Medial temporal atrophy but not memory deficit predicts progression to dementia in patients with mild cognitive impairment

Facial Emotion Recognition in Older Adults With Cognitive Complaints

Background and Purpose

Facial emotion recognition deficits impact the daily life, particularly of Alzheimer's disease patients. We aimed to assess these deficits in the following three groups: subjective cognitive decline (SCD), mild cognitive impairment (MCI), and mild Alzheimer's dementia (AD). Additionally, we explored the associations between facial emotion recognition and cognitive performance.

Methods

We used the Korean version of the Florida Facial Affect Battery (K-FAB) in 72 SCD, 76 MCI, and 76 mild AD subjects. The comparison was conducted using the analysis of covariance (ANCOVA), with adjustments being made for age and sex. The Mini-Mental State Examination (MMSE) was utilized to gauge the overall cognitive status, while the Seoul Neuropsychological Screening Battery (SNSB) was employed to evaluate the performance in the following five cognitive domains: attention, language, visuospatial abilities, memory, and frontal executive functions.

Results

The ANCOVA results showed significant differences in K-FAB subtests 3, 4, and 5 (p=0.001, p=0.003, and p=0.004, respectively), especially for anger and fearful emotions. Recognition of 'anger' in the FAB subtest 5 declined from SCD to MCI to mild AD. Correlations were observed with age and education, and after controlling for these factors, MMSE and frontal executive function were associated with FAB tests, particularly in the FAB subtest 5 (r=0.507, p<0.001 and r=-0.288, p=0.026, respectively).

Conclusions

Emotion recognition deficits worsened from SCD to MCI to mild AD, especially for negative emotions. Complex tasks, such as matching, selection, and naming, showed greater deficits, with a connection to cognitive impairment, especially frontal executive dysfunction.

Stability in cognitive classification as a function of severity of impairment and ethnicity: A longitudinal analysis

OBJECTIVE

The interaction of ethnicity, progression of cognitive impairment, and neuroimaging biomarkers of Alzheimer's Disease remains unclear. We investigated the stability in cognitive status classification (cognitively normal [CN] and mild cognitive impairment [MCI]) of 209 participants (124 Hispanics/Latinos and 85 European Americans).

METHODS

Biomarkers (structural MRI and amyloid PET scans) were compared between Hispanic/Latino and European American individuals who presented a change in cognitive diagnosis during the second or third follow-up and those who remained stable over time.

RESULTS

There were no significant differences in biomarkers between ethnic groups in any of the diagnostic categories. The frequency of CN and MCI participants who were progressors (progressed to a more severe cognitive diagnosis at follow-up) and non-progressors (either stable through follow-ups or unstable [progressed but later reverted to a diagnosis of CN]) did not significantly differ across ethnic groups. Progressors had greater atrophy in the hippocampus (HP) and entorhinal cortex (ERC) at baseline compared to unstable non-progressors (reverters) for both ethnic groups, and more significant ERC atrophy was observed among progressors of the Hispanic/Latino group. For European Americans diagnosed with MCI, there were 60% more progressors than reverters (reverted from MCI to CN), while among Hispanics/Latinos with MCI, there were 7% more reverters than progressors. Binomial logistic regressions predicting progression, including brain biomarkers, MMSE, and ethnicity, demonstrated that only MMSE was a predictor for CN participants at baseline. However, for MCI participants at baseline, HP atrophy, ERC atrophy, and MMSE predicted progression.

Cerebral Small Vessel Disease-Related Dementia: More Questions Than Answers

Cerebral small vessel disease (CSVD) has emerged as a common factor driving age-dependent diseases, including stroke and dementia. CSVD-related dementia will affect a growing fraction of the aging population, requiring improved recognition, understanding, and treatments. This review describes evolving criteria and imaging biomarkers for the diagnosis of CSVD-related dementia. We describe diagnostic challenges, particularly in the context of mixed pathologies and the absence of highly effective biomarkers for CSVD-related dementia. We review evidence regarding CSVD as a risk factor for developing neurodegenerative disease and potential mechanisms by which CSVD leads to progressive brain injury. Finally, we summarize recent studies on the effects of major classes of cardiovascular medicines relevant to CSVD-related cognitive impairment. Although many key questions remain, the increased attention to CSVD has resulted in a sharper vision for what will be needed to meet the upcoming challenges imposed by this disease.

Impact prediction of translocation of the mitochondrial outer membrane 70 as biomarker in Alzheimer's disease

Mitochondrial dysfunction plays a key role in the pathogenesis of Alzheimer's disease (AD). The translocase of the outer membrane (TOM) complex controls the input of mitochondrial precursor proteins to maintain mitochondrial function under pathophysiological conditions. However, its role in AD development remains unclear. TOM70 is an important translocase present in the TOM complex. In the current study, we found that TOM70 levels were reduced in the peripheral blood and hippocampus of the APP/PS1 mice. In addition, we examined the whole-blood mRNA levels of TOM70 in patients with AD, dementia with Lewy bodies (DLB), and post-stroke dementia (PSD). Our study revealed that the mRNA level of TOM70 was decreased in the blood samples of patients with AD, which was also correlated with the progression of clinical stages. Therefore, we proposed that the expression of TOM70 could be a promising biomarker for AD diagnosis and monitoring of disease progression.

Associations of multiple visual rating scales based on structural magnetic resonance imaging with disease severity and cerebrospinal fluid biomarkers in patients with Alzheimer’s disease

The relationships between multiple visual rating scales based on structural magnetic resonance imaging (sMRI) with disease severity and cerebrospinal fluid (CSF) biomarkers in patients with Alzheimer’s disease (AD) were ambiguous. In this study, a total of 438 patients with clinically diagnosed AD were recruited. All participants underwent brain sMRI scan, and medial temporal lobe atrophy (MTA), posterior atrophy (PA), global cerebral atrophy-frontal sub-scale (GCA-F), and Fazekas rating scores were visually evaluated. Meanwhile, disease severity was assessed by neuropsychological tests such as the Mini-Mental State Examination (MMSE), Montreal Cognitive Assessment (MoCA), and Clinical Dementia Rating (CDR). Among them, 95 patients were tested for CSF core biomarkers, including Aβ_1–42, Aβ_1–40, Aβ_1–42/Aβ_1–40, p-tau, and t-tau. As a result, the GCA-F and Fazekas scales showed positively significant correlations with onset age (r = 0.181, p < 0.001; r = 0.411, p < 0.001, respectively). Patients with late-onset AD (LOAD) showed higher GCA-F and Fazekas scores (p < 0.001, p < 0.001). With regard to the disease duration, the MTA and GCA-F were positively correlated (r = 0.137, p < 0.05; r = 0.106, p < 0.05, respectively). In terms of disease severity, a positively significant association emerged between disease severity and the MTA, PA GCA-F, and Fazekas scores (p < 0.001, p < 0.001, p < 0.001, p < 0.05, respectively). Moreover, after adjusting for age, gender, and APOE alleles, the MTA scale contributed to moderate to severe AD in statistical significance independently by multivariate logistic regression analysis (p < 0.05). The model combining visual rating scales, age, gender, and APOE alleles showed the best performance for the prediction of moderate to severe AD significantly (AUC = 0.712, sensitivity = 51.5%, specificity = 84.6%). In addition, we observed that the MTA and Fazekas scores were associated with a lower concentration of Aβ_1–42 (p < 0.031, p < 0.022, respectively). In summary, we systematically analyzed the benefits of multiple visual rating scales in predicting the clinical status of AD. The visual rating scales combined with age, gender, and APOE alleles showed best performance in predicting the severity of AD. MRI biomarkers in combination with CSF biomarkers can be used in clinical practice.

Deep Learning and Microscopic Imaging in the Nursing Process of Neurosurgery Operation

Neurosurgery is mainly for the treatment of head trauma, cerebrovascular disease, brain tumors, and spinal cord disorders. These operations are difficult and risky, so disability and mortality are high. To reduce the risk of surgery, reduce postoperative complications, and improve the treatment effect of patients, this article applies deep learning and microscopic imaging to the nursing process of neurosurgery. Through deep learning and microscopic imaging, doctors can learn about patients during surgery. The specific situation of the trauma site, after which surgery is performed according to the situation, effectively reduces the casualties, reduces the loss of patients, and provides a reference for the research of neurosurgery nursing. Research results prove that deep learning and microscopic imaging can play an important role in the nursing process of neurosurgery. Compared with conventional treatment methods, microscopic imaging treatment can effectively improve the treatment effect, and the operation time for patients is less than that of conventional treatment. About 20% and the incidence of postoperative complications is lower than 30%, which can effectively reduce the cost to patients and improve the quality of treatment.

