Multisite reliability and repeatability of an advanced brain MRI protocol.

BACKGROUND: MRI is the imaging modality of choice for diagnosis and intervention assessment in neurological disease. Its full potential has not been realized due in part to challenges in harmonizing advanced techniques across multiple sites. PURPOSE: To develop a method for the assessment of reliability and repeatability of advanced multisite-multisession neuroimaging studies and specifically to assess the reliability of an advanced MRI protocol, including multiband fMRI and diffusion tensor MRI, in a multisite setting. STUDY TYPE: Prospective. POPULATION: Twice repeated measurement of a single subject with stable relapsing-remitting multiple sclerosis (MS) at seven institutions. FIELD STRENGTH/SEQUENCE: A 3 T MRI protocol included higher spatial resolution anatomical scans, a variable flip-angle longitudinal relaxation rate constant (R1 ≡ 1/T1 ) measurement, quantitative magnetization transfer imaging, diffusion tensor imaging, and a resting-state fMRI (rsFMRI) series. ASSESSMENT: Multiple methods of assessing intrasite repeatability and intersite reliability were evaluated for imaging metrics derived from each sequence. STATISTICAL TESTS: Student's t-test, Pearson's r, and intraclass correlation coefficient (ICC) (2,1) were employed to assess repeatability and reliability. Two new statistical metrics are introduced that frame reliability and repeatability in the respective units of the measurements themselves. RESULTS: Intrasite repeatability was excellent for quantitative R1 , magnetization transfer ratio (MTR), and diffusion-weighted imaging (DWI) based metrics (r > 0.95). rsFMRI metrics were less repeatable (r = 0.8). Intersite reliability was excellent for R1 , MTR, and DWI (ICC >0.9), and moderate for rsFMRI metrics (ICC∼0.4). DATA CONCLUSION: From most reliable to least, using a new reliability metric introduced here, MTR > R1 > DWI > rsFMRI; for repeatability, MTR > DWI > R1 > rsFMRI. A graphical method for at-a-glance assessment of reliability and repeatability, effect sizes, and outlier identification in multisite-multisession neuroimaging studies is introduced. LEVEL OF EVIDENCE: 1 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;50:878-888.

Connectome-based Models Predict Separable Components of Attention in Novel Individuals.

Although we typically talk about attention as a single process, it comprises multiple independent components. But what are these components, and how are they represented in the functional organization of the brain? To investigate whether long-studied components of attention are reflected in the brain's intrinsic functional organization, here we apply connectome-based predictive modeling (CPM) to predict the components of Posner and Petersen's influential model of attention: alerting (preparing and maintaining alertness and vigilance), orienting (directing attention to a stimulus), and executive control (detecting and resolving cognitive conflict) [Posner, M. I., & Petersen, S. E. The attention system of the human brain. Annual Review of Neuroscience, 13, 25-42, 1990]. Participants performed the Attention Network Task (ANT), which measures these three factors, and rested during fMRI scanning. CPMs tested with leave-one-subject-out cross-validation successfully predicted novel individual's overall ANT accuracy, RT variability, and executive control scores from functional connectivity observed during ANT performance. CPMs also generalized to predict participants' alerting scores from their resting-state functional connectivity alone, demonstrating that connectivity patterns observed in the absence of an explicit task contain a signature of the ability to prepare for an upcoming stimulus. Suggesting that significant variance in ANT performance is also explained by an overall sustained attention factor, the sustained attention CPM, a model defined in prior work to predict sustained attentional abilities, predicted accuracy, RT variability, and executive control from task-based data and predicted RT variability from resting-state data. Our results suggest that, whereas executive control may be closely related to sustained attention, the infrastructure that supports alerting is distinct and can be measured at rest. In the future, CPM may be applied to elucidate additional independent components of attention and relationships between the functional brain networks that predict them.

Reorganization of brain connectivity in obesity.

