MRI-based Deep Learning Assessment of Amyloid, Tau, and Neurodegeneration Biomarker Status across the Alzheimer Disease Spectrum.

Background PET can be used for amyloid-tau-neurodegeneration (ATN) classification in Alzheimer disease, but incurs considerable cost and exposure to ionizing radiation. MRI currently has limited use in characterizing ATN status. Deep learning techniques can detect complex patterns in MRI data and have potential for noninvasive characterization of ATN status. Purpose To use deep learning to predict PET-determined ATN biomarker status using MRI and readily available diagnostic data. Materials and Methods MRI and PET data were retrospectively collected from the Alzheimer's Disease Imaging Initiative. PET scans were paired with MRI scans acquired within 30 days, from August 2005 to September 2020. Pairs were randomly split into subsets as follows: 70% for training, 10% for validation, and 20% for final testing. A bimodal Gaussian mixture model was used to threshold PET scans into positive and negative labels. MRI data were fed into a convolutional neural network to generate imaging features. These features were combined in a logistic regression model with patient demographics, APOE gene status, cognitive scores, hippocampal volumes, and clinical diagnoses to classify each ATN biomarker component as positive or negative. Area under the receiver operating characteristic curve (AUC) analysis was used for model evaluation. Feature importance was derived from model coefficients and gradients. Results There were 2099 amyloid (mean patient age, 75 years ± 10 [SD]; 1110 male), 557 tau (mean patient age, 75 years ± 7; 280 male), and 2768 FDG PET (mean patient age, 75 years ± 7; 1645 male) and MRI pairs. Model AUCs for the test set were as follows: amyloid, 0.79 (95% CI: 0.74, 0.83); tau, 0.73 (95% CI: 0.58, 0.86); and neurodegeneration, 0.86 (95% CI: 0.83, 0.89). Within the networks, high gradients were present in key temporal, parietal, frontal, and occipital cortical regions. Model coefficients for cognitive scores, hippocampal volumes, and APOE status were highest. Conclusion A deep learning algorithm predicted each component of PET-determined ATN status with acceptable to excellent efficacy using MRI and other available diagnostic data. © RSNA, 2023 Supplemental material is available for this article.

Column-based cortical depth analysis of the diffusion anisotropy and radiality in submillimeter whole-brain diffusion tensor imaging of the human cortical gray matter in vivo.

High-resolution diffusion tensor imaging (DTI) can noninvasively probe the microstructure of cortical gray matter in vivo. In this study, 0.9-mm isotropic whole-brain DTI data were acquired in healthy subjects with an efficient multi-band multi-shot echo-planar imaging sequence. A column-based analysis that samples the fractional anisotropy (FA) and radiality index (RI) along radially oriented cortical columns was then performed to quantitatively analyze the FA and RI dependence on the cortical depth, cortical region, cortical curvature, and cortical thickness across the whole brain, which has not been simultaneously and systematically investigated in previous studies. The results showed characteristic FA and RI vs. cortical depth profiles, with an FA local maximum and minimum (or two inflection points) and a single RI maximum at intermediate cortical depths in most cortical regions, except for the postcentral gyrus where no FA peaks and a lower RI were observed. These results were consistent between repeated scans from the same subjects and across different subjects. They were also dependent on the cortical curvature and cortical thickness in that the characteristic FA and RI peaks were more pronounced i) at the banks than at the crown of gyri or at the fundus of sulci and ii) as the cortical thickness increases. This methodology can help characterize variations in microstructure along the cortical depth and across the whole brain in vivo, potentially providing quantitative biomarkers for neurological disorders.

Changes in resting state networks in high school football athletes across a single season.

