A Rapid Segmentation-Insensitive "Digital Biopsy" Method for Radiomic Feature Extraction: Method and Pilot Study Using CT Images of Non-Small Cell Lung Cancer.

Quantitative imaging approaches compute features within images' regions of interest. Segmentation is rarely completely automatic, requiring time-consuming editing by experts. We propose a new paradigm, called "digital biopsy," that allows for the collection of intensity- and texture-based features from these regions at least 1 order of magnitude faster than the current manual or semiautomated methods. A radiologist reviewed automated segmentations of lung nodules from 100 preoperative volume computed tomography scans of patients with non-small cell lung cancer, and manually adjusted the nodule boundaries in each section, to be used as a reference standard, requiring up to 45 minutes per nodule. We also asked a different expert to generate a digital biopsy for each patient using a paintbrush tool to paint a contiguous region of each tumor over multiple cross-sections, a procedure that required an average of <3 minutes per nodule. We simulated additional digital biopsies using morphological procedures. Finally, we compared the features extracted from these digital biopsies with our reference standard using intraclass correlation coefficient (ICC) to characterize robustness. Comparing the reference standard segmentations to our digital biopsies, we found that 84/94 features had an ICC >0.7; comparing erosions and dilations, using a sphere of 1.5-mm radius, of our digital biopsies to the reference standard segmentations resulted in 41/94 and 53/94 features, respectively, with ICCs >0.7. We conclude that many intensity- and texture-based features remain consistent between the reference standard and our method while substantially reducing the amount of operator time required

Reliability of single-subject neural activation patterns in speech production tasks

Traditional group fMRI (functional magnetic resonance imaging) analyses are not designed to detect individual differences that may be crucial to better understanding speech disorders. Single-subject research could therefore provide a richer characterization of the neural substrates of speech production in development and disease. Before this line of research can be tackled, however, it is necessary to evaluate whether healthy individuals exhibit reproducible brain activation across multiple sessions during speech production tasks. In the present study, we evaluated the reliability and discriminability of cortical functional magnetic resonance imaging data from twenty neurotypical subjects who participated in two experiments involving reading aloud mono- or bisyllabic speech stimuli. Using traditional methods like the Dice and intraclass correlation coefficients, we found that most individuals displayed moderate to high reliability, with exceptions likely due to increased head motion in the scanner. Further, this level of reliability for speech production was not directly correlated with reliable patterns in the underlying average blood oxygenation level dependent signal across the brain. Finally, we found that a novel machine-learning subject classifier could identify these individuals by their speech activation patterns with 97% accuracy from among a dataset of seventy-five subjects. These results suggest that single-subject speech research would yield valid results and that investigations into the reliability of speech activation in people with speech disorders are warranted.Accepted manuscrip

The Value of Instability: An Investigation of Intra-Subject Variability in Brain Activity Among Obese Adolescent Girls

BACKGROUND: The present study investigated the value of intra-subject variability (ISV) as a metric for revealing differences in cognition and brain activation associated with an obese versus lean body mass. METHODS: Ninety-six adolescents with a lean body mass (BMI %-ile = 5-85), and 92 adolescents with an obese body mass (BMI %-ile \u3e=95), performed two tasks (Stroop and Go/NoGo) challenging response inhibition skills. The standard deviations and averages of their reaction time and P300 electroencephalographic responses to task stimuli were computed across trials. RESULTS: During the Go/NoGo task, the reaction times of subjects with an obese body mass were more variable than those of their lean body mass peers. Accompanying the greater ISV in reaction times was a group difference in P300 amplitude ISV in the opposite direction across both tasks. The effect sizes associated with these group differences in ISV were marginally greater than the effect sizes for the comparisons of the group means. CONCLUSIONS: ISV may be superior to the mean as a tool for differentiating groups without significant cognitive impairment. The co-occurrence of reduced ISV in P300 amplitude and elevated ISV in reaction time may indicate a constraint among obese adolescent girls in the range of information processing strategies and neural networks that can compete to optimize response output. It remains to be determined if this decrement in neural plasticity has implications for their problem solving skills as well as their response to weight management interventions

Test-retest reliability of evoked heat stimulation bold functional magnetic resonance imaging

To date, the blood oxygenated-level dependent (BOLD) functional magnetic resonance imaging (fMRI) technique has enabled an objective and deeper understanding of pain processing mechanisms embedded within the human central nervous system (CNS). In order to further comprehend the benefits and limitations of BOLD fMRI in the context of pain as well as the corresponding subjective pain ratings, we evaluated the univariate response, test-retest reliability and confidence intervals (CIs) at the 95% level of both data types collected during evoked stimulation of 40°C (non-noxious), 44°C (mildly noxious) and a subject-specific temperature eliciting a 7/10 pain rating. The test-retest reliability between two scanning sessions was determined by calculating group-level interclass correlation coefficients and at the single-subject level. Across the three stimuli, we initially observed a graded response of increasing magnitude for both visual analogue scale (VAS) pain ratings and fMRI data. Test-retest reliability was observed to be highest for VAS pain ratings obtained during the 7/10 pain stimulation (intraclass correlation coefficient (ICC) = 0.938), while ICC values of pain fMRI data for a distribution of CNS structures ranged from 0.5 to 0.859 (p < 0.05). Importantly, the upper and lower CI bounds reported herein could be utilized in subsequent trials involving healthy volunteers to hypothesize the magnitude of effect required to overcome inherent variability of either VAS pain ratings or BOLD responses evoked during innocuous or noxious thermal stimulation

Learning Repeatable Speech Embeddings Using An Intra-class Correlation Regularizer

