Jeanette A. Mumford

Radiological versus histological diagnosis in UIP and NSIP: survival implications

Background: High resolution computed tomography (HRCT) has an important diagnostic role in idiopathic interstitial pneumonia (IIP). We hypothesised that the HRCT appearance would have an impact on survival in patients with IIP. Methods: HRCT scans from patients with histological usual interstitial pneumonia (UIP; n=73) or histological non-specific interstitial pneumonia (NSIP; n=23) were characterised as definite UIP, probable UIP, indeterminate, probable NSIP, or definite NSIP. Cox regression analysis examined the relationships between histopathological and radiological diagnoses and mortality, controlling for patient age, sex, and smoking status. Results: All 27 patients with definite or probable UIP on HRCT had histological UIP; 18 of 44 patients with probable or definite NSIP on HRCT had histological NSIP. Patients with HRCT diagnosed definite or probable UIP had a shorter survival than those with indeterminate CT (hazards ratio (HR) 2.43, 95% CI 1.06 to 5.58; median survival 2.08 v 5.76 years) or HRCT diagnosed definite or probable NSIP (HR 3.47, 95% CI 1.58 to 7.63; median survival 2.08 v 5.81 years). Patients with histological UIP with no HRCT diagnosis of probable or definite UIP fared better than patients with histological UIP and an HRCT diagnosis of definite or probable UIP (HR 0.49, 95% CI 0.25 to 0.98; median survival 5.76 v 2.08 years) and worse than those with a histological diagnosis of NSIP (HR 5.42, 95% CI 1.25 to 23.5; median survival 5.76 v >9 years). Conclusions: Patients with a typical HRCT appearance of UIP experience the highest mortality. A surgical lung biopsy is indicated for patients without an HRCT appearance of UIP to differentiate between histological UIP and NSIP.

Striatal Dopamine D2/D3 Receptor Availability Is Reduced in Methamphetamine Dependence and Is Linked to Impulsivity

While methamphetamine addiction has been associated with both impulsivity and striatal dopamine D2/D3 receptor deficits, human studies have not directly linked the latter two entities. We therefore compared methamphetamine-dependent and healthy control subjects using the Barratt Impulsiveness Scale (version 11, BIS-11) and positron emission tomography with [18F]fallypride to measure striatal dopamine D2/D3 receptor availability. The methamphetamine-dependent subjects reported recent use of the drug 3.3 g per week, and a history of using methamphetamine, on average, for 12.5 years. They had higher scores than healthy control subjects on all BIS-11 impulsiveness subscales (p < 0.001). Volume-of-interest analysis found lower striatal D2/D3 receptor availability in methamphetamine-dependent than in healthy control subjects (p < 0.01) and a negative relationship between impulsiveness and striatal D2/D3 receptor availability in the caudate nucleus and nucleus accumbens that reached statistical significance in methamphetamine-dependent subjects. Combining data from both groups, voxelwise analysis indicated that impulsiveness was related to D2/D3 receptor availability in left caudate nucleus and right lateral putamen/claustrum (p < 0.05, determined by threshold-free cluster enhancement). In separate group analyses, correlations involving the head and body of the caudate and the putamen of methamphetamine-dependent subjects and the lateral putamen/claustrum of control subjects were observed at a weaker threshold (p < 0.12 corrected). The findings suggest that low striatal D2/D3 receptor availability may mediate impulsive temperament and thereby influence addiction.

Functional System and Areal Organization of a Highly Sampled Individual Human Brain

Resting state functional MRI (fMRI) has enabled description of group-level functional brain organization at multiple spatial scales. However, cross-subject averaging may obscure patterns of brain organization specific to each individual. Here, we characterized the brain organization of a single individual repeatedly measured over more than a year. We report a reproducible and internally valid subject-specific areal-level parcellation that corresponds with subject-specific task activations. Highly convergent correlation network estimates can be derived from this parcellation if sufficient data are collected-considerably more than typically acquired. Notably, within-subject correlation variability across sessions exhibited a heterogeneous distribution across the cortex concentrated in visual and somato-motor regions, distinct from the pattern of intersubject variability. Further, although the individuals systems-level organization is broadly similar to the group, it demonstrates distinct topological features. These results provide a foundation for studies of individual differences in cortical organization and function, especially for special or rare individuals. VIDEO ABSTRACT.

Deconvolving BOLD activation in event-related designs for multivoxel pattern classification analyses.