OBJECTIVE: The aim of this pilot cohort study was to examine changes in the organization of resting-state brain networks in high school football athletes and its relationship to exposure to on-field head impacts over the course of a single season. METHODS: Seventeen male high school football players underwent functional magnetic resonance imaging and computerized neurocognitive testing (CNS Vital Signs) before the start of contact practices and again after the conclusion of the season. The players were equipped with helmet accelerometer systems (Head Impact Telemetry System) to record head impacts in practices and games. Graph theory analysis was applied to study intranetwork local efficiency and strength of connectivity within six anatomically defined brain networks. RESULTS: We observed a significant decrease in the local efficiency (-24.9 ± 51.4%, r = 0.7, p < 0.01) and strength (-14.5 ± 26.8%, r = 0.5, p < 0.01) of functional connectivity within the frontal lobe resting-state network and strength within the parietal lobe resting-state network (-7.5 ± 17.3%, r = 0.1, p < 0.01), as well as a concomitant increase in the local efficiency (+55.0 +/- 59.8%, r = 0.5, p < 0.01) and strength (+47.4 +/- 47.3%, r = 0.5, p < 0.01) within the mediotemporal networks. These alterations in network organization were associated with changes in performance on verbal memory (p < 0.05) and executive function (p < 0.05). We did not observe a significant relationship between the frequency or cumulative magnitude of impacts sustained during the season and neurocognitive or imaging outcomes (p > 0.05). CONCLUSION: Our findings suggest the efficiency and strength of resting-state networks are altered across a season of high school football, but the association of exposure levels to subconcussive impacts is unclear. ADVANCES IN KNOWLEDGE: The efficiency of resting-state networks is dynamic in high school football athletes; such changes may be related to impacts sustained during the season, though further study is needed.

Impact of Computerized Cognitive Training on Default Mode Network Connectivity in Subjects at Risk for Alzheimer's Disease: A 78-week Randomized Controlled Trial.

BACKGROUND: Mild cognitive impairment (MCI) represents a high risk group for Alzheimer's disease (AD). Computerized Cognitive Games Training (CCT) is an investigational strategy to improve targeted functions in MCI through the modulation of cognitive networks. OBJECTIVE: The goal of this study was to examine the effect of CCT versus a non-targeted active brain exercise on functional cognitive networks. METHODS: 107 patients with MCI were randomized to CCT or web-based crossword puzzles. Resting-state functional MRI (fMRI) was obtained at baseline and 18 months to evaluate differences in fMRI measured within- and between-network functional connectivity (FC) of the default mode network (DMN) and other large-scale brain networks: the executive control, salience, and sensorimotor networks. RESULTS: There were no differences between crosswords and games in the primary outcome, within-network DMN FC across all subjects. However, secondary analyses suggest differential effects on between-network connectivity involving the DMN and SLN, and within-network connectivity of the DMN in subjects with late MCI. Paradoxically, in both cases, there was a decrease in FC for games and an increase for the crosswords control (p < 0.05), accompanied by lesser cognitive decline in the crosswords group. CONCLUSION: Results do not support a differential impact on within-network DMN FC between games and crossword puzzle interventions. However, crossword puzzles might result in cognitively beneficial remodeling between the DMN and other networks in more severely impaired MCI subjects, parallel to the observed clinical benefits.

Prenatal heroin exposure alters brain morphology and connectivity in adolescent mice.

The United States is experiencing a dramatic increase in maternal opioid misuse and, consequently, the number of individuals exposed to opioids in utero. Prenatal opioid exposure has both acute and long-lasting effects on health and wellbeing. Effects on the brain, often identified at school age, manifest as cognitive impairment, attention deficit, and reduced scholastic achievement. The neurobiological basis for these effects is poorly understood. Here, we examine how in utero exposure to heroin affects brain development into early adolescence in a mouse model. Pregnant C57BL/6J mice received escalating doses of heroin twice daily on gestational days 4-18. The brains of offspring were assessed on postnatal day 28 using 9.4 T diffusion MRI of postmortem specimens at 36 µm resolution. Whole-brain volumes and the volumes of 166 bilateral regions were compared between heroin-exposed and control offspring. We identified a reduction in whole-brain volume in heroin-exposed offspring and heroin-associated volume changes in 29 regions after standardizing for whole-brain volume. Regions with bilaterally reduced standardized volumes in heroin-exposed offspring relative to controls include the ectorhinal and insular cortices. Regions with bilaterally increased standardized volumes in heroin-exposed offspring relative to controls include the periaqueductal gray, septal region, striatum, and hypothalamus. Leveraging microscopic resolution diffusion tensor imaging and precise regional parcellation, we generated whole-brain structural MRI diffusion connectomes. Using a dimension reduction approach with multivariate analysis of variance to assess group differences in the connectome, we found that in utero heroin exposure altered structure-based connectivity of the left septal region and the region that acts as a hub for limbic regulatory actions. Consistent with clinical evidence, our findings suggest that prenatal opioid exposure may have effects on brain morphology, connectivity, and, consequently, function that persist into adolescence. This work expands our understanding of the risks associated with opioid misuse during pregnancy and identifies biomarkers that may facilitate diagnosis and treatment.