Biomarkers Analysis and Clinical Manifestations in Comorbid Creutzfeldt–Jakob Disease: A Retrospective Study in 215 Autopsy Cases

Creutzfeldt–Jakob disease (CJD), the most common human prion disorder, may occur as “pure” neurodegeneration with isolated prion deposits in the brain tissue; however, comorbid cases with different concomitant neurodegenerative diseases have been reported. This retrospective study examined correlations of clinical, neuropathological, molecular-genetic, immunological, and neuroimaging biomarkers in pure and comorbid CJD. A total of 215 patients have been diagnosed with CJD during the last ten years by the Czech National Center for Prion Disorder Surveillance. Data were collected from all patients with respect to diagnostic criteria for probable CJD, including clinical description, EEG, MRI, and CSF findings. A detailed neuropathological analysis uncovered that only 11.16% were “pure” CJD, while 62.79% had comorbid tauopathy, 20.47% had Alzheimer’s disease, 3.26% had frontotemporal lobar degeneration, and 2.33% had synucleinopathy. The comorbid subgroup analysis revealed that tauopathy was linked to putaminal hyperintensity on MRIs, and AD mainly impacted the age of onset, hippocampal atrophy on MRIs, and beta-amyloid levels in the CSF. The retrospective data analysis found a surprisingly high proportion of comorbid neuropathologies; only 11% of cases were verified as “pure” CJD, i.e., lacking hallmarks of other neurodegenerations. Comorbid neuropathologies can impact disease manifestation and can complicate the clinical diagnosis of CJD.

Prediction of Cognitive Progression in Individuals with Mild Cognitive Impairment Using Radiomics as an Improvement of the ATN System: A Five-Year Follow-Up Study

Objective

To improve the N biomarker in the amyloid/tau/neurodegeneration system by radiomics and study its value for predicting cognitive progression in individuals with mild cognitive impairment (MCI).

Materials and Methods

A group of 147 healthy controls (HCs) (72 male; mean age ± standard deviation, 73.7 ± 6.3 years), 197 patients with MCI (114 male; 72.2 ± 7.1 years), and 128 patients with Alzheimer’s disease (AD) (74 male; 73.7 ± 8.4 years) were included. Optimal A, T, and N biomarkers for discriminating HC and AD were selected using receiver operating characteristic (ROC) curve analysis. A radiomics model containing comprehensive information of the whole cerebral cortex and deep nuclei was established to create a new N biomarker. Cerebrospinal fluid (CSF) biomarkers were evaluated to determine the optimal A or T biomarkers. All MCI patients were followed up until AD conversion or for at least 60 months. The predictive value of A, T, and the radiomics-based N biomarker for cognitive progression of MCI to AD were analyzed using Kaplan-Meier estimates and the log-rank test.

Results

The radiomics-based N biomarker showed an ROC curve area of 0.998 for discriminating between AD and HC. CSF Aβ42 and p-tau proteins were identified as the optimal A and T biomarkers, respectively. For MCI patients on the Alzheimer’s continuum, isolated A+ was an indicator of cognitive stability, while abnormalities of T and N, separately or simultaneously, indicated a high risk of progression. For MCI patients with suspected non-Alzheimer’s disease pathophysiology, isolated T+ indicated cognitive stability, while the appearance of the radiomics-based N+ indicated a high risk of progression to AD.

Conclusion

We proposed a new radiomics-based improved N biomarker that could help identify patients with MCI who are at a higher risk for cognitive progression. In addition, we clarified the value of a single A/T/N biomarker for predicting the cognitive progression of MCI.

Structural magnetic resonance imaging for the early diagnosis of dementia due to Alzheimer's disease in people with mild cognitive impairment

BACKGROUND

Mild cognitive impairment (MCI) due to Alzheimer's disease is the symptomatic predementia phase of Alzheimer's disease dementia, characterised by cognitive and functional impairment not severe enough to fulfil the criteria for dementia. In clinical samples, people with amnestic MCI are at high risk of developing Alzheimer's disease dementia, with annual rates of progression from MCI to Alzheimer's disease estimated at approximately 10% to 15% compared with the base incidence rates of Alzheimer's disease dementia of 1% to 2% per year.

OBJECTIVES

To assess the diagnostic accuracy of structural magnetic resonance imaging (MRI) for the early diagnosis of dementia due to Alzheimer's disease in people with MCI versus the clinical follow-up diagnosis of Alzheimer's disease dementia as a reference standard (delayed verification). To investigate sources of heterogeneity in accuracy, such as the use of qualitative visual assessment or quantitative volumetric measurements, including manual or automatic (MRI) techniques, or the length of follow-up, and age of participants. MRI was evaluated as an add-on test in addition to clinical diagnosis of MCI to improve early diagnosis of dementia due to Alzheimer's disease in people with MCI.

SEARCH METHODS

On 29 January 2019 we searched Cochrane Dementia and Cognitive Improvement's Specialised Register and the databases, MEDLINE, Embase, BIOSIS Previews, Science Citation Index, PsycINFO, and LILACS. We also searched the reference lists of all eligible studies identified by the electronic searches.

SELECTION CRITERIA

We considered cohort studies of any size that included prospectively recruited people of any age with a diagnosis of MCI. We included studies that compared the diagnostic test accuracy of baseline structural MRI versus the clinical follow-up diagnosis of Alzheimer's disease dementia (delayed verification). We did not exclude studies on the basis of length of follow-up. We included studies that used either qualitative visual assessment or quantitative volumetric measurements of MRI to detect atrophy in the whole brain or in specific brain regions, such as the hippocampus, medial temporal lobe, lateral ventricles, entorhinal cortex, medial temporal gyrus, lateral temporal lobe, amygdala, and cortical grey matter.

DATA COLLECTION AND ANALYSIS

Four teams of two review authors each independently reviewed titles and abstracts of articles identified by the search strategy. Two teams of two review authors each independently assessed the selected full-text articles for eligibility, extracted data and solved disagreements by consensus. Two review authors independently assessed the quality of studies using the QUADAS-2 tool. We used the hierarchical summary receiver operating characteristic (HSROC) model to fit summary ROC curves and to obtain overall measures of relative accuracy in subgroup analyses. We also used these models to obtain pooled estimates of sensitivity and specificity when sufficient data sets were available.

MAIN RESULTS

We included 33 studies, published from 1999 to 2019, with 3935 participants of whom 1341 (34%) progressed to Alzheimer's disease dementia and 2594 (66%) did not. Of the participants who did not progress to Alzheimer's disease dementia, 2561 (99%) remained stable MCI and 33 (1%) progressed to other types of dementia. The median proportion of women was 53% and the mean age of participants ranged from 63 to 87 years (median 73 years). The mean length of clinical follow-up ranged from 1 to 7.6 years (median 2 years). Most studies were of poor methodological quality due to risk of bias for participant selection or the index test, or both. Most of the included studies reported data on the volume of the total hippocampus (pooled mean sensitivity 0.73 (95% confidence interval (CI) 0.64 to 0.80); pooled mean specificity 0.71 (95% CI 0.65 to 0.77); 22 studies, 2209 participants). This evidence was of low certainty due to risk of bias and inconsistency. Seven studies reported data on the atrophy of the medial temporal lobe (mean sensitivity 0.64 (95% CI 0.53 to 0.73); mean specificity 0.65 (95% CI 0.51 to 0.76); 1077 participants) and five studies on the volume of the lateral ventricles (mean sensitivity 0.57 (95% CI 0.49 to 0.65); mean specificity 0.64 (95% CI 0.59 to 0.70); 1077 participants). This evidence was of moderate certainty due to risk of bias. Four studies with 529 participants analysed the volume of the total entorhinal cortex and four studies with 424 participants analysed the volume of the whole brain. We did not estimate pooled sensitivity and specificity for the volume of these two regions because available data were sparse and heterogeneous. We could not statistically evaluate the volumes of the lateral temporal lobe, amygdala, medial temporal gyrus, or cortical grey matter assessed in small individual studies. We found no evidence of a difference between studies in the accuracy of the total hippocampal volume with regards to duration of follow-up or age of participants, but the manual MRI technique was superior to automatic techniques in mixed (mostly indirect) comparisons. We did not assess the relative accuracy of the volumes of different brain regions measured by MRI because only indirect comparisons were available, studies were heterogeneous, and the overall accuracy of all regions was moderate.

