Convergent Results from Neuropsychology and from Neuroimaging in Patients with Mild Cognitive Impairment

Elevated serum platelet count inhibits the effects of brain functional changes on cognitive function in patients with mild cognitive impairment: A resting-state functional magnetic resonance imaging study

Objective

Brain function remodeling has been observed in patients with mild cognitive impairment (MCI) and is closely associated with cognitive performance. However, it is not clear if this relationship is influenced by complete blood counts. This study investigated the role of complete blood counts in the relationship between brain function and cognitive performance.

Methods

Twenty-two MCI patients and eighteen controls were enrolled. All subjects underwent resting-state functional magnetic resonance imaging. A neuropsychological battery [Mini-Mental Status Examination, Auditory Verbal Learning Test (AVLT), Symbol Digit Modalities Test, Boston Naming Test (BNT), Shape Trails Test B (STT-B), Rey Complex Figure Test (RCFT), Hamilton Anxiety Rating Scale (HAMA), and Hamilton Depression Scale] was used to assess cognitive function, and MCI patients received complete blood counts tests for red blood cells (RBC), white blood cells, hemoglobin (HGB), monocytes, and platelet counts (PLT).

Results

Compared with controls, MCI patients demonstrated significantly decreased amplitude of low-frequency fluctuation (ALFF) values in the left dorsolateral superior frontal gyrus, left post orbitofrontal cortex, right medial superior frontal gyrus, right insula, and left triangular inferior frontal gyrus. In the MCI group, there were associations between ALFF values of the left hippocampus (HIP.L) and AVLT (p = 0.003) and AVLT-N5 scores (p = 0.001); ALFF values of the right supramarginal gyrus (SMG.R) and BNT scores (p = 0.044); ALFF values of the right superior temporal gyrus (STG.R) and BNT scores (p = 0.022); ALFF values of the left precuneus (PCUN.L) and STT-B time (p = 0.012); and ALFF values of the left caudate nucleus (CAU.L) and RCFT-time (p = 0.036). Moreover, the HAMA scores were negatively correlated with RBC and HGB levels, and positively correlated with monocyte count. The PLT count was positively correlated with STT-B time. Additionally, high PLT count inhibited the effect of ALFF values of the PCUN. L on STT-B performance in MCI patients (p = 0.0207).

Conclusion

ALFF values of the HIP. L, SMG.R, STG. R, PCUN.L, and CAU. L were associated with decreased memory, language, executive function, and visuospatial ability in MCI patients. Notably, elevated PLT count could inhibit the effect of brain functional changes in the PCUN.L on executive function in MCI patients.

A Comparison of Operational Definitions for Mild Cognitive Impairment

Background: Consideration of many tests from different cognitive domains in defining mild cognitive impairment (MCI) is clinical routine, but guidelines for a neuropsychological operationalization of MCI are lacking. Objective: Among different operational MCI criteria, to identify those which are best in predicting either conversion to dementia, or a biomarker profile indicative for Alzheimer’s disease (AD). Methods: Memory-clinic patients without dementia (N = 558; mean age = 66; up to 3 years of follow-up; n = 360 with baseline CSF biomarkers) were included in an observational study using most liberal criteria of cognitive impairment. Four operational definitions of MCI were retrospectively applied: 1) amnestic MCI (word list delayed recall), 2) CERAD total score, 3) comprehensive criteria and 4) base rate corrected CERAD. We compared their accuracy in predicting incident all-cause dementia or AD dementia within three years, or a concurrent CSF Aβ42/tau-ratio indicative of AD. Results: The four definitions overlapped considerably, classified 35–58% of the original sample as impaired and were associated with markedly increased PPVs regarding incident all-cause dementia (39–46% versus 26% of the original sample), AD dementia and AD biomarker positivity. The base-rate corrected MCI definition had the highest prognostic accuracy. Conclusion: he operational criteria examined seem suitable to specify MCI in memory clinic settings, as they identify subjects at high risk of clinical progression. Depending on the neuropsychological battery in use, one or several of these criteria could help to calibrate the clinical judgment of test results, reduce false-positive decisions, and define risk-enriched groups for clinical trials.

