Dictionary Learning and Sparse Coding-based Denoising for High-Resolution Task Functional Connectivity MRI Analysis

We propose a novel denoising framework for task functional Magnetic Resonance Imaging (tfMRI) data to delineate the high-resolution spatial pattern of the brain functional connectivity via dictionary learning and sparse coding (DLSC). In order to address the limitations of the unsupervised DLSC-based fMRI studies, we utilize the prior knowledge of task paradigm in the learning step to train a data-driven dictionary and to model the sparse representation. We apply the proposed DLSC-based method to Human Connectome Project (HCP) motor tfMRI dataset. Studies on the functional connectivity of cerebrocerebellar circuits in somatomotor networks show that the DLSC-based denoising framework can significantly improve the prominent connectivity patterns, in comparison to the temporal non-local means (tNLM)-based denoising method as well as the case without denoising, which is consistent and neuroscientifically meaningful within motor area. The promising results show that the proposed method can provide an important foundation for the high-resolution functional connectivity analysis, and provide a better approach for fMRI preprocessing.Comment: 8 pages, 3 figures, MLMI201

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

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

Amyloid-driven disruption of default mode network connectivity in cognitively healthy individuals

Cortical accumulation of amyloid beta is one of the first events of Alzheimer's disease pathophysiology, and has been suggested to follow a consistent spatiotemporal ordering, starting in the posterior cingulate cortex, precuneus and medio-orbitofrontal cortex. These regions overlap with those of the default mode network, a brain network also involved in memory functions. Aberrant default mode network functional connectivity and higher network sparsity have been reported in prodromal and clinical Alzheimer's disease. We investigated the association between amyloid burden and default mode network connectivity in the preclinical stage of Alzheimer's disease and its association with longitudinal memory decline. We included 173 participants, in which amyloid burden was assessed both in CSF by the amyloid beta 42/40 ratio, capturing the soluble part of amyloid pathology, and in dynamic PET scans calculating the non-displaceable binding potential in early-stage regions. The default mode network was identified with resting-state functional MRI. Then, we calculated functional connectivity in the default mode network, derived from independent component analysis, and eigenvector centrality, a graph measure recursively defining important nodes on the base of their connection with other important nodes. Memory was tested at baseline, 2- and 4-year follow-up. We demonstrated that higher amyloid burden as measured by both CSF amyloid beta 42/40 ratio and non-displaceable binding potential in the posterior cingulate cortex was associated with lower functional connectivity in the default mode network. The association between amyloid burden (CSF and non-displaceable binding potential in the posterior cingulate cortex) and aberrant default mode network connectivity was confirmed at the voxel level with both functional connectivity and eigenvector centrality measures, and it was driven by voxel clusters localized in the precuneus, cingulate, angular and left middle temporal gyri. Moreover, we demonstrated that functional connectivity in the default mode network predicts longitudinal memory decline synergistically with regional amyloid burden, as measured by non-displaceable binding potential in the posterior cingulate cortex. Taken together, these results suggest that early amyloid beta deposition is associated with aberrant default mode network connectivity in cognitively healthy individuals and that default mode network connectivity markers can be used to identify subjects at risk of memory decline

White matter microstructure disruption in early stage amyloid pathology.

Introduction: Amyloid beta (Aβ) accumulation is the first pathological hallmark of Alzheimer's disease (AD), and it is associated with altered white matter (WM) microstructure. We aimed to investigate this relationship at a regional level in a cognitively unimpaired cohort. Methods: We included 179 individuals from the European Medical Information Framework for AD (EMIF‐AD) preclinAD study, who underwent diffusion magnetic resonance (MR) to determine tract‐level fractional anisotropy (FA); mean, radial, and axial diffusivity (MD/RD/AxD); and dynamic [18F]flutemetamol) positron emission tomography (PET) imaging to assess amyloid burden. Results: Regression analyses showed a non‐linear relationship between regional amyloid burden and WM microstructure. Low amyloid burden was associated with increased FA and decreased MD/RD/AxD, followed by decreased FA and increased MD/RD/AxD upon higher amyloid burden. The strongest association was observed between amyloid burden in the precuneus and body of the corpus callosum (CC) FA and diffusivity (MD/RD) measures. In addition, amyloid burden in the anterior cingulate cortex strongly related to AxD and RD measures in the genu CC. Discussion: Early amyloid deposition is associated with changes in WM microstructure. The non‐linear relationship might reflect multiple stages of axonal damage

Visual assessment of [¹⁸F]flutemetamol PET images can detect early amyloid pathology and grade its extent