AUTHORS' CONCLUSIONS

The volume of hippocampus or medial temporal lobe, the most studied brain regions, showed low sensitivity and specificity and did not qualify structural MRI as a stand-alone add-on test for an early diagnosis of dementia due to Alzheimer's disease in people with MCI. This is consistent with international guidelines, which recommend imaging to exclude non-degenerative or surgical causes of cognitive impairment and not to diagnose dementia due to Alzheimer's disease. In view of the low quality of most of the included studies, the findings of this review should be interpreted with caution. Future research should not focus on a single biomarker, but rather on combinations of biomarkers to improve an early diagnosis of Alzheimer's disease dementia.

Clinical Significance of Magnetic Resonance Imaging Markers of Vascular Brain Injury: A Systematic Review and Meta-analysis

Importance

Covert vascular brain injury (VBI) is highly prevalent in community-dwelling older persons, but its clinical and therapeutic implications are debated.

Objective

To better understand the clinical significance of VBI to optimize prevention strategies for the most common age-related neurological diseases, stroke and dementia.

Data Source

We searched for articles in PubMed between 1966 and December 22, 2017, studying the association of 4 magnetic resonance imaging (MRI) markers of covert VBI (white matter hyperintensities [WMHs] of presumed vascular origin, MRI-defined covert brain infarcts [BIs], cerebral microbleeds [CMBs], and perivascular spaces [PVSs]) with incident stroke, dementia, or death.

Study Selection

Data were taken from prospective, longitudinal cohort studies including 50 or more adults.

Data Extraction and Synthesis

We performed inverse variance-weighted meta-analyses with random effects and z score-based meta-analyses for WMH burden. The significance threshold was P < .003 (17 independent tests). We complied with the Meta-analyses of Observational Studies in Epidemiology guidelines.

Main Outcomes and Measures

Stroke (hemorrhagic and ischemic), dementia (all and Alzheimer disease), and death.

Results

Of 2846 articles identified, 94 studies were eligible, with up to 14 529 participants for WMH, 16 012 participants for BI, 15 693 participants for CMB, and 4587 participants for PVS. Extensive WMH burden was associated with higher risk of incident stroke (hazard ratio [HR], 2.45; 95% CI, 1.93-3.12; P < .001), ischemic stroke (HR, 2.39; 95% CI, 1.65-3.47; P < .001), intracerebral hemorrhage (HR, 3.17; 95% CI, 1.54-6.52; P = .002), dementia (HR, 1.84; 95% CI, 1.40-2.43; P < .001), Alzheimer disease (HR, 1.50; 95% CI, 1.22-1.84; P < .001), and death (HR, 2.00; 95% CI, 1.69-2.36; P < .001). Presence of MRI-defined BIs was associated with higher risk of incident stroke (HR, 2.38; 95% CI, 1.87-3.04; P < .001), ischemic stroke (HR, 2.18; 95% CI, 1.67-2.85; P < .001), intracerebral hemorrhage (HR, 3.81; 95% CI, 1.75-8.27; P < .001), and death (HR, 1.64; 95% CI, 1.40-1.91; P < .001). Presence of CMBs was associated with increased risk of stroke (HR, 1.98; 95% CI, 1.55-2.53; P < .001), ischemic stroke (HR, 1.92; 95% CI, 1.40-2.63; P < .001), intracerebral hemorrhage (HR, 3.82; 95% CI, 2.15-6.80; P < .001), and death (HR, 1.53; 95% CI, 1.31-1.80; P < .001). Data on PVS were limited and insufficient to conduct meta-analyses but suggested an association of high PVS burden with increased risk of stroke, dementia, and death; this requires confirmation.

Conclusions and Relevance

We report evidence that MRI markers of VBI have major clinical significance. This research prompts careful evaluation of the benefit-risk ratio for available prevention strategies in individuals with covert VBI.

Association of short-term cognitive decline and MCI-to-AD dementia conversion with CSF, MRI, amyloid- and 18F-FDG-PET imaging

Disease-modifying treatment trials are increasingly advanced to the prodromal or preclinical phase of Alzheimer's disease (AD), and inclusion criteria are based on biomarkers rather than clinical symptoms. Therefore, it is of great interest to determine which biomarkers should be combined to accurately predict conversion from mild cognitive impairment (MCI) to AD dementia. However, up to date, only few studies performed a complete A/T/N subject characterization using each of the CSF and imaging markers, or they only investigated long-term (≥ 2 years) prognosis. This study aimed to investigate the association between cerebrospinal fluid (CSF), magnetic resonance imaging (MRI), amyloid- and ¹⁸F-FDG positron emission tomography (PET) measures at baseline, in relation to cognitive changes and conversion to AD dementia over a short-term (12-month) period. We included 13 healthy controls, 49 MCI and 16 AD dementia patients with a clinical-based diagnosis and a complete A/T/N characterization at baseline. Global cortical amyloid-β (Aβ) burden was quantified using the ¹⁸F-AV45 standardized uptake value ratio (SUVR) with two different reference regions (cerebellar grey and subcortical white matter), whereas metabolism was assessed based on ¹⁸F-FDG SUVR. CSF measures included Aβ_1–42, Aβ_1–40, T-tau, P-tau₁₈₁, and their ratios, and MRI markers included hippocampal volumes (HV), white matter hyperintensities, and cortical grey matter volumes. Cognitive functioning was measured by MMSE and RBANS index scores. All statistical analyses were corrected for age, sex, education, and APOE ε4 genotype. As a result, faster cognitive decline was most strongly associated with hypometabolism (posterior cingulate) and smaller hippocampal volume (e.g., Δstory recall: β = +0.43 [p < 0.001] and + 0.37 [p = 0.005], resp.) at baseline. In addition, faster cognitive decline was significantly associated with higher baseline Aβ burden only if SUVR was referenced to the subcortical white matter (e.g., Δstory recall: β = −0.28 [p = 0.020]). Patients with MCI converted to AD dementia at an annual rate of 31%, which could be best predicted by combining neuropsychological testing (visuospatial construction skills) with either MRI-based HV or ¹⁸F-FDG-PET. Combining all three markers resulted in 96% specificity and 92% sensitivity. Neither amyloid-PET nor CSF biomarkers could discriminate short-term converters from non-converters.

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FDG-PET and MRI HV are the strongest predictors of cognitive decline and conversion to AD.

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Combination of visuospatial construction testing with FDG-PET or MRI HV present high predicting power of conversion.

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CSF and amyloid-PET seem less suitable markers of disease progression.

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Increased AV45-PET predicts short-term cognitive decline if SUVR is referenced to WM instead of CB.

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.

Clinical validity of delayed recall tests as a gateway biomarker for Alzheimer's disease in the context of a structured 5-phase development framework

Although Alzheimer's disease criteria promote the use of biomarkers, their maturity in clinical routine still needs to be assessed. In the light of the oncology framework, we conducted a literature review on measures used to assess delayed recall impairment due to medial temporal lobe dysfunction (i.e., free and cued word list recall tests). Ample evidence is available for phases 1 (rationale for use), 2 (discriminative ability), and 3 (early detection ability) for many of the tests in routine use. Evidence about phase 4 (performance in real world) and phase 5 (quantify impact and costs) is yet to come. Administration procedures have been standardized and cutoff scores are well validated in large Alzheimer's disease and mild cognitive impaired series. Some aspects (e.g., different task formats), however, hamper the comparability of results among different populations and the reproducibility between laboratories. No definite guideline for their use can thus be proposed at the moment. Accordingly, the maturity of such markers is not yet sufficient and requires future investigation to promote the proper use of memory measures in clinical settings.

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.