Toward a Multimodal Computer-Aided Diagnostic Tool for Alzheimer's Disease Conversion

Alzheimer’s disease (AD) is a progressive neurodegenerative disorder. It is one of the leading sources of morbidity and mortality in the aging population AD cardinal symptoms include memory and executive function impairment that profoundly alters a patient’s ability to perform activities of daily living. People with mild cognitive impairment (MCI) exhibit many of the early clinical symptoms of patients with AD and have a high chance of converting to AD in their lifetime. Diagnostic criteria rely on clinical assessment and brain magnetic resonance imaging (MRI). Many groups are working to help automate this process to improve the clinical workflow. Current computational approaches are focused on predicting whether or not a subject with MCI will convert to AD in the future. To our knowledge, limited attention has been given to the development of automated computer-assisted diagnosis (CAD) systems able to provide an AD conversion diagnosis in MCI patient cohorts followed longitudinally. This is important as these CAD systems could be used by primary care providers to monitor patients with MCI. The method outlined in this paper addresses this gap and presents a computationally efficient pre-processing and prediction pipeline, and is designed for recognizing patterns associated with AD conversion. We propose a new approach that leverages longitudinal data that can be easily acquired in a clinical setting (e.g., T1-weighted magnetic resonance images, cognitive tests, and demographic information) to identify the AD conversion point in MCI subjects with AUC = 84.7. In contrast, cognitive tests and demographics alone achieved AUC = 80.6, a statistically significant difference (n = 669, p < 0.05). We designed a convolutional neural network that is computationally efficient and requires only linear registration between imaging time points. The model architecture combines Attention and Inception architectures while utilizing both cross-sectional and longitudinal imaging and clinical information. Additionally, the top brain regions and clinical features that drove the model’s decision were investigated. These included the thalamus, caudate, planum temporale, and the Rey Auditory Verbal Learning Test. We believe our method could be easily translated into the healthcare setting as an objective AD diagnostic tool for patients with MCI.

Cortical thickness predicts remission of depression with antidepressants in patients with late-life depression and cognitive impairment

BACKGROUND

Depression (DEP) and cognitive impairment (CI) share etiological risk factors, anatomical underpinnings, and interact to produce deleterious treatment outcomes. Both DEP and CI exhibit altered patterns of cortical thickness which may impact the course of antidepressant treatment, though inconsistencies in directionality and affected brain regions have been reported. In this study, we examined the relationship between cortical thickness and treatment outcome in older adults with comorbid DEP-CI.

METHODS

55 patients with DEP-CI received baseline MRI scans as part of a larger clinical trial at NYSPI/Columbia University Medical Center and Duke University Medical Center. Mood was assessed using the Hamilton Depression Rating Scale. Patients received open antidepressant treatment for 8 weeks followed by another 8 weeks of the same medication or switch to another antidepressant for a total of 16 weeks. Cortical thickness was extracted using an automated brain segmentation program (FreeSurfer). Vertex-wise analyses evaluated the relationship between cortical thickness and treatment outcome.

RESULTS

Remitters exhibited diffuse clusters of greater cortical thickness and reduced cortical thickness compared to non-remitters. Thicker baseline middle frontal gyrus most consistently predicted greater likelihood and faster rate of remission. White matter hyperintensities and hippocampal volume were not associated with antidepressant treatment outcome.

LIMITATIONS

MRI was conducted at baseline only and sample size was small.

DISCUSSION

Cortical thickness predicts treatment remission and magnitude of early improvement. Results indicate that individuals with DEP-CI exhibit unique patterns of structural abnormalities compared to their depressed peers without CI that have consequences for their recovery with antidepressant treatment.

Regional Cerebral Associations Between Psychometric Tests and Imaging Biomarkers in Alzheimer's Disease