Data-driven causal model discovery and personalized prediction in Alzheimer's disease.

With the explosive growth of biomarker data in Alzheimer's disease (AD) clinical trials, numerous mathematical models have been developed to characterize disease-relevant biomarker trajectories over time. While some of these models are purely empiric, others are causal, built upon various hypotheses of AD pathophysiology, a complex and incompletely understood area of research. One of the most challenging problems in computational causal modeling is using a purely data-driven approach to derive the model's parameters and the mathematical model itself, without any prior hypothesis bias. In this paper, we develop an innovative data-driven modeling approach to build and parameterize a causal model to characterize the trajectories of AD biomarkers. This approach integrates causal model learning, population parameterization, parameter sensitivity analysis, and personalized prediction. By applying this integrated approach to a large multicenter database of AD biomarkers, the Alzheimer's Disease Neuroimaging Initiative, several causal models for different AD stages are revealed. In addition, personalized models for each subject are calibrated and provide accurate predictions of future cognitive status.

Optimal anti-amyloid-beta therapy for Alzheimer's disease via a personalized mathematical model.

With the recent approval by the FDA of the first disease-modifying drug for Alzheimer's Disease (AD), personalized medicine will be increasingly important for appropriate management and counseling of patients with AD and those at risk. The growing availability of clinical biomarker data and data-driven computational modeling techniques provide an opportunity for new approaches to individualized AD therapeutic planning. In this paper, we develop a new mathematical model, based on AD cognitive, cerebrospinal fluid (CSF) and MRI biomarkers, to provide a personalized optimal treatment plan for individuals. This model is parameterized by biomarker data from the AD Neuroimaging Initiative (ADNI) cohort, a large multi-institutional database monitoring the natural history of subjects with AD and mild cognitive impairment (MCI). Optimal control theory is used to incorporate time-varying treatment controls and side-effects into the model, based on recent clinical trial data, to provide a personalized treatment regimen with anti-amyloid-beta therapy. In-silico treatment studies were conducted on the approved treatment, aducanumab, as well as on another promising anti-amyloid-beta therapy under evaluation, donanemab. Clinical trial simulations were conducted over both short-term (78 weeks) and long-term (10 years) periods with low-dose (6 mg/kg) and high-dose (10 mg/kg) regimens for aducanumab, and a single-dose regimen (1400 mg) for donanemab. Results confirm those of actual clinical trials showing a large and sustained effect of both aducanumab and donanemab on amyloid beta clearance. The effect on slowing cognitive decline was modest for both treatments, but greater for donanemab. This optimal treatment computational modeling framework can be applied to other single and combination treatments for both prediction and optimization, as well as incorporate new clinical trial data as it becomes available.

Validity of the Web-Based, Self-Directed, NeuroCognitive Performance Test in Mild Cognitive Impairment.

BACKGROUND: Digital cognitive tests offer several potential advantages over established paper-pencil tests but have not yet been fully evaluated for the clinical evaluation of mild cognitive impairment. OBJECTIVE: The NeuroCognitive Performance Test (NCPT) is a web-based, self-directed, modular battery intended for repeated assessments of multiple cognitive domains. Our objective was to examine its relationship with the Alzheimer's Disease Assessment Scale-Cognition Subscale (ADAS-Cog) and Mini-Mental State Examination (MMSE) as well as with established paper-pencil tests of cognition and daily functioning in mild cognitive impairment (MCI). METHODS: We used Spearman correlations, regressions and principal components analysis followed by a factor analysis (varimax rotated) to examine our objectives. RESULTS: In MCI subjects, the NCPT composite is significantly correlated with both a composite measure of established tests (r = 0.78, p < 0.0001) as well as with the ADAS-Cog (r = -0.55, p < 0.0001). Both NCPT and paper-pencil test batteries had a similar factor structure that included a large "g" component with a high eigenvalue. The correlation for the analogous tests (e.g., Trails A and B, learning memory tests) were significant (p < 0.0001). Further, both the NCPT and established tests significantly (p < 0.0001) predicted the University of California San Diego Performance-Based Skills Assessment and Functional Activities Questionnaire, measures of daily functioning. CONCLUSION: The NCPT, a web-based, self-directed, computerized test, shows high concurrent validity with established tests and hence offers promise for use as a research or clinical tool in MCI. Despite limitations such as a relatively small sample, absence of control group and cross-sectional nature, these findings are consistent with the growing literature on the promise of self-directed, web-based cognitive assessments for MCI.

