Emily S. Finn

A neuromarker of sustained attention from whole-brain functional connectivity

Although attention plays a ubiquitous role in perception and cognition, researchers lack a simple way to measure a persons overall attentional abilities. Because behavioral measures are diverse and difficult to standardize, we pursued a neuromarker of an important aspect of attention, sustained attention, using functional magnetic resonance imaging. To this end, we identified functional brain networks whose strength during a sustained attention task predicted individual differences in performance. Models based on these networks generalized to previously unseen individuals, even predicting performance from resting-state connectivity alone. Furthermore, these same models predicted a clinical measure of attention—symptoms of attention deficit hyperactivity disorder—from resting-state connectivity in an independent sample of children and adolescents. These results demonstrate that whole-brain functional network strength provides a broadly applicable neuromarker of sustained attention.

The (in)stability of functional brain network measures across thresholds.

The large-scale organization of the brain has features of complex networks that can be quantified using network measures from graph theory. However, many network measures were designed to be calculated on binary graphs, whereas functional brain organization is typically inferred from a continuous measure of correlations in temporal signal between brain regions. Thresholding is a necessary step to use binary graphs derived from functional connectivity data. However, there is no current consensus on what threshold to use, and network measures and group contrasts may be unstable across thresholds. Nevertheless, whole-brain network analyses are being applied widely with findings typically reported at an arbitrary threshold or range of thresholds. This study sought to evaluate the stability of network measures across thresholds in a large resting state functional connectivity dataset. Network measures were evaluated across absolute (correlation-based) and proportional (sparsity-based) thresholds, and compared between sex and age groups. Overall, network measures were found to be unstable across absolute thresholds. For example, the direction of group differences in a given network measure may change depending on the threshold. Network measures were found to be more stable across proportional thresholds. These results demonstrate that caution should be used when applying thresholds to functional connectivity data and when interpreting results from binary graph models.

Sex differences in normal age trajectories of functional brain networks

Resting‐state functional magnetic resonance image (rs‐fMRI) is increasingly used to study functional brain networks. Nevertheless, variability in these networks due to factors such as sex and aging is not fully understood. This study explored sex differences in normal age trajectories of resting‐state networks (RSNs) using a novel voxel‐wise measure of functional connectivity, the intrinsic connectivity distribution (ICD). Males and females showed differential patterns of changing connectivity in large‐scale RSNs during normal aging from early adulthood to late middle‐age. In some networks, such as the default‐mode network, males and females both showed decreases in connectivity with age, albeit at different rates. In other networks, such as the fronto‐parietal network, males and females showed divergent connectivity trajectories with age. Main effects of sex and age were found in many of the same regions showing sex‐related differences in aging. Finally, these sex differences in aging trajectories were robust to choice of preprocessing strategy, such as global signal regression. Our findings resolve some discrepancies in the literature, especially with respect to the trajectory of connectivity in the default mode, which can be explained by our observed interactions between sex and aging. Overall, results indicate that RSNs show different aging trajectories for males and females. Characterizing effects of sex and age on RSNs are critical first steps in understanding the functional organization of the human brain. Hum Brain Mapp 36:1524–1535, 2015.

Using connectome-based predictive modeling to predict individual behavior from brain connectivity

Neuroimaging is a fast-developing research area in which anatomical and functional images of human brains are collected using techniques such as functional magnetic resonance imaging (fMRI), diffusion tensor imaging (DTI), and electroencephalography (EEG). Technical advances and large-scale data sets have allowed for the development of models capable of predicting individual differences in traits and behavior using brain connectivity measures derived from neuroimaging data. Here, we present connectome-based predictive modeling (CPM), a data-driven protocol for developing predictive models of brain-behavior relationships from connectivity data using cross-validation. This protocol includes the following steps: (i) feature selection, (ii) feature summarization, (iii) model building, and (iv) assessment of prediction significance. We also include suggestions for visualizing the most predictive features (i.e., brain connections). The final result should be a generalizable model that takes brain connectivity data as input and generates predictions of behavioral measures in novel subjects, accounting for a considerable amount of the variance in these measures. It has been demonstrated that the CPM protocol performs as well as or better than many of the existing approaches in brain-behavior prediction. As CPM focuses on linear modeling and a purely data-driven approach, neuroscientists with limited or no experience in machine learning or optimization will find it easy to implement these protocols. Depending on the volume of data to be processed, the protocol can take 10-100 min for model building, 1-48 h for permutation testing, and 10-20 min for visualization of results.