Global brain connectivity (GBC) identifies regions of the brain, termed "hubs," which are densely connected and metabolically costly, and have a wide influence on brain function. Since obesity is associated with central and peripheral metabolic dysfunction we sought to determine if GBC is altered in obesity. Two independent fMRI data sets were subjected to GBC analyses. The first data set was acquired while participants (n = 15 healthy weight and 15 obese) tasted milkshake and the second with participants at rest (n = 33 healthy weight and 28 obese). In the resting state and during milkshake consumption GBC is consistently decreased in the ventromedial and ventrolateral prefrontal cortex, insula and caudate nucleus, and increased in brain regions belonging to the dorsal attention network including premotor areas, superior parietal lobule, and visual cortex. During milkshake consumption, but not at rest, additional decreases in GBC are observed in feeding-related circuitry including the insula, amygdala, anterior hippocampus, hypothalamus, midbrain, brainstem and somatomotor cortex. Additionally, GBC differences were not accounted for by age. These results demonstrate that obesity is associated with decreased GBC in prefrontal and feeding circuits and increased GBC in the dorsal attention network. We therefore conclude that global brain organization is altered in obesity to favor networks important for external orientation over those monitoring homeostatic state and guiding feeding decisions. Furthermore, since prefrontal decreases are also observed at rest in obese individuals future work should evaluate whether these changes are associated with neurocognitive impairments frequently observed in obesity and diabetes. Hum Brain Mapp 38:1403-1420, 2017. © 2016 Wiley Periodicals, Inc.

Human visual consciousness involves large scale cortical and subcortical networks independent of task report and eye movement activity.

The full neural circuits of conscious perception remain unknown. Using a visual perception task, we directly recorded a subcortical thalamic awareness potential (TAP). We also developed a unique paradigm to classify perceived versus not perceived stimuli using eye measurements to remove confounding signals related to reporting on conscious experiences. Using fMRI, we discovered three major brain networks driving conscious visual perception independent of report: first, increases in signal detection regions in visual, fusiform cortex, and frontal eye fields; and in arousal/salience networks involving midbrain, thalamus, nucleus accumbens, anterior cingulate, and anterior insula; second, increases in frontoparietal attention and executive control networks and in the cerebellum; finally, decreases in the default mode network. These results were largely maintained after excluding eye movement-based fMRI changes. Our findings provide evidence that the neurophysiology of consciousness is complex even without overt report, involving multiple cortical and subcortical networks overlapping in space and time.

Using functional connectivity models to characterize relationships between working and episodic memory.

INTRODUCTION: Working memory is a critical cognitive ability that affects our daily functioning and relates to many cognitive processes and clinical conditions. Episodic memory is vital because it enables individuals to form and maintain their self-identities. Our study analyzes the extent to which whole-brain functional connectivity observed during completion of an N-back memory task, a common measure of working memory, can predict both working memory and episodic memory. METHODS: We used connectome-based predictive models (CPMs) to predict 502 Human Connectome Project (HCP) participants' in-scanner 2-back memory test scores and out-of-scanner working memory test (List Sorting) and episodic memory test (Picture Sequence and Penn Word) scores based on functional magnetic resonance imaging (fMRI) data collected both during rest and N-back task performance. We also analyzed the functional brain connections that contributed to prediction for each of these models. RESULTS: Functional connectivity observed during N-back task performance predicted out-of-scanner List Sorting scores and to a lesser extent out-of-scanner Picture Sequence scores, but did not predict out-of-scanner Penn Word scores. Additionally, the functional connections predicting 2-back scores overlapped to a greater degree with those predicting List Sorting scores than with those predicting Picture Sequence or Penn Word scores. Functional connections with the insula, including connections between insular and parietal regions, predicted scores across the 2-back, List Sorting, and Picture Sequence tasks. CONCLUSIONS: Our findings validate functional connectivity observed during the N-back task as a measure of working memory, which generalizes to predict episodic memory to a lesser extent. By building on our understanding of the predictive power of N-back task functional connectivity, this work enhances our knowledge of relationships between working memory and episodic memory.

Differential Resting State Connectivity Responses to Glycemic State in Type 1 Diabetes.