Recently, imaging biomarkers have gained importance for the characterization of patients with Alzheimer's disease; however, the relationship between regional biomarker expression and cognitive function remains unclear. In our study, we investigated associations between scores on CERAD neuropsychological assessment battery (CERAD-NAB) subtests with regional glucose metabolism, cortical thickness and amyloid deposition in patients with early Alzheimer's disease (AD) using [18F]-fluorodeoxyglucose (FDG), structural MRI, and 11C-Pittsburgh Compound B (PiB) positron emission tomography (PET), respectively. A total of 76 patients (mean age 68.4 ± 8.5 years, 57.9% male) with early AD (median global clinical dementia rating (CDR) score = 0.5, range: 0.5-2.0) were studied. Associations were investigated by correlation and multiple regression analyses. Scores on cognitive subtests were most closely predicted by regional glucose metabolism with explained variance up to a corrected R² of 0.518, followed by cortical thickness and amyloid deposition. Prediction of cognitive subtest performance was increased up to a corrected R² of 0.622 for Word List-Delayed Recall, when biomarker information from multiple regions and multiple modalities were included. For verbal, visuoconstructive and mnestic domains the closest associations with FDG-PET imaging were found in the left lateral temporal lobe, right parietal lobe, and posterior cingulate cortex, respectively. Decreased cortical thickness in parietal regions was most predictive of impaired subtest performance. Remarkably, cerebral amyloid deposition significantly predicted cognitive function in about half of the subtests but with smaller extent of variance explained (corrected R² ≤ 0.220). We conclude that brain metabolism and atrophy affect cognitive performance in a regionally distinct way. Significant predictions of cognitive function by PiB-PET in half of CERAD-NAB subtests suggest functional relevance even in symptomatic patients with AD, challenging the concept of plateauing cortical amyloid deposition early in the disease course. Our results underscore the complex spatial relationship between different imaging biomarkers.

The Brain Metabolic Correlates of the Main Indices of Neuropsychological Assessment in Alzheimer's Disease

Background: The study aimed to investigate the relationships between F-18 fluorodeoxyglucose (18F)FDG uptake and neuropsychological assessment in Alzheimer’s disease (AD). Methods: We evaluated 116 subjects with AD according to the NINCDS-ADRDA criteria. All the subjects underwent a brain PET/CT with (18F)FDG, cerebrospinal fluid (CSF) assay, mini-mental state examination (MMSE) and further neuropsychological tests: Rey auditory verbal learning test, immediate recall (RAVLT immediate); Rey auditory verbal learning test, delayed recall (RAVLT, delayed); Rey complex figure test, copy (RCFT, copy); Rey complex figure test, delayed recall (RCFT, delayed); Raven’s colored progressive matrices (RCPM); phonological word fluency test (PWF) and Stroop test. We performed the statistical analysis by using statistical parametric mapping (SPM12; Wellcome Department of Cognitive Neurology, London, UK). Results: A significant relationship has been reported between (18F)FDG uptake and RAVLT immediate test in Brodmann area (BA)37 and BA22 and with RCFT, copy in BA40, and BA7. We did not find any significant relationships with other tests. Conclusion: In the AD population, brain (18F)FDG uptake is moderately related to the neuropsychological assessment, suggesting a limited impact on statistical data analysis of glucose brain metabolism.

Neuropsychological assessment and cortisol levels in biofluids from early Alzheimer's disease patients

Cortisol dysregulation is proposed as a factor in the development of Alzheimer's disease (AD). AD patients can show high cortisol levels in prodromal phases of AD, early enough that neuropsychological alterations exist but activities of daily living remain unimpaired. Nevertheless, it is unknown if biofluid cortisol levels can have some AD predictive power together with neuropsychological assessment in prodromal stages in comparison with other cognitive disorders. In this work, an analytical method based on ultra-performance liquid chromatography coupled to tandem mass spectrometry (UPLC-MS/MS) was applied to determine the cortisol levels in different biofluids (urine, plasma, saliva, cerebrospinal fluid). Early AD patients and non-AD patients recruited at out-patient neurological unit were classified from the standard cerebrospinal fluid biomarkers levels (β-amyloid, tau, phosphorylated tau), and studied with an extensive neuropsychological assessment including global, neuropsychological, functional and affective scales. We used a logistic regression model to discriminate between the AD and non-AD groups. Higher plasma cortisol levels were found in the AD group than in the non-AD group (p < 0.001). Regarding neuropsychological evaluation, delayed memory was used as representative of the neuropsychological status, and lower scores were obtained in the AD group (p < 0.001). The prediction model, including plasma cortisol levels and delayed memory scores, achieved an AUC of 0.93, as well as a sensitivity of 97% and a specificity of 69.4%. In conclusion, plasma cortisol levels and delayed memory scores were specifically impaired in early AD, allowing the development of a new diagnostic model which could be employed as a very satisfactory screening system.