Predicting Fluid Intelligence of Children using T1-weighted MR Images and a StackNet

In this work, we utilize T1-weighted MR images and StackNet to predict fluid intelligence in adolescents. Our framework includes feature extraction, feature normalization, feature denoising, feature selection, training a StackNet, and predicting fluid intelligence. The extracted feature is the distribution of different brain tissues in different brain parcellation regions. The proposed StackNet consists of three layers and 11 models. Each layer uses the predictions from all previous layers including the input layer. The proposed StackNet is tested on a public benchmark Adolescent Brain Cognitive Development Neurocognitive Prediction Challenge 2019 and achieves a mean squared error of 82.42 on the combined training and validation set with 10-fold cross-validation. In addition, the proposed StackNet also achieves a mean squared error of 94.25 on the testing data. The source code is available on GitHub.Comment: 8 pages, 2 figures, 3 tables, Accepted by MICCAI ABCD-NP Challenge 2019; Added ND

Television Watching and Mental Health in the General Population of New York City After September 11

The September 11, 2001 terrorist attacks were watched on television by millions. Using data from a telephone survey of New York City residents in January 2002 (N = 2001), we examined the relations between television watching and probable posttraumatic stress disorder (PTSD) after the attacks. Among those who were directly affected by the attacks or had prior traumatic experiences, watching television was associated with probable PTSD. Experiencing a peri-event panic reaction accounted for some of the association between television watching and probable PTSD. Future research directions are suggested for better understanding the mechanisms behind observed associations between television watching and PTSD.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/40284/2/Ahern_Television Watching and Mental Health in_2005.pd

Medicine in the Popular Press: The Influence of the Media on Perceptions of Disease

In an age of increasing globalization and discussion of the possibility of global pandemics, increasing rates of reporting of these events may influence public perception of risk. The present studies investigate the impact of high levels of media reporting on the perceptions of disease. Undergraduate psychology and medical students were asked to rate the severity, future prevalence and disease status of both frequently reported diseases (e.g. avian flu) and infrequently reported diseases (e.g. yellow fever). Participants considered diseases that occur frequently in the media to be more serious, and have higher disease status than those that infrequently occur in the media, even when the low media frequency conditions were considered objectively ‘worse’ by a separate group of participants. Estimates of severity also positively correlated with popular print media frequency in both student populations. However, we also see that the concurrent presentation of objective information about the diseases can mitigate this effect. It is clear from these data that the media can bias our perceptions of disease

ABCD Neurocognitive Prediction Challenge 2019: Predicting individual fluid intelligence scores from structural MRI using probabilistic segmentation and kernel ridge regression

We applied several regression and deep learning methods to predict fluid intelligence scores from T1-weighted MRI scans as part of the ABCD Neurocognitive Prediction Challenge (ABCD-NP-Challenge) 2019. We used voxel intensities and probabilistic tissue-type labels derived from these as features to train the models. The best predictive performance (lowest mean-squared error) came from Kernel Ridge Regression (KRR; $\lambda=10$), which produced a mean-squared error of 69.7204 on the validation set and 92.1298 on the test set. This placed our group in the fifth position on the validation leader board and first place on the final (test) leader board.Comment: Winning entry in the ABCD Neurocognitive Prediction Challenge at MICCAI 2019. 7 pages plus references, 3 figures, 1 tabl

ABCD Neurocognitive Prediction Challenge 2019: Predicting individual residual fluid intelligence scores from cortical grey matter morphology

We predicted residual fluid intelligence scores from T1-weighted MRI data available as part of the ABCD NP Challenge 2019, using morphological similarity of grey-matter regions across the cortex. Individual structural covariance networks (SCN) were abstracted into graph-theory metrics averaged over nodes across the brain and in data-driven communities/modules. Metrics included degree, path length, clustering coefficient, centrality, rich club coefficient, and small-worldness. These features derived from the training set were used to build various regression models for predicting residual fluid intelligence scores, with performance evaluated both using cross-validation within the training set and using the held-out validation set. Our predictions on the test set were generated with a support vector regression model trained on the training set. We found minimal improvement over predicting a zero residual fluid intelligence score across the sample population, implying that structural covariance networks calculated from T1-weighted MR imaging data provide little information about residual fluid intelligence.Comment: 8 pages plus references, 3 figures, 2 tables. Submission to the ABCD Neurocognitive Prediction Challenge at MICCAI 201