PURPOSE: To investigate the sensitivity of visual read (VR) to detect early amyloid pathology and the overall utility of regional VR. METHODS: [¹⁸F]flutemetamol PET images of 497 subjects (ALFA+ N = 352; ADC N = 145) were included. Scans were visually assessed according to product guidelines, recording the number of positive regions (0-5) and a final negative/positive classification. Scans were quantified using the standard and regional Centiloid (CL) method. The agreement between VR-based classification and published CL-based cut-offs for early (CL = 12) and established (CL = 30) pathology was determined. An optimal CL cut-off maximizing Youden's index was derived. Global and regional CL quantification was compared to VR. Finally, 28 post-mortem cases from the [¹⁸F]flutemetamol phase III trial were included to assess the percentage agreement between VR and neuropathological classification of neuritic plaque density. RESULTS: VR showed excellent agreement against CL = 12 (κ = .89, 95.2%) and CL = 30 (κ = .88, 95.4%) cut-offs. ROC analysis resulted in an optimal CL = 17 cut-off against VR (sensitivity = 97.9%, specificity = 97.8%). Each additional positive VR region corresponded to a clear increase in global CL. Regional VR was also associated with regional CL quantification. Compared to mCERAD_{SOT}-based classification (i.e., any region mCERAD_{SOT} > 1.5), VR was in agreement in 89.3% of cases, with 13 true negatives, 12 true positives, and 3 false positives (FP). Regional sparse-to-moderate neuritic and substantial diffuse Aβ plaque was observed in all FP cases. Regional VR was also associated with regional plaque density. CONCLUSION: VR is an appropriate method for assessing early amyloid pathology and that grading the extent of visual amyloid positivity could present clinical value

Cognitive reserve and clinical progression in Alzheimer disease: A paradoxical relationship

OBJECTIVE: To investigate the relationship between cognitive reserve (CR) and clinical progression across the Alzheimer disease (AD) spectrum. // METHODS: We selected 839 β-amyloid (Aβ)-positive participants with normal cognition (NC, n = 175), mild cognitive impairment (MCI, n = 437), or AD dementia (n = 227) from the Alzheimer's Disease Neuroimaging Initiative (ADNI). CR was quantified using standardized residuals (W scores) from a (covariate-adjusted) linear regression with global cognition (13-item Alzheimer's Disease Assessment Scale-cognitive subscale) as an independent variable of interest, and either gray matter volumes or white matter hyperintensity volume as dependent variables. These W scores, reflecting whether an individual's degree of cerebral damage is lower or higher than clinically expected, were tested as predictors of diagnostic conversion (i.e., NC to MCI/AD dementia, or MCI to AD dementia) and longitudinal changes in memory (ADNI-MEM) and executive functions (ADNI-EF). // RESULTS: The median follow-up period was 24 months (interquartile range 6-42). Corrected for age, sex, APOE4 status, and baseline cerebral damage, higher gray matter volume-based W scores (i.e., greater CR) were associated with a lower diagnostic conversion risk (hazard ratio [HR] 0.22, p < 0.001) and slower decline in memory (β = 0.48, p < 0.001) and executive function (β = 0.67, p < 0.001). Stratified by disease stage, we found similar results for NC (diagnostic conversion: HR 0.30, p = 0.038; ADNI-MEM: β = 0.52, p = 0.028; ADNI-EF: β = 0.42, p = 0.077) and MCI (diagnostic conversion: HR 0.21, p < 0.001; ADNI-MEM: β = 0.43, p = 0.003; ADNI-EF: β = 0.59, p < 0.001), but opposite findings (i.e., more rapid decline) for AD dementia (ADNI-MEM: β = -0.91, p = 0.002; ADNI-EF: β = -0.77, p = 0.081). // CONCLUSIONS: Among Aβ-positive individuals, greater CR related to attenuated clinical progression in predementia stages of AD, but accelerated cognitive decline after the onset of dementia

Scale‐free brain dynamics under physical and psychological distress: Pre‐treatment effects in women diagnosed with breast cancer