Potential Use and Challenges of Functional Connectivity Mapping in Intractable Epilepsy

This review focuses on the use of resting-state functional magnetic resonance imaging data to assess functional connectivity in the human brain and its application in intractable epilepsy. This approach has the potential to predict outcomes for a given surgical procedure based on the pre-surgical functional organization of the brain. Functional connectivity can also identify cortical regions that are organized differently in epilepsy patients either as a direct function of the disease or through indirect compensatory responses. Functional connectivity mapping may help identify epileptogenic tissue, whether this is a single focal location or a network of seizure-generating tissues. This review covers the basics of connectivity analysis and discusses particular issues associated with analyzing such data. These issues include how to define nodes, as well as differences between connectivity analyses of individual nodes, groups of nodes, and whole-brain assessment at the voxel level. The need for arbitrary thresholds in some connectivity analyses is discussed and a solution to this problem is reviewed. Overall, functional connectivity analysis is becoming an important tool for assessing functional brain organization in epilepsy.

Ketamine-Induced Hallucinations

Background: Ketamine, the NMDA glutamate receptor antagonist drug, is increasingly employed as an experimental model of psychosis in healthy volunteers. At subanesthetic doses, it safely and reversibly causes delusion-like ideas, amotivation and perceptual disruptions reminiscent of the aberrant salience experiences that characterize first-episode psychosis. However, auditory verbal hallucinations, a hallmark symptom of schizophrenia, have not been reported consistently in healthy volunteers even at high doses of ketamine. Sampling and Methods: Here we present data from a set of healthy participants who received moderately dosed, placebo-controlled ketamine infusions in the reduced stimulation environment of the magnetic resonance imaging (MRI) scanner. We highlight the phenomenological experiences of 3 participants who experienced particularly vivid hallucinations. Results: Participants in this series reported auditory verbal and musical hallucinations at a ketamine dose that does not induce auditory hallucination outside of the scanner. Conclusions: We interpret the observation of ketamine-induced auditory verbal hallucinations in the context of the reduced perceptual environment of the MRI scanner and offer an explanation grounded in predictive coding models of perception and psychosis - the brain fills in expected perceptual inputs, and it does so more in situations of altered perceptual input. The altered perceptual input of the MRI scanner creates a mismatch between top-down perceptual expectations and the heightened bottom-up signals induced by ketamine. Such circumstances induce aberrant percepts, including musical and auditory verbal hallucinations. We suggest that these circumstances might represent a useful experimental model of auditory verbal hallucinations and highlight the impact of ambient sensory stimuli on psychopathology.

Multisite reliability of MR-based functional connectivity

Abstract Recent years have witnessed an increasing number of multisite MRI functional connectivity (fcMRI) studies. While multisite studies provide an efficient way to accelerate data collection and increase sample sizes, especially for rare clinical populations, any effects of site or MRI scanner could ultimately limit power and weaken results. Little data exists on the stability of functional connectivity measurements across sites and sessions. In this study, we assess the influence of site and session on resting state functional connectivity measurements in a healthy cohort of traveling subjects (8 subjects scanned twice at each of 8 sites) scanned as part of the North American Prodrome Longitudinal Study (NAPLS). Reliability was investigated in three types of connectivity analyses: (1) seed‐based connectivity with posterior cingulate cortex (PCC), right motor cortex (RMC), and left thalamus (LT) as seeds; (2) the intrinsic connectivity distribution (ICD), a voxel‐wise connectivity measure; and (3) matrix connectivity, a whole‐brain, atlas‐based approach to assessing connectivity between nodes. Contributions to variability in connectivity due to subject, site, and day‐of‐scan were quantified and used to assess between‐session (test‐retest) reliability in accordance with Generalizability Theory. Overall, no major site, scanner manufacturer, or day‐of‐scan effects were found for the univariate connectivity analyses; instead, subject effects dominated relative to the other measured factors. However, summaries of voxel‐wise connectivity were found to be sensitive to site and scanner manufacturer effects. For all connectivity measures, although subject variance was three times the site variance, the residual represented 60–80% of the variance, indicating that connectivity differed greatly from scan to scan independent of any of the measured factors (i.e., subject, site, and day‐of‐scan). Thus, for a single 5 min scan, reliability across connectivity measures was poor (ICC=0.07–0.17), but increased with increasing scan duration (ICC=0.21–0.36 at 25 min). The limited effects of site and scanner manufacturer support the use of multisite studies, such as NAPLS, as a viable means of collecting data on rare populations and increasing power in univariate functional connectivity studies. However, the results indicate that aggregation of fcMRI data across longer scan durations is necessary to increase the reliability of connectivity estimates at the single‐subject level. HighlightsfcMRI (seed, matrix, ICD) is stable across 8 sites in a Traveling Subjects dataset.No major site, scanner manufacturer, or day‐of‐scan effects were found (GLM).No outlier sites were found (leave‐one‐site‐out analysis of variance).Reliability substantially improves when averaging data over multiple days.Data can be combined across sites to increase power without impacting reliability.

