in selected patients, donepezil await replication. Pre-symptomatic AD: human epidemiological data indicate that lithium reduces AD risk. Animal model studies (AMS) reveal encouraging results for quetiapine, lithium, donepezil, and memantine. Early PD: valproate AMS findings show promise. Pre-symptomatic PD: lithium and valproate AMS findings are encouraging. Early HD: uncontrolled clinical data indicate non-progression with lithium, fluoxetine, donepezil, and memantine. Pre-symptomatic HD: lithium and valproate are auspicious in AMS. Many other promising findings awaiting replication (valproate in MCI; lithium, valproate, fluoxetine in pre-symptomatic AD; lithium in early PD; lithium, valproate, fluoxetine in pre-symptomatic PD; donepezil in early HD; lithium, fluoxetine, memantine in pre-symptomatic HD) are reviewed. Dose- and stage-dependent effects are considered. Suggestions for signal-enhancement in human trials are provided for each NDD stage.

Using visual rating to diagnose dementia: a critical evaluation of MRI atrophy scales

Visual rating scales, developed to assess atrophy in patients with cognitive impairment, offer a cost-effective diagnostic tool that is ideally suited for implementation in clinical practice. By focusing attention on brain regions susceptible to change in dementia and enforcing structured reporting of these findings, visual rating can improve the sensitivity, reliability and diagnostic value of radiological image interpretation. Brain imaging is recommended in all current diagnostic guidelines relating to dementia, and recent guidelines have also recommended the application of medial temporal lobe atrophy rating. Despite these recommendations, and the ease with which rating scales can be applied, there is still relatively low uptake in routine clinical assessments. Careful consideration of atrophy rating scales is needed to verify their diagnostic potential and encourage uptake among clinicians. Determining the added value of combining scores from visual rating in different brain regions may also increase the diagnostic value of these tools.

Using magnetic resonance imaging in diagnosing dementia: a Dutch outpatient memory clinics survey

BACKGROUND

In the Netherlands, dementia syndromes are diagnosed in specialized memory outpatient clinics (MC). Many radiologists are not trained to assess magnetic resonance imaging (MRI) scans with respect to possible radiological changes that may indicate neurodegenerative disease.

METHODS

This is a cross-sectional descriptive study. A survey was sent to all Dutch MC and included questions as to how MRI scans are assessed by radiologists and how these assessments are used in the diagnostic process.

RESULTS

In most MC, radiologists report on typical Alzheimer pathology and large vessel disease. Small vessel disease and other anatomical changes signifying neurodegenerative disease frequently are not assessed. In the majority of MC, the radiological assessment is not standardized, and physicians assess MRI for themselves to use this information to discuss the consensus diagnosis subsequently.

CONCLUSION

MRI assessment by radiologists in Dutch MC probably underestimates the presence of cerebrovascular and neurodegenerative disease. The validity of standardized assessment protocols in routine clinical practice deserves further study, as the implementation of standardization outside research settings could improve diagnostic accuracy.

Practical cut-offs for visual rating scales of medial temporal, frontal and posterior atrophy in Alzheimer's disease and mild cognitive impairment

BACKGROUND

Atrophy in the medial temporal lobe, frontal lobe and posterior cortex can be measured with visual rating scales such as the medial temporal atrophy (MTA), global cortical atrophy - frontal subscale (GCA-F) and posterior atrophy (PA) scales, respectively. However, practical cut-offs are urgently needed, especially now that different presentations of Alzheimer's disease (AD) are included in the revised diagnostic criteria.

AIMS

The aim of this study was to generate a list of practical cut-offs for the MTA, GCA-F and PA scales, for both diagnosis of AD and determining prognosis in mild cognitive impairment (MCI), and to evaluate the influence of key demographic and clinical factors on these cut-offs.

METHODS

AddNeuroMed and ADNI cohorts were combined giving a total of 1147 participants (322 patients with AD, 480 patients with MCI and 345 control subjects). The MTA, GCA-F and PA scales were applied and a broad range of cut-offs was evaluated.

RESULTS

The MTA scale showed better diagnostic and predictive performances than the GCA-F and PA scales. Age, apolipoprotein E (ApoE) ε4 status and age at disease onset influenced all three scales. For the age ranges 45-64, 65-74, 75-84 and 85-94 years, the following cut-offs should be used. MTA: ≥1.5, ≥1.5, ≥2 and ≥2.5; GCA-F, ≥1, ≥1, ≥1 and ≥1; and PA, ≥1, ≥1, ≥1 and ≥1, respectively, with an adjustment for early-onset ApoE ε4 noncarrier AD patients (MTA: ≥2, ≥2, ≥3 and ≥3; and GCA-F: ≥1, ≥1, ≥2 and ≥2, respectively).

CONCLUSIONS

If successfully validated in clinical settings, the list of practical cut-offs proposed here might be useful in clinical practice. Their use might also (i) promote research on atrophy subtypes, (ii) increase the understanding of different presentations of AD, (iii) improve diagnosis and prognosis and (iv) aid population selection and enrichment for clinical trials.

Structural Image Analysis of the Brain in Neuropsychology Using Magnetic Resonance Imaging (MRI) Techniques

Magnetic resonance imaging (MRI) of the brain provides exceptional image quality for visualization and neuroanatomical classification of brain structure. A variety of image analysis techniques provide both qualitative as well as quantitative methods to relate brain structure with neuropsychological outcome and are reviewed herein. Of particular importance are more automated methods that permit analysis of a broad spectrum of anatomical measures including volume, thickness and shape. The challenge for neuropsychology is which metric to use, for which disorder and the timing of when image analysis methods are applied to assess brain structure and pathology. A basic overview is provided as to the anatomical and pathoanatomical relations of different MRI sequences in assessing normal and abnormal findings. Some interpretive guidelines are offered including factors related to similarity and symmetry of typical brain development along with size-normalcy features of brain anatomy related to function. The review concludes with a detailed example of various quantitative techniques applied to analyzing brain structure for neuropsychological outcome studies in traumatic brain injury.