CONTEXT: Individuals with type 1 diabetes mellitus (T1DM) have alterations in brain activity that have been postulated to contribute to the adverse neurocognitive consequences of T1DM; however, the impact of T1DM and hypoglycemic unawareness on the brain's resting state activity remains unclear. OBJECTIVE: To determine whether individuals with T1DM and hypoglycemia unawareness (T1DM-Unaware) had changes in the brain resting state functional connectivity compared to healthy controls (HC) and those with T1DM and hypoglycemia awareness (T1DM-Aware). DESIGN: Observational study. SETTING: Academic medical center. PARTICIPANTS: 27 individuals with T1DM and 12 HC volunteers participated in the study. INTERVENTION: All participants underwent blood oxygenation level dependent (BOLD) resting state functional magnetic brain imaging during a 2-step hyperinsulinemic euglycemic (90 mg/dL)-hypoglycemic (60 mg/dL) clamp. OUTCOME: Changes in resting state functional connectivity. RESULTS: Using 2 separate methods of functional connectivity analysis, we identified distinct differences in the resting state brain responses to mild hypoglycemia between HC, T1DM-Aware, and T1DM-Unaware participants, particularly in the angular gyrus, an integral component of the default mode network (DMN). Furthermore, changes in angular gyrus connectivity also correlated with greater symptoms of hypoglycemia (r = 0.461, P = 0.003) as well as higher scores of perceived stress (r = 0.531, P = 0.016). CONCLUSION: These findings provide evidence that individuals with T1DM have changes in the brain's resting state connectivity patterns, which may be further associated with differences in awareness to hypoglycemia. These changes in connectivity may be associated with alterations in functional outcomes among individuals with T1DM.

Behavioral and brain signatures of substance use vulnerability in childhood.

The prevalence of risky behavior such as substance use increases during adolescence; however, the neurobiological precursors to adolescent substance use remain unclear. Predictive modeling may complement previous work observing associations with known risk factors or substance use outcomes by developing generalizable models that predict early susceptibility. The aims of the current study were to identify and characterize behavioral and brain models of vulnerability to future substance use. Principal components analysis (PCA) of behavioral risk factors were used together with connectome-based predictive modeling (CPM) during rest and task-based functional imaging to generate predictive models in a large cohort of nine- and ten-year-olds enrolled in the Adolescent Brain & Cognitive Development (ABCD) study (NDA release 2.0.1). Dimensionality reduction (n = 9,437) of behavioral measures associated with substance use identified two latent dimensions that explained the largest amount of variance: risk-seeking (PC1; e.g., curiosity to try substances) and familial factors (PC2; e.g., family history of substance use disorder). Using cross-validated regularized regression in a subset of data (Year 1 Fast Track data; n>1,500), functional connectivity during rest and task conditions (resting-state; monetary incentive delay task; stop signal task; emotional n-back task) significantly predicted individual differences in risk-seeking (PC1) in held-out participants (partial correlations between predicted and observed scores controlling for motion and number of frames [rp]: 0.07-0.21). By contrast, functional connectivity was a weak predictor of familial risk factors associated with substance use (PC2) (rp: 0.03-0.06). These results demonstrate a novel approach to understanding substance use vulnerability, which-together with mechanistic perspectives-may inform strategies aimed at early identification of risk for addiction.

Language lateralization in epilepsy patients: fMRI validated with the Wada procedure.

PURPOSE: This work examines the efficacy of functional magnetic resonance imaging (fMRI) for language lateralization using a comprehensive three-task language-mapping approach. Two localization methods and four different metrics for quantifying activation within hemisphere are compared and validated with Wada testing. Sources of discordance between fMRI and Wada lateralization are discussed with respect to specific patient examples. METHODS: fMRI language mapping was performed in patients with epilepsy (N = 40) using reading sentence comprehension, auditory sentence comprehension, and a verbal fluency task. This was compared with the Wada procedure using both whole-brain and midline exclusion-based analyses. Different laterality scores were examined as a function of statistical threshold to investigate the sensitivity to threshold effects. RESULTS: For the lateralized patients categorized by Wada, fMRI laterality indices (LIs) were concordant with the Wada procedure results in 83.87% patients for the reading task, 83.33% patients for the auditory task, 76.92% patients for the verbal fluency task, and in 91.3% patients for the conjunction analysis. The patients categorized as bilateral via the Wada procedure showed some hemispheric dominance in fMRI, and discrepancies between the Wada test findings and the functional laterality scores arose for a range of reasons. DISCUSSION: Discordance was dependent upon whether whole-brain or midline exclusion method-based lateralization was calculated, and in the former case the inclusion of the occipital and other midline regions often negatively influenced the lateralization scores. Overall fMRI was in agreement with the Wada test in 91.3% of patients, suggesting its utility for clinical use with the proper consideration given to the confounds discussed in this work.