Incremental value of biomarker combinations to predict progression of mild cognitive impairment to Alzheimer's dementia

Background

The progression of mild cognitive impairment (MCI) to Alzheimer’s disease (AD) dementia can be predicted by cognitive, neuroimaging, and cerebrospinal fluid (CSF) markers. Since most biomarkers reveal complementary information, a combination of biomarkers may increase the predictive power. We investigated which combination of the Mini-Mental State Examination (MMSE), Clinical Dementia Rating (CDR)-sum-of-boxes, the word list delayed free recall from the Consortium to Establish a Registry of Dementia (CERAD) test battery, hippocampal volume (HCV), amyloid-beta_1–42 (Aβ42), amyloid-beta_1–40 (Aβ40) levels, the ratio of Aβ42/Aβ40, phosphorylated tau, and total tau (t-Tau) levels in the CSF best predicted a short-term conversion from MCI to AD dementia.

Methods

We used 115 complete datasets from MCI patients of the “Dementia Competence Network”, a German multicenter cohort study with annual follow-up up to 3 years. MCI was broadly defined to include amnestic and nonamnestic syndromes. Variables known to predict progression in MCI patients were selected a priori. Nine individual predictors were compared by receiver operating characteristic (ROC) curve analysis. ROC curves of the five best two-, three-, and four-parameter combinations were analyzed for significant superiority by a bootstrapping wrapper around a support vector machine with linear kernel. The incremental value of combinations was tested for statistical significance by comparing the specificities of the different classifiers at a given sensitivity of 85%.

Results

Out of 115 subjects, 28 (24.3%) with MCI progressed to AD dementia within a mean follow-up period of 25.5 months. At baseline, MCI-AD patients were no different from stable MCI in age and gender distribution, but had lower educational attainment. All single biomarkers were significantly different between the two groups at baseline. ROC curves of the individual predictors gave areas under the curve (AUC) between 0.66 and 0.77, and all single predictors were statistically superior to Aβ40. The AUC of the two-parameter combinations ranged from 0.77 to 0.81. The three-parameter combinations ranged from AUC 0.80–0.83, and the four-parameter combination from AUC 0.81–0.82. None of the predictor combinations was significantly superior to the two best single predictors (HCV and t-Tau). When maximizing the AUC differences by fixing sensitivity at 85%, the two- to four-parameter combinations were superior to HCV alone.

Conclusion

A combination of two biomarkers of neurodegeneration (e.g., HCV and t-Tau) is not superior over the single parameters in identifying patients with MCI who are most likely to progress to AD dementia, although there is a gradual increase in the statistical measures across increasing biomarker combinations. This may have implications for clinical diagnosis and for selecting subjects for participation in clinical trials.

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.

Feasibility and acceptance of simultaneous amyloid PET/MRI

PURPOSE

Established Alzheimer's disease (AD) biomarker concepts classify into amyloid pathology and neuronal injury biomarkers, while recent alternative concepts classify into diagnostic and progression AD biomarkers. However, combined amyloid positron emission tomography/magnetic resonance imaging (PET/MRI) offers the chance to obtain both biomarker category read-outs within one imaging session, with increased patient as well as referrer convenience. The aim of this pilot study was to investigate this matter for the first time.

METHODS

100 subjects (age 70 ± 10 yrs, 46 female), n = 51 with clinically defined mild cognitive impairment (MCI), n = 44 with possible/probable AD dementia, and n = 5 with frontotemporal lobe degeneration, underwent simultaneous [¹⁸F]florbetaben or [¹¹C]PIB PET/MRI (3 Tesla Siemens mMR). Brain amyloid load, mesial temporal lobe atrophy (MTLA) by means of the Scheltens scale, and other morphological brain pathologies were scored by respective experts. The patients/caregivers as well as the referrers were asked to assess on a five-point scale the convenience related to the one-stop-shop PET and MRI approach.

RESULTS

In three subjects, MRI revealed temporal lobe abnormalities other than MTLA. According to the National Institute on Aging-Alzheimer's Association classification, the combined amyloid-beta PET/MRI evaluation resulted in 31 %, 45 %, and 24 % of the MCI subjects being categorized as "MCI-unlikely due to AD", "MCI due to AD-intermediate likelihood", and "MCI due to AD-high likelihood", respectively. 50 % of the probable AD dementia patients were categorized as "High level of evidence of AD pathophysiological process", and 56 % of the possible AD dementia patients as "Possible AD dementia - with evidence of AD pathophysiological process". With regard to the International Working Group 2 classification, 36 subjects had both positive diagnostic and progression biomarkers. The patient/caregiver survey revealed a gain of convenience in 88 % of responders as compared to a theoretically separate PET and MR imaging. In the referrer survey, an influence of the combined amyloid-beta PET/MRI on the final diagnosis was reported by 82 % of responders, with a referrer acceptance score of 3.7 ± 1.0 on a 5-point scale.

CONCLUSION

Simultaneous amyloid PET/MRI is feasible and provides imaging biomarkers of all categories which are able to supplement the clinical diagnosis of MCI due to AD and that of AD dementia. Further, patient and referrer convenience is improved by this one-stop-shop imaging approach.

The role of attention in emotional memory enhancement in pathological and healthy aging

After short delays between encoding and retrieval, healthy young participants have better memory performance for emotional stimuli than for neutral stimuli. Divided-attention paradigms suggest that this emotional enhancement of memory (EEM) is due to different attention mechanisms involved during encoding: automatic processing for negative stimuli, and controlled processing for positive stimuli. As far as we know, no study on the influence of these factors on EEM in Alzheimer's disease (AD) and mild cognitive impairment (MCI) patients, as compared to healthy young and older controls, has been conducted. Thus, the goal of our study was to ascertain whether the EEM in these populations depends on the attention resources available at encoding. Participants completed two encoding phases: full attention (FA) and divided attention (DA), followed by two retrieval phases (recognition tasks). There was no EEM on the discrimination accuracy, independently of group and encoding condition. Nevertheless, all participants used a more liberal response criterion for the negative and positive stimuli than for neutral ones. In AD patients, larger numbers of false recognitions for negative and positive stimuli than for neutral ones were observed after both encoding conditions. In MCI patients and in healthy older and younger controls this effect was observed only for negative stimuli, and it depended on the encoding condition. Thus, this effect was observed in young controls after both encoding conditions, in older controls after the DA encoding, and in MCI patients after the FA encoding. In conclusion, our results suggest that emotional valence does not always enhance discrimination accuracy. Nevertheless, in certain conditions related to the attention resources available at encoding, emotional valence, especially the negative one, enhances the subjective feeling of familiarity and, consequently, engenders changes in response bias. This effect seems to be sensitive to the age and the pathology of participants.

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.

Serial position effects in patients with mild cognitive impairment and early and moderate Alzheimer's disease compared with healthy comparison subjects

BACKGROUND/AIMS

This study aimed to investigate whether the serial position effects in memory can differentiate patients with different subtypes of mild cognitive impairment (MCI) from healthy controls and patients with different stages of Alzheimer's disease (AD).

METHODS

The serial position effects was tested with the CERAD word list task in 184 persons (39 healthy control subjects, 15 amnestic MCI single domain subjects, 23 amnestic MCI multiple domain subjects, 31 nonamnestic MCI subjects, 45 early or mild AD patients, and 31 moderate AD patients).

RESULTS

With progression of dementia, memory deficits increased and the impairment in the primacy effect during the learning trials advanced, whereas the recall of recent items was less impaired. The serial position profile of nonamnestic MCI patients resembled that of healthy control subjects, whereas amnestic MCI patients showed poorer performance in all 3 positions but no significant difference as a function of serial word position.

CONCLUSION

Analyses of the serial position effect may be a useful complement to clinical neuropsychological measures for distinguishing amnestic MCI patients from normal aging and patients with different stages of dementia.

Autonomic dysfunction in mild cognitive impairment: evidence from power spectral analysis of heart rate variability in a cross-sectional case-control study

BACKGROUND

Mild cognitive impairment (MCI) is set to become a major health problem with the exponential ageing of the world's population. The association between MCI and autonomic dysfunction, supported by indirect evidence and rich with clinical implications in terms of progression to dementia and increased risk of mortality and falls, has never been specifically demonstrated.

AIM

To conduct a comprehensive assessment of autonomic function in subjects with MCI by means of power spectral analysis (PSA) of heart rate variability (HRV) at rest and during provocative manoeuvres.