Use of multivoxel pattern analysis (MVPA) to predict the cognitive state of a subject during task performance has become a popular focus of fMRI studies. The input to these analyses consists of activation patterns corresponding to different tasks or stimulus types. These activation patterns are fairly straightforward to calculate for blocked trials or slow event-related designs, but for rapid event-related designs the evoked BOLD signal for adjacent trials will overlap in time, complicating the identification of signal unique to specific trials. Rapid event-related designs are often preferred because they allow for more stimuli to be presented and subjects tend to be more focused on the task, and thus it would be beneficial to be able to use these types of designs in MVPA analyses. The present work compares 8 different models for estimating trial-by-trial activation patterns for a range of rapid event-related designs varying by interstimulus interval and signal-to-noise ratio. The most effective approach obtains each trials estimate through a general linear model including a regressor for that trial as well as another regressor for all other trials. Through the analysis of both simulated and real data we have found that this model shows some improvement over the standard approaches for obtaining activation patterns. The resulting trial-by-trial estimates are more representative of the true activation magnitudes, leading to a boost in classification accuracy in fast event-related designs with higher signal-to-noise. This provides the potential for fMRI studies that allow simultaneous optimization of both univariate and MVPA approaches.

Altered functional connectivity in frontal lobe circuits is associated with variation in the autism risk gene CNTNAP2.

Children who carry one variant of a brain protein associated with autism exhibit fewer long-range connections between the prefrontal cortex and more posterior brain regions. A Window into the Genetic Control of Brain Function Even seemingly simple traits like height are controlled by more than 180 separate genes. Imagine the complexity of the genetic network that determines the structure of the human brain: Billions of neurons connected to one another by at least as many axons. Variations in these links lead to differences among us and, sometimes, to disability, but picking out the key connections is not easy. Now, Scott-van Zeeland and colleagues show that the two versions of a protein that guides growth of the prefrontal cortex—one of which is known to confer risk of autism—generate distinct neural circuits in this region of the brain, possibly explaining the increased risk of autism and other intellectual disabilities in carriers. The protein is contactin-associated protein-like 2 (CNTNAP2), which has turned up in a number of genetic studies as associated with autism and other language-related disorders. Caspr2, the protein encoded by CNTNAP2, participates in cellular migration and in forming the final layered organization of the brain. It is expressed during development in the frontal and temporal lobes, including the frontal cortex and stratum, areas that participate in language and learning. The authors of this study have used functional magnetic resonance imaging (fMRI) of the brain to pinpoint the differences in brain structure and function that result from two variants of CNTNAP2, one of which confers risk of autism. They found in a discovery and a replication cohort of children that carriers of the risky allele showed more neural activity in the medial prefrontal cortex as they performed an assigned task. Moreover, this region was connected only locally in a diffuse bilateral network in the carriers, whereas in those with the nonrisk allele the medial prefrontal cortex conveyed information to more posterior regions via a network on the left side. This left lateralized functional anterior-posterior connection in the noncarriers involves regions of the brain known to control language processing, a skill that is defective in some people with autism. It is possible that the lack of efficient information transfer to these regions from frontal areas in the risk allele–carrying children may contribute to the increased chance that they will be affected by autism or other related disorders. The careful dissection of genetic contributions to discrete aspects of brain structure and function (so-called endophenotypes) such as reported here is one way to begin to untangle the basis of human-to-human variations in cognition and behavior. Genetic studies are rapidly identifying variants that shape risk for disorders of human cognition, but the question of how such variants predispose to neuropsychiatric disease remains. Noninvasive human brain imaging allows assessment of the brain in vivo, and the combination of genetics and imaging phenotypes remains one of the only ways to explore functional genotype-phenotype associations in human brain. Common variants in contactin-associated protein-like 2 (CNTNAP2), a neurexin superfamily member, have been associated with several allied neurodevelopmental disorders, including autism and specific language impairment, and CNTNAP2 is highly expressed in frontal lobe circuits in the developing human brain. Using functional neuroimaging, we have demonstrated a relationship between frontal lobar connectivity and common genetic variants in CNTNAP2. These data provide a mechanistic link between specific genetic risk for neurodevelopmental disorders and empirical data implicating dysfunction of long-range connections within the frontal lobe in autism. The convergence between genetic findings and cognitive-behavioral models of autism provides evidence that genetic variation at CNTNAP2 predisposes to diseases such as autism in part through modulation of frontal lobe connectivity.