Computerized Games versus Crosswords Training in Mild Cognitive Impairment.

BACKGROUND: Mild cognitive impairment (MCI) increases the risk of dementia. The efficacy of cognitive training in patients with MCI is unclear. METHODS: In a two-site, single-blinded, 78-week trial, participants with MCI - stratified by age, severity (early/late MCI), and site - were randomly assigned to 12 weeks of intensive, home-based, computerized training with Web-based cognitive games or Web-based crossword puzzles, followed by six booster sessions. In mixed-model analyses, the primary outcome was change from baseline in the 11-item Alzheimer's Disease Assessment Scale-Cognitive (ADAS-Cog) score, a 70 point scale in which higher scores indicate greater cognitive impairment at 78 weeks, adjusted for baseline. Secondary outcomes included change from baseline in neuropsychological composite score, University of California San Diego Performance-Based Skills Assessment (functional outcome) score, and Functional Activities Questionnaire (functional outcome) score at 78 weeks, adjusted for baseline. Changes in hippocampal volume and cortical thickness on magnetic resonance imaging were assessed. RESULTS: Among 107 participants (n=51 [games]; n=56 [crosswords]), ADAS-Cog score worsened slightly for games and improved for crosswords at week 78 (least squares [LS] means difference, -1.44; 95% confidence interval [CI], -2.83 to -0.06; P=0.04). From baseline to week 78, mean ADAS-Cog score worsened for games (9.53 to 9.93) and improved for crosswords (9.59 to 8.61). The late MCI subgroup showed similar results (LS means difference, -2.45; SE, 0.89; 95% CI, -4.21 to -0.70). Among secondary outcomes, the Functional Activities Questionnaire score worsened more with games than with crosswords at week 78 (LS means difference, -1.08; 95% CI, -1.97 to -0.18). Other secondary outcomes showed no differences. Decreases in hippocampal volume and cortical thickness were greater for games than for crosswords (LS means difference, 34.07; SE, 17.12; 95% CI, 0.51 to 67.63 [hippocampal volume]; LS means difference, 0.02; SE, 0.01; 95% CI, 0.00 to 0.04 [cortical thickness]). CONCLUSIONS: Home-based computerized training with crosswords demonstrated superior efficacy to games for the primary outcome of baseline-adjusted change in ADAS-Cog score over 78 weeks. (Supported by the National Institutes of Health, National Institute on Aging; ClinicalTrials.gov number, NCT03205709.).

Diffusion Tensor MRI Structural Connectivity and PET Amyloid Burden in Preclinical Autosomal Dominant Alzheimer Disease: The DIAN Cohort.

Background Pathologic evidence of Alzheimer disease (AD) is detectable years before onset of clinical symptoms. Imaging-based identification of structural changes of the brain in people at genetic risk for early-onset AD may provide insights into how genes influence the pathologic cascade that leads to dementia. Purpose To assess structural connectivity differences in cortical networks between cognitively normal autosomal dominant Alzheimer disease (ADAD) mutation carriers versus noncarriers and to determine the cross-sectional relationship of structural connectivity and cortical amyloid burden with estimated years to symptom onset (EYO) of dementia in carriers. Materials and Methods In this exploratory analysis of a prospective trial, all participants enrolled in the Dominantly Inherited Alzheimer Network between January 2009 and July 2014 who had normal cognition at baseline, T1-weighted MRI scans, and diffusion tensor imaging (DTI) were analyzed. Amyloid PET imaging using Pittsburgh compound B was also analyzed for mutation carriers. Areas of the cerebral cortex were parcellated into three cortical networks: the default mode network, frontoparietal control network, and ventral attention network. The structural connectivity of the three networks was calculated from DTI. General linear models were used to examine differences in structural connectivity between mutation carriers and noncarriers and the relationship between structural connectivity, amyloid burden, and EYO in mutation carriers. Correlation network analysis was performed to identify clusters of related clinical and imaging markers. Results There were 30 mutation carriers (mean age ± standard deviation, 34 years ± 10; 17 women) and 38 noncarriers (mean age, 37 years ± 10; 20 women). There was lower structural connectivity in the frontoparietal control network in mutation carriers compared with noncarriers (estimated effect of mutation-positive status, -0.0266; P = .04). Among mutation carriers, there was a correlation between EYO and white matter structural connectivity in the frontoparietal control network (estimated effect of EYO, -0.0015, P = .01). There was no significant relationship between cortical global amyloid burden and EYO among mutation carriers (P > .05). Conclusion White matter structural connectivity was lower in autosomal dominant Alzheimer disease mutation carriers compared with noncarriers and correlated with estimated years to symptom onset. Clinical trial registration no. NCT00869817 © RSNA, 2021 Online supplemental material is available for this article. See also the editorial by McEvoy in this issue.