METHODS

This cross-sectional study involved 80 older outpatients (aged ≥ 65) consecutively referred to a geriatric unit and diagnosed with MCI or normal cognition (controls) based on neuropsychological testing. PSA was performed on 5-minute electrocardiographic recordings under three conditions--supine rest with free breathing (baseline), supine rest with paced breathing at 12 breaths/minute (parasympathetic stimulation), and active standing (orthosympathetic stimulation)--with particular focus on the changes from baseline to stimulation of indices of sympathovagal balance: normalized low frequency (LFn) and high frequency (HFn) powers and the LF/HF ratio. Blood pressure (BP) was measured at baseline and during standing. Given its exploratory nature in a clinical population the study included subjects on medications with a potential to affect HRV.

RESULTS

There were no significant differences in HRV indices between the two groups at baseline. MCI subjects exhibited smaller physiological changes in all three HRV indices during active standing, consistently with a dysfunction of the orthosympathetic system. Systolic BP after 10 minutes of standing was lower in MCI subjects, suggesting dysautonomia-related orthostatic BP dysregulation.

CONCLUSIONS

Our study is novel in providing evidence of autonomic dysfunction in MCI. This is associated with orthostatic BP dysregulation and the ongoing follow-up of the study population will determine its prognostic relevance as a predictor of adverse health outcomes.

What do temporal lobe epilepsy and progressive mild cognitive impairment have in common?

Temporal lobe epilepsy (TLE) and mild cognitive impairment (MCI) are both subject to intensive memory research. Memory problems are a core characteristic of both conditions and we wonder if there are analogies which would enrich the two distinct research communities. In this review we focus on memory decline in both conditions, that is, the most feared psychosocial effect. While it is clear that memory decline in MCI is highly likely and would lead to the more severe diagnosis of Alzheimer's disease, it is a debate if TLE is a dementing disease or not. As such, like for MCI, one can differentiate progressive from stable TLE subtypes, mainly depending on the age of onset. Neuroimaging techniques such as volumetric analysis of the hippocampus, entorhinal, and perirhinal cortex show evidence of pathological changes in TLE and are predictive for memory decline in MCI. Several studies emphasize that it is necessary to extend the region of interest—even whole-brain characteristics can be predictive for conversion from MCI to Alzheimer's disease. Electroencephalography is increasingly subject to computational neuroscience, revealing new approaches for analyzing frequency, spatial synchronization, and information content of the signals. These methods together with event-related designs that assess memory functions are highly promising for understanding the mechanisms of memory decline in both TLE and MCI populations. Finally, there is evidence that the potential of such markers for memory decline is far from being exhausted. Similar structural and neurophysiological characteristics are linked to memory decline in TLE and MCI. We raise the hope that interdisciplinary research and cross-talk between fields such as research on epilepsy and dementia, will shed further light on the dementing characteristics of the pathological basis of MCI and TLE and support the development of new memory enhancing treatment strategies.

Semi-supervised multimodal relevance vector regression improves cognitive performance estimation from imaging and biological biomarkers

Accurate estimation of cognitive scores for patients can help track the progress of neurological diseases. In this paper, we present a novel semi-supervised multimodal relevance vector regression (SM-RVR) method for predicting clinical scores of neurological diseases from multimodal imaging and biological biomarker, to help evaluate pathological stage and predict progression of diseases, e.g., Alzheimer's diseases (AD). Unlike most existing methods, we predict clinical scores from multimodal (imaging and biological) biomarkers, including MRI, FDG-PET, and CSF. Considering that the clinical scores of mild cognitive impairment (MCI) subjects are often less stable compared to those of AD and normal control (NC) subjects due to the heterogeneity of MCI, we use only the multimodal data of MCI subjects, but no corresponding clinical scores, to train a semi-supervised model for enhancing the estimation of clinical scores for AD and NC subjects. We also develop a new strategy for selecting the most informative MCI subjects. We evaluate the performance of our approach on 202 subjects with all three modalities of data (MRI, FDG-PET and CSF) from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. The experimental results show that our SM-RVR method achieves a root-mean-square error (RMSE) of 1.91 and a correlation coefficient (CORR) of 0.80 for estimating the MMSE scores, and also a RMSE of 4.45 and a CORR of 0.78 for estimating the ADAS-Cog scores, demonstrating very promising performances in AD studies.

Risk factors for the progression of mild cognitive impairment to dementia

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

FDG-PET in early AD diagnosis

FDG-PET is a valuable tool that will continue to aid in identifying AD in its prodromal and early dementia stages, distinguishing it from other causes of dementia, and tracking progression of the disease. As brain FDG-PET scans and well-trained readers of these scans are becoming more widely available to clinicians who are becoming more informed about the role FDG-PET can play in early AD diagnosis, its use is expected to increase.

Cerebral white matter hyperintensities in the prediction of cognitive decline and incident dementia

Cerebral white matter hyperintensities (WMH), detected in vivo with magnetic resonance imaging (MRI), are commonly used to assess cerebrovascular burden in cognitive impairment. However, the association between WMH and cognition is not consistent across the literature. The present review examines evidence from published longitudinal studies. We reviewed the PubMed data base from January 1990 to March 2013 and included studies investigating the association of WMH with (1) the risk of dementia in the general population, (2) the risk of conversion to dementia in the mild cognitive impairment (MCI) population, and (3) cognitive decline in the general population. WMH were associated with all types of dementia in the general population, but not in MCI patients. Results are discrepant for global decline. WMH appear to be early predictors of the risk of dementia, but this association appears to be modulated by cognitive reserve, age and the spatial distribution of lesions. There are, however, some limits in the use of WMH as a marker of vascular burden. In addition to their ischaemic origin, WMH may be the result of co-occurring morbidity. Further research is needed to elucidate to what extent WMH actually reflect vascular risk to evaluate the likely efficacy of interventions specifically targeting WMH reduction.

Analysis of grey matter in thalamus and basal ganglia based on EEG α3/α2 frequency ratio reveals specific changes in subjects with mild cognitive impairment

GM (grey matter) changes of thalamus and basal ganglia have been demonstrated to be involved in AD (Alzheimer's disease). Moreover, the increase of a specific EEG (electroencephalogram) marker, α3/α2, have been associated with AD-converters subjects with MCI (mild cognitive impairment). To study the association of prognostic EEG markers with specific GM changes of thalamus and basal ganglia in subjects with MCI to detect biomarkers (morpho-physiological) early predictive of AD and non-AD dementia. Seventy-four adult subjects with MCI underwent EEG recording and high-resolution 3D MRI (three-dimensional magnetic resonance imaging). The α3/α2 ratio was computed for each subject. Three groups were obtained according to increasing tertile values of α3/α2 ratio. GM density differences between groups were investigated using a VBM (voxel-based morphometry) technique. Subjects with higher α3/α2 ratios when compared with subjects with lower and middle α3/α2 ratios showed minor atrophy in the ventral stream of basal ganglia (head of caudate nuclei and accumbens nuclei bilaterally) and of the pulvinar nuclei in the thalamus; The integrated analysis of EEG and morpho-structural markers could be useful in the comprehension of anatomo-physiological underpinning of the MCI entity.

Quantitative EEG Markers in Mild Cognitive Impairment: Degenerative versus Vascular Brain Impairment

We evaluated the relationship between brain rhythmicity and both the cerebrovascular damage (CVD) and amygdalohippocampal complex (AHC) atrophy, as revealed by scalp electroencephalography (EEG) in a cohort of subjects with mild cognitive impairment (MCI). All MCI subjects underwent EEG recording and magnetic resonance imaging. EEGs were recorded at rest. Relative power was separately computed for delta, theta, alpha1, alpha2, and alpha3 frequency bands. In the spectral band power the severity of CVD was associated with increased delta power and decreased alpha2 power. No association of vascular damage was observed with alpha3 power. Moreover, the theta/alpha1 ratio could be a reliable index for the estimation of the individual extent of CV damage. On the other side, the group with moderate hippocampal atrophy showed the highest increase of alpha2 and alpha3 power. Moreover, when the amygdalar and hippocampal volumes are separately considered, within amygdalohippocampal complex (AHC), the increase of theta/gamma ratio is best associated with amygdalar atrophy whereas alpha3/alpha2 ratio is best associated with hippocampal atrophy. CVD and AHC damages are associated with specific EEG markers. So far, these EEG markers could have a prospective value in differential diagnosis between vascular and degenerative MCI.

Hippocampal atrophy in Alzheimer’s disease

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

Overtime reliability of medial temporal lobe atrophy rating in a clinical setting

BACKGROUND

Medial temporal lobe atrophy (MTA) is one of the first magnetic resonance imaging (MRI) signs in patients with Alzheimer's disease (AD) and used as a measure of disease progression. Visual assessment of MTA is easy to perform but the reliability of MTA rating over time has not been studied.

