Rey's Auditory Verbal Learning Test scores can be predicted from whole brain MRI in Alzheimer's disease

Empirical examination of the replicability of associations between brain structure and psychological variables

Linking interindividual differences in psychological phenotype to variations in brain structure is an old dream for psychology and a crucial question for cognitive neurosciences. Yet, replicability of the previously-reported 'structural brain behavior' (SBB)-associations has been questioned, recently. Here, we conducted an empirical investigation, assessing replicability of SBB among heathy adults. For a wide range of psychological measures, the replicability of associations with gray matter volume was assessed. Our results revealed that among healthy individuals 1) finding an association between performance at standard psychological tests and brain morphology is relatively unlikely 2) significant associations, found using an exploratory approach, have overestimated effect sizes and 3) can hardly be replicated in an independent sample. After considering factors such as sample size and comparing our findings with more replicable SBB-associations in a clinical cohort and replicable associations between brain structure and non-psychological phenotype, we discuss the potential causes and consequences of these findings.

A Clinical Trial of Transcranial Electromagnetic Treatment in Alzheimer's Disease: Cognitive Enhancement and Associated Changes in Cerebrospinal Fluid, Blood, and Brain Imaging

BACKGROUND

Small aggregates (oligomers) of the toxic proteins amyloid-β (Aβ) and phospho-tau (p-tau) are essential contributors to Alzheimer's disease (AD). In mouse models for AD or human AD brain extracts, Transcranial Electromagnetic Treatment (TEMT) disaggregates both Aβ and p-tau oligomers, and induces brain mitochondrial enhancement. These apparent "disease-modifying" actions of TEMT both prevent and reverse memory impairment in AD transgenic mice.

OBJECTIVE

To evaluate the safety and initial clinical efficacy of TEMT against AD, a comprehensive open-label clinical trial was performed.

METHODS

Eight mild/moderate AD patients were treated with TEMT in-home by their caregivers for 2 months utilizing a unique head device. TEMT was given for two 1-hour periods each day, with subjects primarily evaluated at baseline, end-of-treatment, and 2 weeks following treatment completion.

RESULTS

No deleterious behavioral effects, discomfort, or physiologic changes resulted from 2 months of TEMT, as well as no evidence of tumor or microhemorrhage induction. TEMT induced clinically important and statistically significant improvements in ADAS-cog, as well as in the Rey AVLT. TEMT also produced increases in cerebrospinal fluid (CSF) levels of soluble Aβ1-40 and Aβ1-42, cognition-related changes in CSF oligomeric Aβ, a decreased CSF p-tau/Aβ1-42 ratio, and reduced levels of oligomeric Aβ in plasma. Pre- versus post-treatment FDG-PET brain scans revealed stable cerebral glucose utilization, with several subjects exhibiting enhanced glucose utilization. Evaluation of diffusion tensor imaging (fractional anisotropy) scans in individual subjects provided support for TEMT-induced increases in functional connectivity within the cognitively-important cingulate cortex/cingulum.

CONCLUSION

TEMT administration to AD subjects appears to be safe, while providing cognitive enhancement, changes to CSF/blood AD markers, and evidence of stable/enhanced brain connectivity.

Associations between polygenic risk scores for four psychiatric illnesses and brain structure using multivariate pattern recognition

Psychiatric illnesses are complex and polygenic. They are associated with widespread alterations in the brain, which are partly influenced by genetic factors. There have been some attempts to relate polygenic risk scores (PRS) - a measure of the overall genetic risk an individual carries for a disorder - to brain structure using univariate methods. However, PRS are likely associated with distributed and covarying effects across the brain. We therefore used multivariate machine learning in this proof-of-principle study to investigate associations between brain structure and PRS for four psychiatric disorders; attention deficit-hyperactivity disorder (ADHD), autism, bipolar disorder and schizophrenia. The sample included 213 individuals comprising patients with depression (69), bipolar disorder (33), and healthy controls (111). The five psychiatric PRSs were calculated based on summary data from the Psychiatric Genomics Consortium. T1-weighted magnetic resonance images were obtained and voxel-based morphometry was implemented in SPM12. Multivariate relevance vector regression was implemented in the Pattern Recognition for Neuroimaging Toolbox (PRoNTo). Across the whole sample, a multivariate pattern of grey matter significantly predicted the PRS for autism (r = 0.20, pFDR = 0.03; MSE = 4.20 × 10-5, pFDR = 0.02). For the schizophrenia PRS, the MSE was significant (MSE = 1.30 × 10-5, pFDR = 0.02) although the correlation was not (r = 0.15, pFDR = 0.06). These results lend support to the hypothesis that polygenic liability for autism and schizophrenia is associated with widespread changes in grey matter concentrations. These associations were seen in individuals not affected by these disorders, indicating that this is not driven by the expression of the disease, but by the genetic risk captured by the PRSs.