Effects of abstinence on brain morphology in alcoholism: A MRI study

Chronic alcohol abuse leads to morphological changes of the brain. We investigated if these volumetric changes are reversible after a period of abstinence. For this reason 41 male and 15 female alcohol patients underwent MRI-scanning after in-patient detoxification (baseline) entering alcoholism treatment programs, and between 6 and 9 months later (follow-up), in a phase of convalescence. Additionally, 29 male and 16 female control subjects were examined. The MRI-scans were delineated and the resulting regions of interest, volumes of lateral ventricles and prefrontal lobes were expressed relatively to total brain volume. Compared to control subjects alcohol patients showed bilaterally decreased prefrontal lobes (11% reduction) and increased lateral ventricles (up to 42% enlargement). The extent of the ventricular increase was depending on patient’s additional psychiatric diagnosis, showing smaller lateral ventricles in patients with additional personality disorder. While at follow-up the size of prefrontal lobes remained unchanged, volumes of the lateral ventricles decreased (5–6% reduction) in alcohol patients with abstinence and improved drinking behavior, especially in patients that underwent only one detoxification. The extent of the ventricular enlargement correlated with the elevation of alcohol related laboratory measures (mean corpuscular volume, gamma-glutamyl transpeptidase). In conclusion this study confirms the hypothesis that alcoholism causes brain damages that are partially reversible. It should be analyzed in further studies with larger sample sizes, if complete brain regeneration is possible maintaining abstinence over a longer period

Volumetric Magnetic Resonance Imaging Quantification of Longitudinal Brain Changes in Abstinent Alcoholics

Magnetic resonance imaging (MRI) of the brain was performed on a group of 24 recently detoxified, male alcoholics approximately 1 month after their date of last drink. The imaging was repeated 3 months later, at which point 9 subjects had resumed drinking and 15 had maintained abstinence. Contrasts between these two drinking groups revealed that, despite comparable baseline values, the Abstainers exhibited volumetric white matter increases and cerebrospinal fluid reductions over the follow-up interval, whereas the Drinkers did not show significant change on either of these MRI indices. These results provide the first evidence suggestive of significant volumetric white matter increase with abstinence

Frontally mediated inhibitory processing and white matter microstructure: age and alcoholism effects

RationaleThe NOGO P3 event-related potential is a sensitive marker of alcoholism, relates to EEG oscillation in the δ and θ frequency ranges, and reflects activation of an inhibitory processing network. Degradation of white matter tracts related to age or alcoholism should negatively affect the oscillatory activity within the network.ObjectiveThis study aims to evaluate the effect of alcoholism and age on δ and θ oscillations and the relationship between these oscillations and measures of white matter microstructural integrity.MethodsData from ten long-term alcoholics to 25 nonalcoholic controls were used to derive P3 from Fz, Cz, and Pz using a visual GO/NOGO protocol. Total power and across trial phase synchrony measures were calculated for δ and θ frequencies. DTI, 1.5 T, data formed the basis of quantitative fiber tracking in the left and right cingulate bundles and the genu and splenium of the corpus callosum. Fractional anisotropy and diffusivity (λL and λT) measures were calculated from each tract.ResultsNOGO P3 amplitude and δ power at Cz were smaller in alcoholics than controls. Lower δ total power was related to higher λT in the left and right cingulate bundles. GO P3 amplitude was lower and GO P3 latency was longer with advancing age, but none of the time-frequency analysis measures displayed significant age or diagnosis effects.ConclusionsThe relation of δ total power at CZ with λT in the cingulate bundles provides correlational evidence for a functional role of fronto-parietal white matter tracts in inhibitory processing

Fuzzy Fibers: Uncertainty in dMRI Tractography

Fiber tracking based on diffusion weighted Magnetic Resonance Imaging (dMRI) allows for noninvasive reconstruction of fiber bundles in the human brain. In this chapter, we discuss sources of error and uncertainty in this technique, and review strategies that afford a more reliable interpretation of the results. This includes methods for computing and rendering probabilistic tractograms, which estimate precision in the face of measurement noise and artifacts. However, we also address aspects that have received less attention so far, such as model selection, partial voluming, and the impact of parameters, both in preprocessing and in fiber tracking itself. We conclude by giving impulses for future research