PURPOSE

To investigate what happens to the MTA rating scores if two radiologists rate the same MRI scans six times over a period of 1 year.

MATERIAL AND METHODS

One hundred outpatients were included in this study. All patients underwent MRI with a protocol and sequences used for geriatric patients, according to local clinical standards. One neuroradiologist and one general radiologist independently of each other performed retrospective visual assessments of MTA six times, using the same scans, over a period of 1 year.

RESULTS

Intra-rater kappa varied between κ 0.65 and 0.84 for the neuroradiologist and κ 0.38 and 0.74 for the general radiologist. Intra-rater weighted kappa (wκ) values showed almost perfect agreement for both investigators (wκ 0.83-0.94). Inter-rater reliability showed fair to moderate agreement, with the kappa value varying from κ 0.29 to 0.48 and weighted kappa values ranging from wκ 0.72 to 0.84. There was a statistically significant difference in rating between the two investigators.

CONCLUSION

Visual assessment of MTA repeated over time has a high grade of reproducibility when performed by an experienced investigator. The reproducibility drops when assessment is rarely performed. Inter-rater reliability is low when two investigators not working together are compared.

Specific EEG changes associated with atrophy of hippocampus in subjects with mild cognitive impairment and Alzheimer's disease

We evaluated the association between hippocampal atrophy and increase of the EEG markers alpha3/alpha2 relative power ratio in mild cognitive impairment (MCI) and Alzheimer's disease patients. Seventy-nine subjects with MCI and 11 patients with AD underwent EEG recording and MRI scan. The MCI group was subdivided in three subgroups according to growing hippocampal atrophy. The groups were characterized by alpha3/alpha2 relative power ratio. In AD patients group mapped hippocampal regions were computed and related with alpha3/alpha2 power ratio. Results show that the increase of alpha3/alpha2 power ratio is correlated with atrophy of hippocampus both in MCI and in Alzheimer's disease patients. This finding confirms the possible diagnostic role of EEG markers as diagnostic and prognostic factors in patient with prodromal and declared Alzheimer's disease.

Test sequence of CSF and MRI biomarkers for prediction of AD in subjects with MCI

Our aim was to identify the best diagnostic test sequence for predicting Alzheimer's disease (AD)-type dementia in subjects with mild cognitive impairment (MCI) using cerebrospinal fluid (CSF) and magnetic resonance imaging (MRI) biomarkers. We selected 153 subjects with mild cognitive impairment from a multicenter memory clinic-based cohort. We tested the CSF beta amyloid (Aβ)1-42/tau ratio using enzyme-linked immunosorbent assay (ELISA) and hippocampal volumes (HCVs) using the atlas-based learning embeddings for atlas propagation (LEAP) method. Outcome measure was progression to AD-type dementia in 2 years. At follow-up, 48 (31%) subjects converted to AD-type dementia. In multivariable analyses, CSF Aβ1-42/tau and HCV predicted AD-type dementia regardless of apolipoprotein E (APOE) genotype and cognitive scores. Test sequence analyses showed that CSF Aβ1-42/tau increased predictive accuracy in subjects with normal HCV (p < 0.001) and abnormal HCV (p = 0.025). HCV increased predictive accuracy only in subjects with normal CSF Aβ1-42/tau (p = 0.014). Slope analyses for annual cognitive decline yielded similar results. For selection of subjects for a prodromal AD trial, the best balance between sample size and number of subjects needed to screen was obtained with CSF markers. These results provide further support for the use of CSF and magnetic resonance imaging biomarkers to identify prodromal AD.

Combining structural brain changes improves the prediction of Alzheimer's disease and mild cognitive impairment

BACKGROUND

Several structural brain changes have been associated with Alzheimer's disease (AD). This study investigated the prediction of AD by combining multiple brain changes with the hallmark medial temporal lobe atrophy (MTA).

METHODS

High-resolution magnetic resonance imaging (MRI) data of people with mild AD (n = 39), mild cognitive impairment (MCI; n = 82), and of healthy controls (HC; n = 58) were obtained at baseline. Among these people, 26 AD, 53 MCI, and 46 HC subjects had 24-month follow-up MRI scans. Bilateral MTA was evaluated using a medial temporal lobe atrophy scale (MTAS). Common changes in the aging brain were summarized using a brain atrophy and lesion index (BALI). The performance of the MTAS, BALI, and a score combining both, using a logistic regression model, were assessed.

RESULTS

The MTAS and BALI scores were closely correlated (r(2) > 0.56); each differed between the diagnostic groups. Having an unfavorable MTAS score was associated with an increased risk of MCI-AD conversion (OR = 3.71, p = 0.039), adjusted for age, sex, and education; having an unfavorable BALI score marginally contributed to such risks (OR = 4.08, p = 0.080). Combining MTAS and BALI components resulted in a greater OR (8.99, p = 0.007) and an improved predictive accuracy (75.9%, p = 0.002).

CONCLUSION

Multiple structural changes have an additive effect on AD. The data support potential future roles of combining multiple coexisting structural changes to benefit AD diagnosis, progression monitoring, and/or treatment effect evaluation.

EEG markers are associated to gray matter changes in thalamus and basal ganglia in subjects with mild cognitive impairment

BACKGROUND

Gray matter (GM) changes of thalamus and basal ganglia have been demonstrated to be involved in Alzheimer's disease (AD). Moreover, the increase of two EEG markers, alpha3/alpha2 and theta/gamma ratio, have been associated with, respectively, AD converter and non-AD converter subjects with mild cognitive impairment (MCI).

OBJECTIVE

To study the association of prognostic EEG markers with specific GM changes of thalamus and basal ganglia in subjects with MCI to identify different MCI populations.

METHODS

74 adult subjects with mild cognitive impairment underwent EEG recording and high resolution 3D magnetic resonance imaging (MRI). The theta/gamma and alpha3/alpha2 ratio was computed for each subject. Three groups were obtained according to increasing tertile values of both alpha3/alpha2 and theta/gamma ratio. Gray matter density differences between groups were investigated using a voxel-based morphometry technique.

RESULTS

Subjects with higher a3/a2 ratios when compared to subjects with lower and middle a3/a2 ratios showed minor atrophy in the ventral stream of basal ganglia (head of caudate nuclei and accumbens nuclei bilaterally) and of the pulvinar nuclei in the thalamus; subjects with higher t/g ratio showed minor atrophy in putamina nuclei bilaterally than subjects with middle ratio.

CONCLUSION

The integrated analysis of EEG and morpho-structural markers could be useful in the comprehension of anatomo-physiological underpinning of the MCI entity.

Brain activation disturbance for target detection in patients with mild cognitive impairment: an fMRI study

Functional brain imaging in mild cognitive impairment (MCI) reveals differences in activation of task-relevant brain areas between patients and age-matched healthy controls. However, some studies reported hyperactivation and others hypoactivation in MCI compared with controls. The inconsistencies may be explained by compensatory mechanisms due to high complexity of the applied tasks. The oddball task is a simple paradigm that is known to activate a widespread network in the brain, involving attentional and monitoring mechanisms. In the present study, we examined amnestic or amnestic multidomain MCI patients (n = 12) and healthy controls (n = 13) in an auditory oddball task. Participants had to respond to infrequent targets and inhibit response to infrequent novel-nontarget stimuli. Lower stimulus related activation was found in MCI patients compared with healthy controls in parts of the middle temporal gyrus, the temporal pole, regions along the superior temporal sulcus, in the left cuneus, the left supramarginal gyrus, the anterior cingulated cortex and in the left inferior and middle frontal gyrus. Activation for oddball stimuli is assumed to reflect an automatic reflexive engagement of many brain regions in response to potentially important changes in the environment as well as cognitive control to monitor responses. The mechanisms of attention and cognitive control may be severely impaired in MCI and thus, underlie the cognitive deficits of this clinical group.