Can brain state be manipulated to emphasize individual differences in functional connectivity

&NA; While neuroimaging studies typically collapse data from many subjects, brain functional organization varies between individuals, and characterizing this variability is crucial for relating brain activity to behavioral phenotypes. Rest has become the default state for probing individual differences, chiefly because it is easy to acquire and a supposed neutral backdrop. However, the assumption that rest is the optimal condition for individual differences research is largely untested. In fact, other brain states may afford a better ratio of within‐ to between‐subject variability, facilitating biomarker discovery. Depending on the trait or behavior under study, certain tasks may bring out meaningful idiosyncrasies across subjects, essentially enhancing the individual signal in networks of interest beyond what can be measured at rest. Here, we review theoretical considerations and existing work on how brain state influences individual differences in functional connectivity, present some preliminary analyses of within‐ and between‐subject variability across conditions using data from the Human Connectome Project, and outline questions for future study. HighlightsRest is the default for studying individual differences in functional connectivity.But certain tasks may improve the ratio of within‐ to between‐subject variability.We review work on how scan condition influences individual differences.Preliminary results using HCP data show individual differences change with task.Using certain tasks over rest may improve sensitivity of imaging‐based biomarkers.

Individual differences in functional connectivity during naturalistic viewing conditions

&NA; Naturalistic viewing paradigms such as movies have been shown to reduce participant head motion and improve arousal during fMRI scanning relative to task‐free rest, and have been used to study both functional connectivity and stimulus‐evoked BOLD‐signal changes. These task‐based hemodynamic changes are synchronized across subjects and involve large areas of the cortex, and it is unclear whether individual differences in functional connectivity are enhanced or diminished under such naturalistic conditions. This work first aims to characterize variability in BOLD‐signal based functional connectivity (FC) across 2 distinct movie conditions and eyes‐open rest (n=31 healthy adults, 2 scan sessions each). We found that movies have higher within‐ and between‐subject correlations in cluster‐wise FC relative to rest. The anatomical distribution of inter‐individual variability was similar across conditions, with higher variability occurring at the lateral prefrontal lobes and temporoparietal junctions. Second, we used an unsupervised test‐retest matching algorithm that identifies individual subjects from within a group based on FC patterns, quantifying the accuracy of the algorithm across the three conditions. The movies and resting state all enabled identification of individual subjects based on FC matrices, with accuracies between 61% and 100%. Overall, pairings involving movies outperformed rest, and the social, faster‐paced movie attained 100% accuracy. When the parcellation resolution, scan duration, and number of edges used were increased, accuracies improved across conditions, and the pattern of movies>rest was preserved. These results suggest that using dynamic stimuli such as movies enhances the detection of FC patterns that are unique at the individual level. HighlightsWithin‐ and between‐subject FC correlations are compared across rest and movies.Movies outperform rest in an unsupervised identification algorithm based on FC.Movies outperform rest regardless of scan duration or number of edges used.Watching movies enhances the detection of individual differences in FC.

Methylphenidate Modulates Functional Network Connectivity to Enhance Attention

Recent work has demonstrated that human whole-brain functional connectivity patterns measured with fMRI contain information about cognitive abilities, including sustained attention. To derive behavioral predictions from connectivity patterns, our group developed a connectome-based predictive modeling (CPM) approach (Finn et al., 2015; Rosenberg et al., 2016). Previously using CPM, we defined a high-attention network, comprising connections positively correlated with performance on a sustained attention task, and a low-attention network, comprising connections negatively correlated with performance. Validating the networks as generalizable biomarkers of attention, models based on network strength at rest predicted attention-deficit/hyperactivity disorder (ADHD) symptoms in an independent group of individuals (Rosenberg et al., 2016). To investigate whether these networks play a causal role in attention, here we examined their strength in healthy adults given methylphenidate (Ritalin), a common ADHD treatment, compared with unmedicated controls. As predicted, individuals given methylphenidate showed patterns of connectivity associated with better sustained attention: higher high-attention and lower low-attention network strength than controls. There was significant overlap between the high-attention network and a network with greater strength in the methylphenidate group, and between the low-attention network and a network with greater strength in the control group. Network strength also predicted behavior on a stop-signal task, such that participants with higher go response rates showed higher high-attention and lower low-attention network strength. These results suggest that methylphenidate acts by modulating functional brain networks related to sustained attention, and that changing whole-brain connectivity patterns may help improve attention. SIGNIFICANCE STATEMENT Recent work identified a promising neuromarker of sustained attention based on whole-brain functional connectivity networks. To investigate the causal role of these networks in attention, we examined their response to a dose of methylphenidate, a common and effective treatment for attention-deficit/hyperactivity disorder, in healthy adults. As predicted, individuals on methylphenidate showed connectivity signatures of better sustained attention: higher high-attention and lower low-attention network strength than controls. These results suggest that methylphenidate acts by modulating strength in functional brain networks related to attention, and that changing whole-brain connectivity patterns may improve attention.