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

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

Cognitive control in belief-laden reasoning during conclusion processing: An ERP study

Belief bias is the tendency to accept conclusions that are compatible with existing beliefs more frequently than those that contradict beliefs. It is one of the most replicated behavioral findings in the reasoning literature. Recently, neuroimaging studies using functional magnetic resonance imaging (fMRI) and event-related potentials (ERPs) have provided a new perspective and have demonstrated neural correlates of belief bias that have been viewed as supportive of dual-process theories of belief bias. However, fMRI studies have tended to focus on conclusion processing, while ERPs studies have been concerned with the processing of premises. In the present research, the electrophysiological correlates of cognitive control were studied among 12 subjects using high-density ERPs. The analysis was focused on the conclusion presentation phase and was limited to normatively sanctioned responses to valid–believable and valid–unbelievable problems. Results showed that when participants gave normatively sanctioned responses to problems where belief and logic conflicted, a more positive ERP deflection was elicited than for normatively sanctioned responses to nonconflict problems. This was observed from −400 to −200 ms prior to the correct response being given. The positive component is argued to be analogous to the late positive component (LPC) involved in cognitive control processes. This is consistent with the inhibition of empirically anomalous information when conclusions are unbelievable. These data are important in elucidating the neural correlates of belief bias by providing evidence for electrophysiological correlates of conflict resolution during conclusion processing. Moreover, they are supportive of dual-process theories of belief bias that propose conflict detection and resolution processes as central to the explanation of belief bias

Association between a longer duration of illness, age and lower frontal lobe grey matter volume in schizophrenia

The frontal lobe has an extended maturation period and may be vulnerable to the long-term effects of schizophrenia. We tested this hypothesis by studying the relationship between duration of illness (DoI), grey matter (GM) and cerebro-spinal fluid (CSF) volume across the whole brain. Sixty-four patients with schizophrenia and 25 healthy controls underwent structural MRI scanning and neuropsychological assessment. We performed regression analyses in patients to examine the relationship between DoI and GM and CSF volumes across the whole brain, and correlations in controls between age and GM or CSF volume of the regions where GM or CSF volumes were associated with DoI in patients. Correlations were also performed between GM volume in the regions associated with DoI and neuropsychological performance. A longer DoI was associated with lower GM volume in the left dorsomedial prefrontal cortex (PFC), right middle frontal cortex, left fusiform gyrus (FG) and left cerebellum (lobule III). Additionally, age was inversely associated with GM volume in the left dorsomedial PFC in patients, and in the left FG and CSF excess near the left cerebellum in healthy controls. Greater GM volume in the left dorsomedial PFC was associated with better working memory, attention and psychomotor speed in patients. Our findings suggest that the right middle frontal cortex is particularly vulnerable to the long-term effect of schizophrenia illness whereas the dorsomedial PFC, FG and cerebellum are affected by both a long DoI and aging. The effect of illness chronicity on GM volume in the left dorsomedial PFC may be extended to brain structure–neuropsychological function relationships

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

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

Altered Neurocircuitry in the Dopamine Transporter Knockout Mouse Brain

The plasma membrane transporters for the monoamine neurotransmitters dopamine, serotonin, and norepinephrine modulate the dynamics of these monoamine neurotransmitters. Thus, activity of these transporters has significant consequences for monoamine activity throughout the brain and for a number of neurological and psychiatric disorders. Gene knockout (KO) mice that reduce or eliminate expression of each of these monoamine transporters have provided a wealth of new information about the function of these proteins at molecular, physiological and behavioral levels. In the present work we use the unique properties of magnetic resonance imaging (MRI) to probe the effects of altered dopaminergic dynamics on meso-scale neuronal circuitry and overall brain morphology, since changes at these levels of organization might help to account for some of the extensive pharmacological and behavioral differences observed in dopamine transporter (DAT) KO mice. Despite the smaller size of these animals, voxel-wise statistical comparison of high resolution structural MR images indicated little morphological change as a consequence of DAT KO. Likewise, proton magnetic resonance spectra recorded in the striatum indicated no significant changes in detectable metabolite concentrations between DAT KO and wild-type (WT) mice. In contrast, alterations in the circuitry from the prefrontal cortex to the mesocortical limbic system, an important brain component intimately tied to function of mesolimbic/mesocortical dopamine reward pathways, were revealed by manganese-enhanced MRI (MEMRI). Analysis of co-registered MEMRI images taken over the 26 hours after introduction of Mn^(2+) into the prefrontal cortex indicated that DAT KO mice have a truncated Mn^(2+) distribution within this circuitry with little accumulation beyond the thalamus or contralateral to the injection site. By contrast, WT littermates exhibit Mn^(2+) transport into more posterior midbrain nuclei and contralateral mesolimbic structures at 26 hr post-injection. Thus, DAT KO mice appear, at this level of anatomic resolution, to have preserved cortico-striatal-thalamic connectivity but diminished robustness of reward-modulating circuitry distal to the thalamus. This is in contradistinction to the state of this circuitry in serotonin transporter KO mice where we observed more robust connectivity in more posterior brain regions using methods identical to those employed here