Multi-modal multi-task learning for joint prediction of multiple regression and classification variables in Alzheimer's disease

Many machine learning and pattern classification methods have been applied to the diagnosis of Alzheimer's disease (AD) and its prodromal stage, i.e., mild cognitive impairment (MCI). Recently, rather than predicting categorical variables as in classification, several pattern regression methods have also been used to estimate continuous clinical variables from brain images. However, most existing regression methods focus on estimating multiple clinical variables separately and thus cannot utilize the intrinsic useful correlation information among different clinical variables. On the other hand, in those regression methods, only a single modality of data (usually only the structural MRI) is often used, without considering the complementary information that can be provided by different modalities. In this paper, we propose a general methodology, namely multi-modal multi-task (M3T) learning, to jointly predict multiple variables from multi-modal data. Here, the variables include not only the clinical variables used for regression but also the categorical variable used for classification, with different tasks corresponding to prediction of different variables. Specifically, our method contains two key components, i.e., (1) a multi-task feature selection which selects the common subset of relevant features for multiple variables from each modality, and (2) a multi-modal support vector machine which fuses the above-selected features from all modalities to predict multiple (regression and classification) variables. To validate our method, we perform two sets of experiments on ADNI baseline MRI, FDG-PET, and cerebrospinal fluid (CSF) data from 45 AD patients, 91 MCI patients, and 50 healthy controls (HC). In the first set of experiments, we estimate two clinical variables such as Mini Mental State Examination (MMSE) and Alzheimer's Disease Assessment Scale-Cognitive Subscale (ADAS-Cog), as well as one categorical variable (with value of 'AD', 'MCI' or 'HC'), from the baseline MRI, FDG-PET, and CSF data. In the second set of experiments, we predict the 2-year changes of MMSE and ADAS-Cog scores and also the conversion of MCI to AD from the baseline MRI, FDG-PET, and CSF data. The results on both sets of experiments demonstrate that our proposed M3T learning scheme can achieve better performance on both regression and classification tasks than the conventional learning methods.

MCI patients' EEGs show group differences between those who progress and those who do not progress to AD

The theta/gamma and alpha3/alpha2 ratio were investigated as early markers for prognosticating of progression to dementia. 76 subjects with mild cognitive impairment (MCI) underwent EEG recording, MRI scans and neuropsychological (NPS) tests. After 3 years of follow-up, three subgroups were characterized as converters to Alzheimer's disease (AD, N=18), converters to non-AD dementia (N=14) and non-converters (N=44). The theta/gamma and alpha3/alpha2 ratio, performance on cognitive tests and hippocampal volume, as evaluated at the time of initial MCI diagnosis, were studied in the three groups. As expected, MCI to AD converters had the smallest mean hippocampal volume and poorest performance on verbal learning tests, whereas MCI to non-AD converters had poorest cognitive performance in non-verbal learning tests, abstract thinking, and letter fluency. Increased theta/gamma ratio was associated with conversion to both AD and non-AD dementia; increased alpha3/alpha2 ratio was only associated with conversion to AD. Theta/gamma and alpha3/alpha2 ratio could be promising prognostic markers in MCI patients. In particular, the increase of high alpha frequency seems to be associated with conversion in AD. EEG markers allow a mean correct percentage of correct classification up to 88.3%. Future prospective studies are needed to evaluate the sensitivity and specificity of these measures for predicting an AD outcome.

Biomarkers in Alzheimer's disease

Alzheimer's disease (AD) is the most common form of dementia in the elderly, and it is characterized by progressive impairment in multiple cognitive domains of sufficient severity to interfere with individuals' daily living activities. Historically, the diagnosis of AD has been based on the identification of a clinical syndrome, and accuracy studies of the current clinical criteria conducted in referral clinics have shown high sensitivity for AD. However, the identification of the disease is still not perfect, and there is growing evidence that the use of biomarkers will increase our ability to better indentify the underlying biology of AD, especially in its early stages. These biomarkers will improve the detection of the patients suitable for research studies and drug trials, and they will contribute to a better management of the disease in the clinical practice. In this review, we discuss the most studied biomarkers in AD: cerebrospinal fluid proteins, structural magnetic resonance imaging, functional neuroimaging techniques, and amyloid imaging.

Direction of Information Flow in Alzheimer's Disease and MCI Patients

Is directionality of electroencephalographic (EEG) synchronization abnormal in amnesic mild cognitive impairment (MCI) and Alzheimer's disease (AD)? And, do cerebrovascular and AD lesions represent additive factors in the development of MCI as a putative preclinical stage of AD? Here we reported two studies that tested these hypotheses. EEG data were recorded in normal elderly (Nold), amnesic MCI, and mild AD subjects at rest condition (closed eyes). Direction of information flow within EEG electrode pairs was performed by directed transfer function (DTF) at δ (2-4 Hz), θ (4-8 Hz), α1 (8-10 Hz), α2 (10-12 Hz), β1 (13-20 Hz), β2 (20-30 Hz), and γ (30-40 Hz). Parieto-to-frontal direction was stronger in Nold than in MCI and/or AD subjects for α and β rhythms. In contrast, the directional flow within interhemispheric EEG functional coupling did not discriminate among the groups. More interestingly, this coupling was higher at θ, α1, α2, and β1 in MCI with higher than in MCI with lower vascular load. These results suggest that directionality of parieto-to-frontal EEG synchronization is abnormal not only in AD but also in amnesic MCI, supporting the additive model according to which MCI state would result from the combination of cerebrovascular and neurodegenerative lesions.

Multimodal classification of Alzheimer's disease and mild cognitive impairment

Effective and accurate diagnosis of Alzheimer's disease (AD), as well as its prodromal stage (i.e., mild cognitive impairment (MCI)), has attracted more and more attention recently. So far, multiple biomarkers have been shown to be sensitive to the diagnosis of AD and MCI, i.e., structural MR imaging (MRI) for brain atrophy measurement, functional imaging (e.g., FDG-PET) for hypometabolism quantification, and cerebrospinal fluid (CSF) for quantification of specific proteins. However, most existing research focuses on only a single modality of biomarkers for diagnosis of AD and MCI, although recent studies have shown that different biomarkers may provide complementary information for the diagnosis of AD and MCI. In this paper, we propose to combine three modalities of biomarkers, i.e., MRI, FDG-PET, and CSF biomarkers, to discriminate between AD (or MCI) and healthy controls, using a kernel combination method. Specifically, ADNI baseline MRI, FDG-PET, and CSF data from 51AD patients, 99 MCI patients (including 43 MCI converters who had converted to AD within 18 months and 56 MCI non-converters who had not converted to AD within 18 months), and 52 healthy controls are used for development and validation of our proposed multimodal classification method. In particular, for each MR or FDG-PET image, 93 volumetric features are extracted from the 93 regions of interest (ROIs), automatically labeled by an atlas warping algorithm. For CSF biomarkers, their original values are directly used as features. Then, a linear support vector machine (SVM) is adopted to evaluate the classification accuracy, using a 10-fold cross-validation. As a result, for classifying AD from healthy controls, we achieve a classification accuracy of 93.2% (with a sensitivity of 93% and a specificity of 93.3%) when combining all three modalities of biomarkers, and only 86.5% when using even the best individual modality of biomarkers. Similarly, for classifying MCI from healthy controls, we achieve a classification accuracy of 76.4% (with a sensitivity of 81.8% and a specificity of 66%) for our combined method, and only 72% even using the best individual modality of biomarkers. Further analysis on MCI sensitivity of our combined method indicates that 91.5% of MCI converters and 73.4% of MCI non-converters are correctly classified. Moreover, we also evaluate the classification performance when employing a feature selection method to select the most discriminative MR and FDG-PET features. Again, our combined method shows considerably better performance, compared to the case of using an individual modality of biomarkers.

The clinical importance of white matter hyperintensities on brain magnetic resonance imaging: systematic review and meta-analysis

OBJECTIVES

To review the evidence for an association of white matter hyperintensities with risk of stroke, cognitive decline, dementia, and death.

DESIGN

Systematic review and meta-analysis.

DATA SOURCES

PubMed from 1966 to 23 November 2009.

STUDY SELECTION

Prospective longitudinal studies that used magnetic resonance imaging and assessed the impact of white matter hyperintensities on risk of incident stroke, cognitive decline, dementia, and death, and, for the meta-analysis, studies that provided risk estimates for a categorical measure of white matter hyperintensities, assessing the impact of these lesions on risk of stroke, dementia, and death.

DATA EXTRACTION

Population studied, duration of follow-up, method used to measure white matter hyperintensities, definition of the outcome, and measure of the association of white matter hyperintensities with the outcome.