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.

Magnetic resonance imaging volumetric analysis in patients with Alternating hemiplegia of childhood: A pilot study.

Quantitative MRI is increasingly being used as a biomarker in neurological disorders. Cerebellar atrophy occurs in some Alternating Hemiplegia of Childhood (AHC) patients. However, it is not known if cerebellar atrophy can be a potential biomarker in AHC or if quantitative MRI is a reliable method to address this question. Here we determine the reproducibility of an MRI-volumetrics method to investigate brain volumes in AHC and apply it to a population of 14 consecutive AHC patients (ages 4-11 years). We studied method reproducibility in the first 11 patients and then performed correlation of cerebellar volumes, relative to published normal population means, with age in all 14. We used FreeSurfer 6.0.0 to automatically segment MRI images, then performed manual resegmentation correction by two different observers. No significant differences were observed in any of ten brain regions between the two reviewers: p > .591 and interclass Correlation Coefficient (ICC) ≥0.975 in all comparisons. Additionally, there were no significant differences between the means of the two reviewers and the automatic segmentation values: p ≥ .106 and ICC ≥0.994 in all comparisons. We found a negative correlation between cerebellar volume and age (R = -0.631, p = .037), even though only one patient showed any cerebellar atrophy upon formal readings of the MRIs by neuroradiology. Sample size did not allow us to rule out potential confounding variables. Thus, findings from this cross-sectional study should be considered as exploratory. Our study supports the prospective investigation of quantitative MRI-volumetrics of the cerebellum as a potential biomarker in AHC.

Correlation of OCTA and Volumetric MRI in Mild Cognitive Impairment and Alzheimer's Disease.

BACKGROUD AND OBJECTIVE: To evaluate the relationship between retinal microvascular parameters on optical coherence tomography angiography (OCTA) and neurodegenerative changes assessed by measurement of brain volume on volumetric magnetic resonance imaging (MRI) in Alzheimer's disease (AD) and mild cognitive impairment (MCI). PATIENTS AND METHODS: Sixteen subjects with AD and MCI underwent OCTA imaging (3 mm × 3 mm and 6 mm × 6 mm scans) and volumetric brain MRI imaging with automated volumetric segmentation and quantification. Spearman's correlation (ρ) was performed between forebrain parenchyma, cortical gray matter, inferolateral ventricle (ILV), lateral ventricle (LV), and hippocampus (HP) MRI volumes and vessel density (VD), along with perfusion density (PD) for the 6-mm circle, 6-mm ring, 3-mm circle, and 3-mm ring Early Treatment Diabetic Retinopathy Study regions of the superficial capillary plexus. RESULTS: Thirty eyes of 16 patients (seven MCI and nine AD) with good-quality OCTA images were analyzed. ILV volume inversely correlated with the VD in the 6-mm circle (ρ = -0 .565, P = .028) and 3-mm ring (ρ = -0.569, P = .027) and PD in the 3-mm ring (ρ = -0.605, P = .0169). Forebrain, cortical gray matter, LV, and HP volumes did not significantly correlate with either VD or PD (P > .05). CONCLUSIONS: In this pilot investigation, the authors found a significant correlation between reduction in the superficial capillary plexus VD and PD on OCTA and expansion of the ILV in MCI and AD. This relationship between the retinal microvasculature and cerebral volumetric changes deserves further investigation. [Ophthalmic Surg Lasers Imaging Retina. 2019;50:709-718.].