A good supervised embedding for a specific machine learning task is only sensitive to changes in the label of interest and is invariant to other confounding factors. We leverage the concept of repeatability from measurement theory to describe this property and propose to use the intra-class correlation coefficient (ICC) to evaluate the repeatability of embeddings. We then propose a novel regularizer, the ICC regularizer, as a complementary component for contrastive losses to guide deep neural networks to produce embeddings with higher repeatability. We use simulated data to explain why the ICC regularizer works better on minimizing the intra-class variance than the contrastive loss alone. We implement the ICC regularizer and apply it to three speech tasks: speaker verification, voice style conversion, and a clinical application for detecting dysphonic voice. The experimental results demonstrate that adding an ICC regularizer can improve the repeatability of learned embeddings compared to only using the contrastive loss; further, these embeddings lead to improved performance in these downstream tasks.Comment: Accepted by NeurIPS 202

Genetically Determined Measures of Striatal D2 Signaling Predict Prefrontal Activity during Working Memory Performance

Background: Variation of the gene coding for D2 receptors (DRD2) has been associated with risk for schizophrenia and with working memory deficits. A functional intronic SNP (rs1076560) predicts relative expression of the two D2 receptors isoforms, D2S (mainly pre-synaptic) and D2L (mainly post-synaptic). However, the effect of functional genetic variation of DRD2 on striatal dopamine D2 signaling and on its correlation with prefrontal activity during working memory in humans is not known. Methods: Thirty-seven healthy subjects were genotyped for rs1076560 (G>T) and underwent SPECT with [123I]IBZM (which binds primarily to post-synaptic D2 receptors) and with [123I]FP-CIT (which binds to pre-synaptic dopamine transporters, whose activity and density is also regulated by pre-synaptic D2 receptors), as well as BOLD fMRI during N-Back working memory. Results: Subjects carrying the T allele (previously associated with reduced D2S expression) had striatal reductions of [ 123I]IBZM and of [123I]FP-CIT binding. DRD2 genotype also differentially predicted the correlation between striatal dopamine D2 signaling (as identified with factor analysis of the two radiotracers) and activity of the prefrontal cortex during working memory as measured with BOLD fMRI, which was positive in GG subjects and negative in GT. Conclusions: Our results demonstrate that this functional SNP within DRD2 predicts striatal binding of the two radiotracers to dopamine transporters and D2 receptors as well as the correlation between striatal D2 signaling with prefrontal cortex activity during performance of a working memory task. These data are consistent with the possibility that the balance of excitatory/inhibitory modulation of striatal neurons may also affect striatal outputs in relationship with prefrontal activity during working memory performance within the cortico-striatal-thalamic- cortical pathwa

Heritability of working memory brain activation

Although key to understanding individual variation in task-related brain activation, the genetic contribution to these individual differences remains largely unknown. Here we report voxel-by-voxel genetic model fitting in a large sample of 319 healthy, young adult, human identical and fraternal twins (mean ± SD age, 23.6 ± 1.8 years) who performed an n-back working memory task during functional magnetic resonance imaging (fMRI) at a high magnetic field (4 tesla). Patterns of task-related brain response (BOLD signal difference of 2-back minus 0-back) were significantly heritable, with the highest estimates (40–65%) in the inferior, middle, and superior frontal gyri, left supplementary motor area, precentral and postcentral gyri, middle cingulate cortex, superior medial gyrus, angular gyrus, superior parietal lobule, including precuneus, and superior occipital gyri. Furthermore, high test-retest reliability for a subsample of 40 twins indicates that nongenetic variance in the fMRI brain response is largely due to unique environmental influences rather than measurement error. Individual variations in activation of the working memory network are therefore significantly influenced by genetic factors. By establishing the heritability of cognitive brain function in a large sample that affords good statistical power, and using voxel-by-voxel analyses, this study provides the necessary evidence for task-related brain activation to be considered as an endophenotype for psychiatric or neurological disorders, and represents a substantial new contribution to the field of neuroimaging genetics. These genetic brain maps should facilitate discovery of gene variants influencing cognitive brain function through genome-wide association studies, potentially opening up new avenues in the treatment of brain disorders

Model specification and the reliability of fMRI results: Implications for longitudinal neuroimaging studies in psychiatry

Functional Magnetic Resonance Imagine (fMRI) is an important assessment tool in longitudinal studies of mental illness and its treatment. Understanding the psychometric properties of fMRI-based metrics, and the factors that influence them, will be critical for properly interpreting the results of these efforts. The current study examined whether the choice among alternative model specifications affects estimates of test-retest reliability in key emotion processing regions across a 6-month interval. Subjects (N = 46) performed an emotional-faces paradigm during fMRI in which neutral faces dynamically morphed into one of four emotional faces. Median voxelwise intraclass correlation coefficients (mvICCs) were calculated to examine stability over time in regions showing task-related activity as well as in bilateral amygdala. Four modeling choices were evaluated: a default model that used the canonical hemodynamic response function (HRF), a flexible HRF model that included additional basis functions, a modified CompCor (mCompCor) model that added corrections for physiological noise in the global signal, and a final model that combined the flexible HRF and mCompCor models. Model residuals were examined to determine the degree to which each pipeline met modeling assumptions. Results indicated that the choice of modeling approaches impacts both the degree to which model assumptions are met and estimates of test-retest reliability. ICC estimates in the visual cortex increased from poor (mvICC = 0.31) in the default pipeline to fair (mvICC = 0.45) in the full alternative pipeline - an increase of 45%. In nearly all tests, the models with the fewest assumption violations generated the highest ICC estimates. Implications for longitudinal treatment studies that utilize fMRI are discussed. © 2014 Fournier et al