Cognitive Assessment Prediction in Alzheimer's Disease by Multi-Layer Multi-Target Regression

Accurate and automatic prediction of cognitive assessment from multiple neuroimaging biomarkers is crucial for early detection of Alzheimer's disease. The major challenges arise from the nonlinear relationship between biomarkers and assessment scores and the inter-correlation among them, which have not yet been well addressed. In this paper, we propose multi-layer multi-target regression (MMR) which enables simultaneously modeling intrinsic inter-target correlations and nonlinear input-output relationships in a general compositional framework. Specifically, by kernelized dictionary learning, the MMR can effectively handle highly nonlinear relationship between biomarkers and assessment scores; by robust low-rank linear learning via matrix elastic nets, the MMR can explicitly encode inter-correlations among multiple assessment scores; moreover, the MMR is flexibly and allows to work with non-smooth ℓ2,1-norm loss function, which enables calibration of multiple targets with disparate noise levels for more robust parameter estimation. The MMR can be efficiently solved by an alternating optimization algorithm via gradient descent with guaranteed convergence. The MMR has been evaluated by extensive experiments on the ADNI database with MRI data, and produced high accuracy surpassing previous regression models, which demonstrates its great effectiveness as a new multi-target regression model for clinical multivariate prediction.

MRI Asymmetry Index of Hippocampal Subfields Increases Through the Continuum From the Mild Cognitive Impairment to the Alzheimer's Disease

Objective: It is well-known that the hippocampus presents significant asymmetry in Alzheimer's disease (AD) and that difference in volumes between left and right exists and varies with disease progression. However, few works investigated whether the asymmetry degree of subfields of hippocampus changes through the continuum from Mild Cognitive Impairment (MCI) to AD. Thus, aim of the present work was to evaluate the Asymmetry Index (AI) of hippocampal substructures as possible MRI biomarkers of Dementia. Moreover, we aimed to assess whether the subfields presented peculiar differences between left and right hemispheres. We also investigated the relationship between the asymmetry magnitude in hippocampal subfields and the decline of verbal memory as assessed by Rey's auditory verbal learning test (RAVLT). Methods: Four-hundred subjects were selected from ADNI, equally divided into healthy controls (HC), AD, stable MCI (sMCI), and progressive MCI (pMCI). The structural baseline T1s were processed with FreeSurfer 6.0 and volumes of whole hippocampus (WH) and 12 subfields were extracted. The AI was calculated as: (|Left-Right|/(Left+Right))*100. ANCOVA was used for evaluating AI differences between diagnoses, while paired t-test was applied for assessing changes between left and right volumes, separately for each group. Partial correlation was performed for exploring relationship between RAVLT summary scores (Immediate, Learning, Forgetting, Percent Forgetting) and hippocampal substructures AI. The statistical threshold was Bonferroni corrected p < 0.05/13 = 0.0038. Results: We found a general trend of increased degree of asymmetry with increasing severity of diagnosis. Indeed, AD presented the higher magnitude of asymmetry compared with HC, sMCI and pMCI, in the WH (AI mean 5.13 ± 4.29 SD) and in each of its twelve subfields. Moreover, we found in AD a significant negative correlation (r = -0.33, p = 0.00065) between the AI of parasubiculum (mean 12.70 ± 9.59 SD) and the RAVLT Learning score (mean 1.70 ± 1.62 SD). Conclusions: Our findings showed that hippocampal subfields AI varies differently among the four groups HC, sMCI, pMCI, and AD. Moreover, we found-for the first time-that hippocampal substructures had different sub-patterns of lateralization compared with the whole hippocampus. Importantly, the severity in learning rate was correlated with pathological high degree of asymmetry in parasubiculum of AD patients.

The association between "Brain-Age Score" (BAS) and traditional neuropsychological screening tools in Alzheimer's disease

INTRODUCTION

We present the Brain-Age Score (BAS) as a magnetic resonance imaging (MRI)-based index for Alzheimer's disease (AD). We developed a fully automated framework for estimating the BAS in healthy controls (HCs) and individuals with mild cognitive impairment (MCI) or AD, using MRI scans.