Computational Causal Modeling of the Dynamic Biomarker Cascade in Alzheimer's Disease.

BACKGROUND: Alzheimer's disease (AD) is a major public health concern, and there is an urgent need to better understand its complex biology and develop effective therapies. AD progression can be tracked in patients through validated imaging and spinal fluid biomarkers of pathology and neuronal loss. We still, however, lack a coherent quantitative model that explains how these biomarkers interact and evolve over time. Such a model could potentially help identify the major drivers of disease in individual patients and simulate response to therapy prior to entry in clinical trials. A current theory of AD biomarker progression, known as the dynamic biomarker cascade model, hypothesizes AD biomarkers evolve in a sequential but temporally overlapping manner. A computational model incorporating assumptions about the underlying biology of this theory and its variations would be useful to test and refine its accuracy with longitudinal biomarker data from clinical trials. METHODS: We implemented a causal model to simulate time-dependent biomarker data under the descriptive assumptions of the dynamic biomarker cascade theory. We modeled pathologic biomarkers (beta-amyloid and tau), neuronal loss biomarkers, and cognitive impairment as nonlinear first-order ordinary differential equations (ODEs) to include amyloid-dependent and nondependent neurodegenerative cascades. We tested the feasibility of the model by adjusting its parameters to simulate three specific natural history scenarios in early-onset autosomal dominant AD and late-onset AD and determine whether computed biomarker trajectories agreed with current assumptions of AD biomarker progression. We also simulated the effects of antiamyloid therapy in late-onset AD. RESULTS: The computational model of early-onset AD demonstrated the initial appearance of amyloid, followed by biomarkers of tau and neurodegeneration and the onset of cognitive decline based on cognitive reserve, as predicted by the prior literature. Similarly, the late-onset AD computational models demonstrated the first appearance of amyloid or nonamyloid-related tauopathy, depending on the magnitude of comorbid pathology, and also closely matched the biomarker cascades predicted by the prior literature. Forward simulation of antiamyloid therapy in symptomatic late-onset AD failed to demonstrate any slowing in progression of cognitive decline, consistent with prior failed clinical trials in symptomatic patients. CONCLUSIONS: We have developed and computationally implemented a mathematical causal model of the dynamic biomarker cascade theory in AD. We demonstrate the feasibility of this model by simulating biomarker evolution and cognitive decline in early- and late-onset natural history scenarios, as well as in a treatment scenario targeted at core AD pathology. Models resulting from this causal approach can be further developed and refined using patient data from longitudinal biomarker studies and may in the future play a key role in personalizing approaches to treatment.

Microstructural alterations of cortical and deep gray matter over a season of high school football revealed by diffusion kurtosis imaging.

OBJECTIVES: To probe microstructural changes that are associated with subconcussive head impact exposure in deep and cortical gray matter of high school football players over a single season. METHODS: Players underwent diffusion kurtosis imaging (DKI) and quantitative susceptibility mapping (QSM) scans. Head impact data was recorded. Association between parametric changes and frequency of frontal head impact was assessed. RESULTS: In deep gray matter, significant decreases in mean kurtosis (MK) and increases in mean diffusivity (MD) over the season were observed in the thalamus and putamen. Correlations between changes in DKI metrics and frequency of frontal impacts were observed in the putamen and caudate. In cortical gray matter, decreases in MK were observed in regions including the pars triangularis and inferior parietal. In addition, increases in MD were observed in the rostral middle frontal cortices. Negative correlations between MK and frequency of frontal impacts were observed in the posterior part of the brain including the pericalcarine, lingual and middle temporal cortices. Magnetic susceptibility values exhibited no significant difference or correlation, suggesting these diffusion changes common within the group may not be associated with iron-related mechanisms. CONCLUSION: Microstructural alterations over the season and correlations with head impacts were captured by DKI metrics, which suggested that DKI imaging of gray matter may yield valuable biomarkers for evaluating brain injuries associated with subconcussive head impact. Findings of associations between frontal impacts and changes in posterior cortical gray matter also indicated that contrecoup injury rather than coup injury might be the dominant mechanism underlying the observed microstructural alterations. ADVANCES IN KNOWLEDGE: Significant microstructural changes, as reflected by DKI metrics, in cortical gray matter such as the rostral middle frontal cortices, and in deep gray matter such as the thalamus were observed in high school football players over the course of a single season without clinically diagnosed concussion. QSM showed no evidence of iron-related changes in the observed subconcussive brain injuries. The detected microstructural changes in cortical and deep gray matter correlated with frequency of subconcussive head impacts. IMPLICATIONS FOR PATIENT CARE: DKI may yield valuable biomarkers for evaluating the severity of brain injuries associated with subconcussive head impacts in contact sport athletes.