DATA SYNTHESIS

46 longitudinal studies evaluated the association of white matter hyperintensities with risk of stroke (n=12), cognitive decline (n=19), dementia (n=17), and death (n=10). 22 studies could be included in a meta-analysis (nine of stroke, nine of dementia, eight of death). White matter hyperintensities were associated with an increased risk of stroke (hazard ratio 3.3, 95% confidence interval 2.6 to 4.4), dementia (1.9, 1.3 to 2.8), and death (2.0, 1.6 to 2.7). An association of white matter hyperintensities with a faster decline in global cognitive performance, executive function, and processing speed was also suggested.

CONCLUSION

White matter hyperintensities predict an increased risk of stroke, dementia, and death. Therefore white matter hyperintensities indicate an increased risk of cerebrovascular events when identified as part of diagnostic investigations, and support their use as an intermediate marker in a research setting. Their discovery should prompt detailed screening for risk factors of stroke and dementia.

Comparing predictors of conversion and decline in mild cognitive impairment

OBJECTIVE

A variety of measurements have been individually linked to decline in mild cognitive impairment (MCI), but the identification of optimal markers for predicting disease progression remains unresolved. The goal of this study was to evaluate the prognostic ability of genetic, CSF, neuroimaging, and cognitive measurements obtained in the same participants.

METHODS

APOE epsilon4 allele frequency, CSF proteins (Abeta(1-42), total tau, hyperphosphorylated tau [p-tau(181p)]), glucose metabolism (FDG-PET), hippocampal volume, and episodic memory performance were evaluated at baseline in patients with amnestic MCI (n = 85), using data from a large multisite study (Alzheimer's Disease Neuroimaging Initiative). Patients were classified as normal or abnormal on each predictor variable based on externally derived cutoffs, and then variables were evaluated as predictors of subsequent conversion to Alzheimer disease (AD) and cognitive decline (Alzheimer's Disease Assessment Scale-Cognitive Subscale) during a variable follow-up period (1.9 +/- 0.4 years).

RESULTS

Patients with MCI converted to AD at an annual rate of 17.2%. Subjects with MCI who had abnormal results on both FDG-PET and episodic memory were 11.7 times more likely to convert to AD than subjects who had normal results on both measures (p <or= 0.02). In addition, the CSF ratio p-tau(181p)/Abeta(1-42) (beta = 1.10 +/- 0.53; p = 0.04) and, marginally, FDG-PET predicted cognitive decline.

CONCLUSIONS

Baseline FDG-PET and episodic memory predict conversion to AD, whereas p-tau(181p)/Abeta(1-42) and, marginally, FDG-PET predict longitudinal cognitive decline. Complementary information provided by these biomarkers may aid in future selection of patients for clinical trials or identification of patients likely to benefit from a therapeutic intervention.

EEG markers discriminate among different subgroup of patients with mild cognitive impairment

Aim of the study is to discriminate among participants with mild cognitive impairment through electroencephalography brain rhythms. A total of 79 participants with MCI were classified into 4 subgroups based on the beginning of memory complaints up to the time of first visit. All participants underwent electroencephalography recording, magnetic resonance imaging, apolipoprotein E characterization, and volumetric morphometry estimation of hippocampal region. Electroencephalography markers show 2 distinct patterns: (1) increase of theta/ delta power ratio and highest value of alpha2 band power in the group with shorter duration of disease, the greater right-left hippocampal volume difference and worst memory performance; (2) the highest value of alpha3 band power and the highest alpha3/alpha2 power ratio in the group with the lesser total hippocampal volume but preserved memory performance. Apolipoprotein E4 is linked to a major risk of early beginning of disease. Electroencephalography markers allow a mean correct percentage of correct classification up to 89%.

Meta-analysis of CSF and MRI biomarkers for detecting preclinical Alzheimer's disease

BACKGROUND

Abnormal levels of biomarkers in cerebrospinal fluid (CSF) and atrophy of medial temporal lobe (MTL) structures on magnetic resonance imaging (MRI) are being used increasingly to diagnose early Alzheimer's disease (AD). We evaluated the claim that these biomarkers can detect preclinical AD before behavioural (i.e. memory) symptoms arise.

METHOD

We included all relevant longitudinal studies of CSF and MRI biomarkers published between January 2003 and November 2008. Subjects were not demented at baseline but some declined to mild cognitive impairment (MCI) or to AD during follow-up. Measures of tau and beta-amyloid in CSF, MTL atrophy on MRI, and performance on delayed memory tasks were extracted from the papers or obtained from the investigators.

RESULTS

Twenty-one MRI studies and 14 CSF studies were retrieved. The effect sizes of total tau (t-tau), phosphorylated tau (p-tau) and amyloid beta 42 (a beta 42) ranged from 0.91 to 1.11. The effect size of MTL atrophy was 0.75. Memory performance had an effect size of 1.06. MTL atrophy and memory impairment tended to increase when assessed closer to the moment of diagnosis, whereas effect sizes of CSF biomarkers tended to increase when assessed longer before the diagnosis.

CONCLUSIONS

Memory impairment is a more accurate predictor of early AD than atrophy of MTL on MRI, whereas CSF abnormalities and memory impairment are about equally predictive. Consequently, the CSF and MRI biomarkers are not very sensitive to preclinical AD. CSF markers remain promising, but studies with long follow-up periods in elderly subjects who are normal at baseline are needed to evaluate this promise.

Automatic analysis of medial temporal lobe atrophy from structural MRIs for the early assessment of Alzheimer disease

The purpose of this study is to develop a software for the extraction of the hippocampus and surrounding medial temporal lobe (MTL) regions from T1-weighted magnetic resonance (MR) images with no interactive input from the user, to introduce a novel statistical indicator, computed on the intensities in the automatically extracted MTL regions, which measures atrophy, and to evaluate the accuracy of the newly developed intensity-based measure of MTL atrophy to (a) distinguish between patients with Alzheimer disease (AD), patients with amnestic mild cognitive impairment (aMCI), and elderly controls by using established criteria for patients with AD and aMCI as the reference standard and (b) infer about the clinical outcome of aMCI patients. For the development of the software, the study included 61 patients with mild AD (17 men, 44 women; mean age +/- standard deviation (SD), 75.8 years +/- 7.8; Mini Mental State Examination (MMSE) score, 24.1 +/- 3.1), 42 patients with aMCI (11 men, 31 women; mean age +/- SD, 75.2 years +/- 4.9; MMSE score, 27.9 +/- 1.9), and 30 elderly healthy controls (10 men, 20 women; mean age +/- SD, 74.7 years +/- 5.2; MMSE score, 29.1 +/- 0.8). For the evaluation of the statistical indicator, 150 patients with mild AD (62 men, 88 women; mean age +/- SD, 76.3 years +/- 5.8; MMSE score, 23.2 +/- 4.1), 247 patients with aMCI (143 men, 104 women; mean age +/- SD, 75.3 years +/- 6.7; MMSE score, 27.0 +/- 1.8), and 135 elderly healthy controls (61 men, 74 women; mean age +/- SD, 76.4 years +/- 6.1). Fifty aMCI patients were evaluated every 6 months over a 3 year period to assess conversion to AD. For each participant, two subimages of the MTL regions were automatically extracted from T1-weighted MR images with high spatial resolution. An intensity-based MTL atrophy measure was found to separate control, MCI, and AD cohorts. Group differences were assessed by using two-sample t test. Individual classification was analyzed by using receiver operating characteristic (ROC) curves. Compared to controls, significant differences in the intensity-based MTL atrophy measure were detected in both groups of patients (AD vs controls, 0.28 +/- 0.03 vs 0.34 +/- 0.03, P < 0.001; aMCI vs controls, 0.31 +/- 0.03 vs 0.34 +/- 0.03, P < 0.001). Moreover, the subgroup of aMCI converters was significantly different from controls (0.27 +/- 0.034 vs 0.34 +/- 0.03, P < 0.001). Regarding the ROC curve for intergroup discrimination, the area under the curve was 0.863 for AD patients vs controls, 0.746 for all aMCI patients vs controls, and 0.880 for aMCI converters vs controls. With specificity set at 85%, the sensitivity was 74% for AD vs controls, 45% for aMCI vs controls, and 83% for aMCI converters vs controls. The automated analysis of MTL atrophy in the segmented volume is applied to the early assessment of AD, leading to the discrimination of aMCI converters with an average 3 year follow-up. This procedure can provide additional useful information in the early diagnosis of AD.