Additive effects of serotonin transporter and tryptophan hydroxylase-2 gene variation on neural correlates of affective processing.

Individual differences in brain response to emotional stimuli have previously been associated with gene variations within the serotonin transporter (5-HTT) and tryptophan hydroxylase-2 (TPH2) genes. We recently reported that these two genes exhibit an additive effect, based on recordings of event-related potentials (ERPs) from individuals viewing emotional scenes. The current study was designed to replicate and extent this initial report in an independent study sample, and use functional magnetic resonance imaging (fMRI) to identify specific neural loci that may mediate the 5-HTT-TPH2 additive effect. Furthermore, we sought to obtain convergent evidence for a gene-gene additive effect by collecting fMRI data from the same individuals engaged in two different cognitive-affective tasks, using emotional and neutral facial expressions and word stimuli. We found evidence for an additive effect of 5-HTT-TPH2 genotype, which was most robust in the putamen, a region rich in both 5-HTT and TPH2 protein, but was also observed in the amygdala at a less stringent threshold, and in other cortical regions. The additive effect was more robust effect for visuospatial than for verbal stimuli, and more robust for negatively than for positively valenced stimuli. These findings confirm and extend the additive effect of two critical genes in the serotonergic regulation of neural processing of affective stimuli, and identify the striatum as a critical site where is gene-gene regulation takes place.

Individual variation in functional brain connectivity: implications for personalized approaches to psychiatric disease.

Functional brain connectivity measured with functional magnetic resonance imaging (fMRI) is a popular technique for investigating neural organization in both healthy subjects and patients with mental illness. Despite a rapidly growing body of literature, however, functional connectivity research has yet to deliver biomarkers that can aid psychiatric diagnosis or prognosis at the single-subject level. One impediment to developing such practical tools has been uncertainty regarding the ratio of intra- to interindividual variability in functional connectivity; in other words, how much variance is state- versus trait-related. Here, we review recent evidence that functional connectivity profiles are both reliable within subjects and unique across subjects, and that features of these profiles relate to behavioral phenotypes. Together, these results suggest the potential to discover reliable correlates of present and future illness and/or response to treatment in the strength of an individual's functional brain connections. Ultimately, this work could help develop personalized approaches to psychiatric illness.

La conectividad cerebral funcional medida con resonancia magnética funcional (RNMf) es una técnica habitual para investigar la organización neural tanto en sujetos sanos como en pacientes con patología mental. A pesar del rápido crecimiento del volumen de literatura, todavía la investigación de la conectividad funcional tiene que aportar biomarcadores que puedan ayudar al diagnóstico o pronóstico psiquiátrico para un sujeto en particular. Un impedimento para desarrollar tales herramientas prácticas han sido las dudas relacionadas con el porcentaje de la variabilidad intra e interindividual en la conectividad funcional, es decir, cuánto de la variación es estado versus rasgo dependiente. En este artículo se revisa la evidencia reciente acerca de los perfiles de conectividad funcional que son confiables para todos los sujetos como específicos entre ellos y las características de estos perfiles relacionadas con los fenotipos conductuales. En conjunto estos resultados sugieren la posibilidad de descubrir correlatos confiables de la enfermedad y/o de la respuesta al tratamiento actual o futura en la intensidad de las conexiones cerebrales funcionales de un individuo. Por último, este trabajo podría contribuir al desarrollo de aproximaciones personalizadas a la patología psiquiátrica.