Donepezil Treatment in Patients With Depression and Cognitive Impairment on Stable Antidepressant Treatment: A Randomized Controlled Trial.

OBJECTIVE: Depression and cognitive impairment are often comorbid in older adults, but optimal treatment strategies remain unclear. In a two-site study, the efficacy and safety of add-on donepezil versus placebo were compared in depressed patients with cognitive impairment receiving stable antidepressant treatment. METHODS: A randomized, double-blind, placebo-controlled trial was conducted in older adults with depression and cognitive impairment (https://clinicaltrials.gov/ct2/show/NCT01658228; NCT01658228). Patients received open-label antidepressant treatment for 16 weeks, initially with citalopram and then with venlafaxine, if needed, followed by random assignment to add-on donepezil 5-10 mg daily or placebo for another 62 weeks. Outcome measures were neuropsychological test performance (Alzheimer's Disease Assessment Scale-Cognitive subscale [ADAS-Cog] and Selective Reminding Test [SRT] total immediate recall) and instrumental activities of daily living (Functional Activities Questionnaire). RESULTS: Of 81 patients who signed informed consent, 79 patients completed the baseline evaluation. Open antidepressant treatment was associated with improvement in depression in 63.93% responders by week 16. In the randomized trial, there were no treatment group differences between donepezil and placebo on dementia conversion rates, ADAS-Cog, SRT total immediate recall, or FAQ. Neither baseline cognitive impairment severity nor apolipoprotein E e4 genotype influenced donepezil efficacy. Donepezil was associated with more adverse effects than placebo. CONCLUSION: The results do not support adjunctive off-label cholinesterase inhibitor treatment in patients with depression and cognitive impairment. The findings highlight the need to prioritize discovery of novel treatments for this highly prevalent population with comorbid illnesses.

Clinical and radiological characteristics of early versus late mild cognitive impairment in patients with comorbid depressive disorder.

OBJECTIVE: The classification of mild cognitive impairment (MCI) continues to be debated though it has recently been subtyped into late (LMCI) versus early (EMCI) stages. Older adults presenting with both a depressive disorder (DEP) and cognitive impairment (CI) represent a unique, understudied population. Our aim was to examine baseline characteristics of DEP-CI patients in the DOTCODE trial, a randomized controlled trial of open antidepressant treatment for 16 weeks followed by add-on donepezil or placebo for 62 weeks. METHODS/DESIGN: Key inclusion criteria were diagnosis of major depression or dysthymic disorder with Hamilton Depression Rating Scale (HAM-D) score >14, and cognitive impairment defined by MMSE score ≥21 and impaired performance on the WMS-R Logical Memory II test. Patients were classified as EMCI or LMCI based on the 1.5 SD cutoff on tests of verbal memory, and compared on baseline clinical, neuropsychological, and anatomical characteristics. RESULTS: Seventy-nine DEP-CI patients were recruited of whom 39 met criteria for EMCI and 40 for LMCI. The mean age was 68.9, and mean HAM-D was 23.0. Late mild cognitive impairment patients had significantly worse ADAS-Cog (P < .001), MMSE (P = .004), Block Design (P = .024), Visual Rep II (P = .006), CFL Animal (P = .006), UPSIT (P = .051), as well as smaller right hippocampal volume (P = .037) compared to EMCI patients. MRI indices of cerebrovascular disease did not differ between EMCI and LMCI patients. CONCLUSIONS: Cognitive and neuronal loss markers differed between EMCI and LMCI among patients with DEP-CI, with LMCI being more likely to have the clinical and neuronal loss markers known to be associated with Alzheimer's disease.

Sex Differences in Cognitive Decline in Subjects with High Likelihood of Mild Cognitive Impairment due to Alzheimer's disease.