Greater Neural Pattern Similarity Across Repetitions Is Associated with Better Memory

One, Two, Three, Remember Me When a stimulus (such as a word or a face) is presented for the second, third, or fourth time, do the neural representations differ? And, if they do, are multiply represented stimuli remembered better? These questions and related ones have fascinated psychologists for decades, but only recently has it become feasible to begin tackling them using neuroimaging. Xue et al. (p. 97, published online 9 September) provide evidence that the greater the similarity in the patterns of neural activity during encoding of the item, the greater the likelihood that the item will be remembered. Similarity in neural representations is associated with better memory, as well as conscious cognition. Repeated study improves memory, but the underlying neural mechanisms of this improvement are not well understood. Using functional magnetic resonance imaging and representational similarity analysis of brain activity, we found that, compared with forgotten items, subsequently remembered faces and words showed greater similarity in neural activation across multiple study in many brain regions, including (but not limited to) the regions whose mean activities were correlated with subsequent memory. This result addresses a longstanding debate in the study of memory by showing that successful episodic memory encoding occurs when the same neural representations are more precisely reactivated across study episodes, rather than when patterns of activation are more variable across time.

Independence in ROI analysis: where is the voodoo?

We discuss the effects of non-independence on region of interest (ROI) analysis of functional magnetic resonance imaging data, which has recently been raised in a prominent article by Vul et al. We outline the problem of non-independence, and use a previously published dataset to examine the effects of non-independence. These analyses show that very strong correlations (exceeding 0.8) can occur even when the ROI is completely independent of the data being analyzed, suggesting that the claims of Vul et al. regarding the implausibility of these high correlations are incorrect. We conclude with some recommendations to help limit the potential problems caused by non-independence.

Engagement of large-scale networks is related to individual differences in inhibitory control.

Understanding which brain regions regulate the execution, and suppression, of goal-directed behavior has implications for a number of areas of research. In particular, understanding which brain regions engaged during tasks requiring the execution and inhibition of a motor response provides insight into the mechanisms underlying individual differences in response inhibition ability. However, neuroimaging studies examining the relation between activation and stopping have been inconsistent regarding the direction of the relationship, and also regarding the anatomical location of regions that correlate with behavior. These limitations likely arise from the relatively low power of voxelwise correlations with small sample sizes. Here, we pooled data over five separate fMRI studies of the Stop-signal task in order to obtain a sufficiently large sample size to robustly detect brain/behavior correlations. In addition, rather than performing mass univariate correlation analysis across all voxels, we increased statistical power by reducing the dimensionality of the data set using independent component analysis and then examined correlations between behavior and the resulting component scores. We found that components reflecting activity in regions thought to be involved in stopping were associated with better stopping ability, while activity in a default-mode network was associated with poorer stopping ability across individuals. These results clearly show a relationship between individual differences in stopping ability in specific activated networks, including regions known to be critical for the behavior. The results also highlight the usefulness of using dimensionality reduction to increase the power to detect brain/behavior correlations in individual differences research.

Power calculation for group fMRI studies accounting for arbitrary design and temporal autocorrelation.

When planning most scientific studies, one of the first steps is to carry out a power analysis to define a design and sample size that will result in a well-powered study. There are limited resources for calculating power for group fMRI studies due to the complexity of the model. Previous approaches for group fMRI power calculation simplify the study design and/or the variance structure in order to make the calculation possible. These approaches limit the designs that can be studied and may result in inaccurate power calculations. We introduce a flexible power calculation model that makes fewer simplifying assumptions, leading to a more accurate power analysis that can be used on a wide variety of study designs. Our power calculation model can be used to obtain region of interest (ROI) summaries of the mean parameters and variance parameters, which can be use to increase understanding of the data as well as calculate power for a future study. Our example illustrates that minimizing cost to achieve 80% power is not as simple as finding the smallest sample size capable of achieving 80% power, since smaller sample sizes require each subject to be scanned longer.

What do differences between multi-voxel and univariate analysis mean? How subject-, voxel-, and trial-level variance impact fMRI analysis

Multi-voxel pattern analysis (MVPA) has led to major changes in how fMRI data are analyzed and interpreted. Many studies now report both MVPA results and results from standard univariate voxel-wise analysis, often with the goal of drawing different conclusions from each. Because MVPA results can be sensitive to latent multidimensional representations and processes whereas univariate voxel-wise analysis cannot, one conclusion that is often drawn when MVPA and univariate results differ is that the activation patterns underlying MVPA results contain a multidimensional code. In the current study, we conducted simulations to formally test this assumption. Our findings reveal that MVPA tests are sensitive to the magnitude of voxel-level variability in the effect of a condition within subjects, even when the same linear relationship is coded in all voxels. We also find that MVPA is insensitive to subject-level variability in mean activation across an ROI, which is the primary variance component of interest in many standard univariate tests. Together, these results illustrate that differences between MVPA and univariate tests do not afford conclusions about the nature or dimensionality of the neural code. Instead, targeted tests of the informational content and/or dimensionality of activation patterns are critical for drawing strong conclusions about the representational codes that are indicated by significant MVPA results.