Stressful life events are related to negative outcomes, including physical and psychological manifestations of distress, and behavioral deficits. Patients diagnosed with breast cancer report impaired attention and working memory prior to adjuvant therapy, which may be induced by distress. In this article, we examine whether brain dynamics show systematic changes due to the distress associated with cancer diagnosis. We hypothesized that impaired working memory is associated with suppression of “long‐memory” neuronal dynamics; we tested this by measuring scale‐free (“fractal”) brain dynamics, quantified by the Hurst exponent (H). Fractal scaling refers to signals that do not occur at a specific time‐scale, possessing a spectral power curve P(f)∝f−β; they are “long‐memory” processes, with significant autocorrelations. In a BOLD functional magnetic resonance imaging study, we scanned three groups during a working memory task: women scheduled to receive chemotherapy or radiotherapy and aged‐matched controls. Surprisingly, patients' BOLD signal exhibited greater H with increasing intensity of anticipated treatment. However, an analysis of H and functional connectivity against self‐reported measures of psychological distress (Worry, Anxiety, Depression) and physical distress (Fatigue, Sleep problems) revealed significant interactions. The modulation of (Worry, Anxiety) versus (Fatigue, Sleep Problems, Depression) showed the strongest effect, where higher worry and lower fatigue was related to reduced H in regions involved in visuospatial search, attention, and memory processing. This is also linked to decreased functional connectivity in these brain regions. Our results indicate that the distress associated with cancer diagnosis alters BOLD scaling, and H is a sensitive measure of the interaction between psychological versus physical distress. Hum Brain Mapp 36:1077–1092, 2015. © 2014 Wiley Periodicals, Inc.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/110706/1/hbm22687-sup-0001-suppinfo01.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/110706/2/hbm22687.pd

Multi-tracer model for staging cortical amyloid deposition using PET imaging

OBJECTIVE: To develop and evaluate a model for staging cortical amyloid deposition using PET with high generalizability. METHODS: 3027 subjects (1763 Cognitively Unimpaired (CU), 658 Impaired, 467 Alzheimer's disease (AD) dementia, 111 non-AD dementia, and 28 with missing diagnosis) from six cohorts (EMIF-AD, ALFA, ABIDE, ADC, OASIS-3, ADNI) who underwent amyloid PET were retrospectively included; 1049 subjects had follow-up scans. Applying dataset-specific cut-offs to global Standard Uptake Value ratio (SUVr) values from 27 regions, single-tracer and pooled multi-tracer regional rankings were constructed from the frequency of abnormality across 400 CU subjects (100 per tracer). The pooled multi-tracer ranking was used to create a staging model consisting of four clusters of regions as it displayed a high and consistent correlation with each single-tracer ranking. Relationships between amyloid stage, clinical variables and longitudinal cognitive decline were investigated. RESULTS: SUVr abnormality was most frequently observed in cingulate, followed by orbitofrontal, precuneal, and insular cortices, then the associative, temporal and occipital regions. Abnormal amyloid levels based on binary global SUVr classification were observed in 1.0%, 5.5%, 17.9%, 90.0%, and 100.0% of stage 0-4 subjects, respectively. Baseline stage predicted decline in MMSE (ADNI: N=867, F=67.37, p3000 subjects across cohorts and radiotracers, and detects pre-global amyloid burden and distinct risk profiles of cognitive decline within globally amyloid-positive subjects

Assessing Amyloid Pathology in Cognitively Normal Subjects Using {18}^F-Flutemetamol PET: Comparing Visual Reads and Quantitative Methods

Our objective was to determine the optimal approach for assessing amyloid disease in a cognitively normal elderly population. Methods: Dynamic {18}^F-flutemetamol PET scans were acquired using a coffee-break protocol (a 0- to 30-min scan and a 90- to 110-min scan) on 190 cognitively normal elderly individuals (mean age, 70.4 y; 60% female). Parametric images were generated from SUV ratio (SUVr) and nondisplaceable binding potential (BP_{ND}) methods, with cerebellar gray matter as a reference region, and were visually assessed by 3 trained readers. Interreader agreement was calculated using κ-statistics, and semiquantitative values were obtained. Global cutoffs were calculated for both SUVr and BP_{ND} using a receiver-operating-characteristic analysis and the Youden index. Visual assessment was related to semiquantitative classifications. Results: Interreader agreement in visual assessment was moderate for SUVr (κ = 0.57) and good for BP_{ND} images (κ = 0.77). There was discordance between readers for 35 cases (18%) using SUVr and for 15 cases (8%) using BP_{ND}, with 9 overlapping cases. For the total cohort, the mean (±SD) SUVr and BP_{ND} were 1.33 (±0.21) and 0.16 (±0.12), respectively. Most of the 35 cases (91%) for which SUVr image assessment was discordant between readers were classified as negative based on semiquantitative measurements. Conclusion: The use of parametric BP_{ND} images for visual assessment of {18}^F-flutemetamol in a population with low amyloid burden improves interreader agreement. Implementing semiquantification in addition to visual assessment of SUVr images can reduce false-positive classification in this population

Narcissism and prosocial behavior

There are many motivations for prosocial behavior, some more altruistic and some more egoistic. We posit that more narcissistic people may perform prosocial acts strategically, for example, to improve their reputations or to receive something in return