Addressing the needs of children with disabilities experiencing disaster or terrorism

Purpose of review: This paper reviews the empirical literature on psychosocial factors relating to children with disabilities in the context of disaster or terrorism. Recent findings: Research indicates individuals with disabilities experience increased exposure to hazards due to existing social disparities and barriers associated with disability status. However, studies on the psychological effects of disaster/terrorism on children with preexisting disabilities are exceedingly few and empirical evidence of the effectiveness of trauma-focused therapies for this population is limited. Secondary adversities, including social stigma and health concerns, also compromise the recovery of these children post-disaster/terrorism. Schools and teachers appear to be particularly important in the recovery of children with disabilities to disaster. Disasters, terrorism, and war all contribute to the incidence of disability, as well as disproportionately affect children with preexisting disabilities. Summary: Disaster preparedness interventions and societal changes are needed to decrease the disproportionate environmental and social vulnerability of children with disabilities to disaster and terrorism

Original research: Trauma exposure and posttraumatic stress disorder among employees of New York City companies affected by the september 11, 2001 attacks on the World Trade Center

OBJECTIVE: Several studies have provided prevalence estimates of posttraumatic stress disorder (PTSD) related to the September 11, 2001 (9/11) attacks in broadly affected populations, although without sufficiently addressing qualifying exposures required for assessing PTSD and estimating its prevalence. A premise that people throughout the New York City area were exposed to the attacks on the World Trade Center (WTC) towers and are thus at risk for developing PTSD has important implications for both prevalence estimates and service provision. This premise has not, however, been tested with respect to DSM-IV-TR criteria for PTSD. This study examined associations between geographic distance from the 9/11 attacks on the WTC and reported 9/11 trauma exposures, and the role of specific trauma exposures in the development of PTSD. METHODS: Approximately 3 years after the attacks, 379 surviving employees (102 with direct exposures, including 65 in the towers, and 277 with varied exposures) recruited from 8 affected organizations were interviewed using the Diagnostic Interview Schedule/Disaster Supplement and reassessed at 6 years. The estimated closest geographic distance from the WTC towers during the attacks and specific disaster exposures were compared with the development of 9/11–related PTSD as defined by the Diagnostic and Statistical Manual, Fourth Edition, Text Revision. RESULTS: The direct exposure zone was largely concentrated within a radius of 0.1 mi and completely contained within 0.75 mi of the towers. PTSD symptom criteria at any time after the disaster were met by 35% of people directly exposed to danger, 20% of those exposed only through witnessed experiences, and 35% of those exposed only through a close associate’s direct exposure. Outside these exposure groups, few possible sources of exposure were evident among the few who were symptomatic, most of whom had preexisting psychiatric illness. CONCLUSIONS: Exposures deserve careful consideration among widely affected populations after large terrorist attacks when conducting clinical assessments, estimating the magnitude of population PTSD burdens, and projecting needs for specific mental health interventions

The impact of substance use on brain structure in people at high risk of developing schizophrenia

Ventricular enlargement and reduced prefrontal volume are consistent findings in schizophrenia. Both are present in first episode subjects and may be detectable before the onset of clinical disorder. Substance misuse is more common in people with schizophrenia and is associated with similar brain abnormalities. We employ a prospective cohort study with nested case control comparison design to investigate the association between substance misuse, brain abnormality, and subsequent schizophrenia. Substance misuse history, imaging data, and clinical information were collected on 147 subjects at high risk of schizophrenia and 36 controls. Regions exhibiting a significant relationship between level of use of alcohol, cannabis or tobacco, and structure volume were identified. Multivariate regression then elucidated the relationship between level of substance use and structure volumes while accounting for correlations between these variables and correcting for potential confounders. Finally, we established whether substance misuse was associated with later risk of schizophrenia. Increased ventricular volume was associated with alcohol and cannabis use in a dose-dependent manner. Alcohol consumption was associated with reduced frontal lobe volume. Multiple regression analyses found both alcohol and cannabis were significant predictors of these abnormalities when simultaneously entered into the statistical model. Alcohol and cannabis misuse were associated with an increased subsequent risk of schizophrenia. We provide prospective evidence that use of cannabis or alcohol by people at high genetic risk of schizophrenia is associated with brain abnormalities and later risk of psychosis. A family history of schizophrenia may render the brain particularly sensitive to the risk-modifying effects of these substances