Sex differences in Alzheimer's disease (AD) biology and progression are not yet fully characterized. The goal of this study is to examine the effect of sex on cognitive progression in subjects with high likelihood of mild cognitive impairment (MCI) due to Alzheimer's and followed up to 10 years in the Alzheimer's Disease Neuroimaging Initiative (ADNI). Cerebrospinal fluid total-tau and amyloid-beta (Aß42) ratio values were used to sub-classify 559 MCI subjects (216 females, 343 males) as having "high" or "low" likelihood for MCI due to Alzheimer's. Data were analyzed using mixed-effects models incorporating all follow-ups. The worsening from baseline in Alzheimer's Disease Assessment Scale-Cognitive score (mean, SD) (9 ± 12) in subjects with high likelihood of MCI due to Alzheimer's was markedly greater than that in subjects with low likelihood (1 ± 6, p < 0.0001). Among MCI due to AD subjects, the mean worsening in cognitive score was significantly greater in females (11.58 ± 14) than in males (6.87 ± 11, p = 0.006). Our findings highlight the need to further investigate these findings in other populations and develop sex specific timelines for Alzheimer's disease progression.

Increasing Rates of Imaging in Failed Back Surgery Syndrome Patients: Implications for Spinal Cord Stimulation.

BACKGROUND: Failed back surgery syndrome (FBSS) has a high incidence following spinal surgery, is notoriously refractory to treatment, and results in high health care utilization. Spinal cord stimulation (SCS) is a well-accepted modality for pain relief in this population; however, until recently magnetic resonance imaging (MRI) was prohibited due to risk of heat conduction through the device. OBJECTIVES: We examined trends in imaging use over the past decade in patients with FBSS to determine its impact on health care utilization and implications for patients receiving SCS. STUDY DESIGN: Retrospective. SETTING: Inpatient and outpatient sample. METHODS: We identified patients from 2000 to 2012 using the Truven MarketScan database. Annual imaging rates (episodes per 1000 patient months) were determined for MRI, computed tomography (CT) scan, x-ray, and ultrasound. A multivariate Poisson regression model was used to determine imaging trends over time, and to compare imaging in SCS and non-SCS populations. RESULTS: A total of 311,730 patients with FBSS were identified, of which 5.17% underwent SCS implantation (n = 16,118). The median (IQR) age was 58.0 (49.0 - 67.0) years. Significant increases in imaging rate ratios were found in all years for each of the modalities. Increases were seen in the use of CT scans (rate ratio [RR] = 3.03; 95% confidence interval [CI]: 2.79 - 3.29; P < 0.0001), MRI (RR = 1.73; 95% CI: 1.61 - 1.85; P < 0.0001), ultrasound (RR = 2.00; 95% CI: 1.84 - 2.18; P < 0.0001), and x-ray (RR = 1.10; 95% CI: 1.05 - 1.15; P < 0.0001). Despite rates of MRI in SCS patients being half that in the non-SCS group, these patients underwent 19% more imaging procedures overall (P < 0.0001). SCS patients had increased rates of x-ray (RR = 1.27; 95% CI: 1.25 - 1.29), CT scans (RR = 1.32; 95% CI: 1.30 - 1.35), and ultrasound (RR = 1.10; 95% CI: 1.07 - 1.13) (all P < 0.0001). LIMITATIONS: This study is limited by a lack of clinical and historical variables including the complexity of prior surgeries and pain symptomatology. Miscoding cannot be precluded, as this sample is taken from a large nationwide database. CONCLUSIONS: We found a significant trend for increased use of advanced imaging modalities between the years 2000 and 2012 in FBSS patients. Those patients treated with SCS were 50% less likely to receive an MRI (as expected, given prior incompatibility of neuromodulation devices), yet 32% and 27% more likely to receive CT and x-ray, respectively. Despite the decrease in the use of MRI in those patients treated with SCS, their overall imaging rate increased by 19% compared to patients without SCS. This underscores the utility of MR-conditional SCS systems. These findings demonstrate that imaging plays a significant role in driving health care expenditures. This is the largest analysis examining the role of imaging in the FBSS population and the impact of SCS procedures. Further studies are needed to assess the impact of MRI-conditional SCS systems on future trends in imaging in FBSS patients receiving neuromodulation therapies. Key words: Failed back surgery syndrome, spinal cord stimulation, imaging, health care utilization, MRI, chronic pain, back pain, neuromodulation.