La mesure par IRMf (imagerie par résonance magnétique fonctionnelle) de la connectivité fonctionnelle cérébrale est une technique courante d'observation de l'organisation neurologique chez les sujets sains et les sujets souffrant de maladie mentale. La littérature scientifique s'enrichit rapidement mais néanmoins, la recherche sur la connectivité fonctionnelle n'a pas encore trouvé de biomarqueurs qui aideraient au diagnostic ou au pronostic psychiatrique à un niveau individuel. L'incertitude du rapport de la variabilité intra- à interindividuelle dans la connectivité fonctionnelle est un des obstacles au développement de tels outils pratiques; en d'autres termes, dans quelle mesure la variance est-elle liée à l'état plutôt qu'à une caractéristique stable ? Nous analysons ici les données récentes selon lesquelles les profils de connectivité fonctionnelle sont à la fois fiables et propres à chaque individu, les caractéristiques de ces profils étant liées à des phénotypes comportementaux. D'après ces résultats, il est possible de découvrir des corrélats fiables de maladie actuelle ou à venir et/ou de réponse au traitement d'après la puissance des connections cérébrales fonctionnelles d'un individu. À terme, ce travail pourrait aider au développement d'approches personnalisées pour les maladies psychiatriques.

Language at rest: A longitudinal study of intrinsic functional connectivity in preterm children.

BACKGROUND: Preterm (PT) children show early cognitive and language deficits and display altered cortical connectivity for language compared to term (T) children. Developmentally, functional connectivity networks become more segregated and integrated, through the weakening of short-range and strengthening of long-range connections. METHODS: Longitudinal intrinsic connectivity distribution (ICD) values were assessed in PT (n = 13) compared to T children (n = 12) at ages 8 vs. 16 using a Linear Mixed Effects model. Connectivity values in regions generated by the group × age interaction analysis were then correlated to scores on full IQ (FSIQ), verbal IQ (VIQ), verbal comprehension IQ (VCIQ), performance IQ (PIQ), Peabody picture vocabulary test-revised (PPVT-R), and Rapid Naming Composite (RDRL_Cmp). RESULTS: Nine regions were generated by the group × age interaction analysis. PT connectivity significantly increased over time in all but two regions, and they ultimately displayed greater relative connectivity at age 16 than Ts in all areas except the left occipito-temporal cortex (OTC). PTs underwent significant connectivity reductions in the left OTC, which corresponded with worse performance on FSIQ, VIQ, and PIQ. These findings differed from Ts, who did not undergo any significant changes in connectivity over time. CONCLUSIONS: These findings suggest that the developmental alterations in connectivity in PT children at adolescence are both pervasive and widespread. The persistent and worsening cognitive and language deficits noted in the PT subjects may be attributed to the loss of connections in the left OTC.

Amygdala gray matter concentration is associated with extraversion and neuroticism.

Using high-resolution magnetic resonance imaging and voxel-based morphometry in 41 healthy individuals, this study evaluated the association between the personality traits of extraversion and neuroticism, on the one hand, and individual differences in localized brain volume and gray matter concentration, on the other, with a special focus on the amygdala. Extraversion was positively correlated with gray matter concentration in the left amygdala, whereas neuroticism was negatively correlated with gray matter concentration in the right amygdala. Given that neuroticism is a risk factor for depression, our finding offers one explanation as to why prior structural imaging studies of depressed patients (which did not control for personality) produced conflicting findings. Furthermore, our data are consistent with the view that amygdala reduction seen in depressed patients precedes the onset of the disease, rather than being a consequence of the illness.

Brain tissue segmentation based on corrected gray-scale analysis.

Image signal-to-noise ratio (SNR) and signal intensity (SI) inhomogeneities are factors that strongly affect the accuracy and precision of brain tissue segmentations in magnetic resonance image (MRI). In this work, SNR and contrast of brain images are optimized by TR and inversion recovery time TI in multi-spectrum MRI data sets. SI inhomogeneities are measured in vivo using a recently developed method allowing improved correction. The three-Gaussain distribution model is used to fit histograms of the images to find the initialization parameters for an Expectation-Maximization (EM) segmentation algorithm. Compared with other methods, the field map method provides better correction of SI inhomogeneities and excellent segmentation results.