METHODS

We trained the proposed framework using 385 HCs from the IXI and OASIS datasets and evaluated 146 HCs, 102 stable-MCI (sMCI), 112 progressive-MCI (pMCI), and 147 AD patients from the J-ADNI dataset. We used a correlation test to determine the association between the BAS and four traditional screening tools of AD: the Mini-Mental State Examination (MMSE), Clinical Dementia Ratio (CDR), Alzheimer's Disease Assessment Score (ADAS), and Functional Assessment Questionnaire (FAQ). Furthermore, we assessed the association between BAS and anatomical MRI measurements: the normalized gray matter (nGM), normalized white matter (nWM), normalized cerebrospinal fluid (nCSF), mean cortical thickness as well as hippocampus volume.

RESULTS

The correlation results demonstrated that the BAS is in line with traditional screening tools of AD (i.e., the MMSE, CDR, ADAS, and FAQ scores) as well as anatomical MRI measurements (i.e., nGM, nCSF, mean cortical thickness, and hippocampus volume).

DISCUSSION

The BAS may be useful for diagnosing the brain atrophy level and can be a reliable automated index for clinical applications and neuropsychological screening tools.

Cognitive impairment in patients with psoriasis: A matched case-control study

BACKGROUND

In the past decade, a few studies have suggested that psoriasis could be associated with the presence of mild cognitive deficits.

OBJECTIVES

The aim of the present matched case-control study was to investigate several cognitive domains (executive functions, verbal memory, attention, and language) in a sample of outpatients with psoriasis. We also investigated whether cognitive impairment was associated with poor health-related quality of life (HRQoL) in patients with psoriasis.

METHODS

Fifty adult outpatients and 50 age- and sex-matched healthy controls were administered a battery of neuropsychological tests investigating major cognitive domains, psychopathology (anxiety and depression), alexithymia, and HRQoL.

RESULTS

At the bivariate level, psoriasis patients (compared to healthy controls) performed worse on most of the neuropsychological tests, and they also reported more anxiety and depressive symptoms, higher scores for alexithymia, and worse physical and mental health. At the multivariate level, cognitive performance was independently associated with psoriasis even when controlling for psychopathology and alexithymia.

CONCLUSIONS

Patients with psoriasis show impaired cognitive performance, high levels of anxiety and depression, and impaired quality of life. Based on the current results, clinicians should assess the presence of psychological symptoms in their patients and evaluate whether the presence of cognitive deficits is limiting the patients' ability to cope with the disease.

Regularized Modal Regression with Applications in Cognitive Impairment Prediction

Linear regression models have been successfully used to function estimation and model selection in high-dimensional data analysis. However, most existing methods are built on least squares with the mean square error (MSE) criterion, which are sensitive to outliers and their performance may be degraded for heavy-tailed noise. In this paper, we go beyond this criterion by investigating the regularized modal regression from a statistical learning viewpoint. A new regularized modal regression model is proposed for estimation and variable selection, which is robust to outliers, heavy-tailed noise, and skewed noise. On the theoretical side, we establish the approximation estimate for learning the conditional mode function, the sparsity analysis for variable selection, and the robustness characterization. On the application side, we applied our model to successfully improve the cognitive impairment prediction using the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort data.

The Abnormality of Topological Asymmetry between Hemispheric Brain White Matter Networks in Alzheimer's Disease and Mild Cognitive Impairment

A large number of morphology-based studies have previously reported a variety of regional abnormalities in hemispheric asymmetry in Alzheimer’s disease (AD). Recently, neuroimaging studies have revealed changes in the topological organization of the structural network in AD. However, little is known about the alterations in topological asymmetries. In the present study, we used diffusion tensor image tractography to construct the hemispheric brain white matter networks of 25 AD patients, 95 mild cognitive impairment (MCI) patients, and 48 normal control (NC) subjects. Graph theoretical approaches were then employed to estimate hemispheric topological properties. Rightward asymmetry in both global and local network efficiencies were observed between the two hemispheres only in AD patients. The brain regions/nodes exhibiting increased rightward asymmetry in both AD and MCI patients were primarily located in the parahippocampal gyrus and cuneus. The observed rightward asymmetry was attributed to changes in the topological properties of the left hemisphere in AD patients. Finally, we found that the abnormal hemispheric asymmetries of brain network properties were significantly correlated with memory performance (Rey’s Auditory Verbal Learning Test). Our findings provide new insights into the lateralized nature of hemispheric disconnectivity and highlight the potential for using hemispheric asymmetry of brain network measures as biomarkers for AD.