Several recent reports in large, independent samples have demonstrated the influence of motion artifact on resting-state functional connectivity MRI (rsfc-MRI). Standard rsfc-MRI preprocessing typically includes regression of confounding signals and band-pass filtering. However, substantial heterogeneity exists in how these techniques are implemented across studies, and no prior study has examined the effect of differing approaches for the control of motion-induced artifacts. To better understand how in-scanner head motion affects rsfc-MRI data, we describe the spatial, temporal, and spectral characteristics of motion artifacts in a sample of 348 adolescents. Analyses utilize a novel approach for describing head motion on a voxelwise basis. Next, we systematically evaluate the efficacy of a range of confound regression and filtering techniques for the control of motion-induced artifacts. Results reveal that the effectiveness of preprocessing procedures on the control of motion is heterogeneous, and that improved preprocessing provides a substantial benefit beyond typical procedures. These results demonstrate that the effect of motion on rsfc-MRI can be substantially attenuated through improved preprocessing procedures, but not completely removed.

A meta-analysis of neuropsychological studies of patients with bipolar disorder in euthymic, manic/ mixed or depressed phases of illness was conducted. Measures of attention, working memory, verbal and non-verbal memory, visuospatial function, psychomotor speed, language, and executivefunction were evaluated in 42 studies of 1197 patients in euthymia, 13 studies consisting of 314 patients in a manic/mixed phase of illness, and 5 studies of 96 patients in a depressed state. Cohen d-values were calculated for each study as the mean difference between patient and control group score on each neuropsychological measure, expressed in pooled standard deviation units. Results for patients in euthymic, depressed and manic/mixed phases were evaluated separately and then a subset of measures on which patients in all three phases were tested were compared. For euthymia, results revealed impairment across all neuropsychological domains, with d-values in the moderate-large range (d=.5-.8) for the vast majority of measures. There was evidence of large effect-size impairment on measures of verbal learning (d=.81), and delayed verbal and non-verbal memory (d=.80-.92), while effect-size impairment on measures of visuospatial function was small-to-moderate (d≤.55). Patients tested during a manic/mixed or depressed phase of illness showed exaggerated impairment on measures of verbal learning, while patients tested during a depressed phase showed greater decrement on measures of phonemic fluency. Consistent with previous meta-analyses (Arts et al, 2007; Bora et al., 2009; Robinson et al., 2007), these results suggest that bipolar illness during euthymia is characterized by generalized moderate level impairment across an array of neurocognitive domains, with particular marked impairment in verbal learning and memory. These results also show that a subset of these deficits moderately worsen during acute disease states. Keywords bipolar illness; neurocognition; mania; depression; euthymia Over the past 10-15 years a growing number of studies have revealed that individuals with bipolar illness show deficits on standardized neuropsychological measures with particularly marked deficits in executive-function and verbal learning (see Arts et al, 2007; Bora et al., 2009;Robinson et al., 2006). Particular significance has been attached to these deficits as they have been linked to the intensity of the disease process (e.g., Denicoff et al., 1999;Ferrier et al., 1999), are persistent despite psychiatric symptom reduction (e.g., Joffe et al. 1998) and Correspondence regarding this article should be addressed to: Matthew M. Kurtz, Ph.D., Department of Psychology, Judd Hall, Wesleyan University, Middletown, CT. 06459; mkurtz@wesleyan.edu. Publisher's Disclaimer: The following manuscript is the final accepted manuscript. It has not been subjected to the final copyediting, fact-checking, and proofreading required for formal publication. It is not the definitive, publisher-authenticated version. The American Psychological Association an...

The ventral striatum (VS) is a critical brain region for reinforcement learning and motivation. Intrinsically motivated subjects performing challenging cognitive tasks engage reinforcement circuitry including VS even in the absence of external feedback or incentives. However, little is known about how such VS responses develop with age, relate to task performance, and are influenced by task difficulty. Here we used fMRI to examine VS activation to correct and incorrect responses during a standard n-back working memory task in a large sample (n= 304) of healthy children, adolescents and young adults aged 8–22. We found that bilateral VS activates more strongly to correct than incorrect responses, and that the VS response scales with the difficulty of the working memory task. Furthermore, VS response was correlated with discrimination performance during the task, and the magnitude of VS response peaked in mid-adolescence. These findings provide evidence for scalable intrinsic reinforcement signals during standard cognitive tasks, and suggest a novel link between motivation and cognition during adolescent development.

An important aspect of adaptive learning is the ability to flexibly use past experiences to guide new decisions. When facing a new decision, some people automatically leverage previously learned associations, while others do not. This variability in transfer of learning across individuals has been demonstrated repeatedly and has important implications for understanding adaptive behavior, yet the source of these individual differences remains poorly understood. In particular, it is unknown why such variability in transfer emerges even among homogeneous groups of young healthy participants who do not vary on other learning-related measures. Here we hypothesized that individual differences in the transfer of learning could be related to relatively stable differences in intrinsic brain connectivity, which could constrain how individuals learn. To test this, we obtained a behavioral measure of memory-based transfer outside of the scanner and on a separate day acquired resting-state functional MRI images in 42 participants. We then analyzed connectivity across independent component analysis-derived brain networks during rest, and tested whether intrinsic connectivity in learning-related networks was associated with transfer. We found that individual differences in transfer were related to intrinsic connectivity between the hippocampus and the ventromedial prefrontal cortex, and between these regions and large-scale functional brain networks. Together, the findings demonstrate a novel role for intrinsic brain dynamics in flexible learning-guided behavior, both within a set of functionally specific regions known to be important for learning, as well as between these regions and the default and frontoparietal networks, which are thought to serve more general cognitive functions.

Complex learned behaviors must involve the integrated action of distributed brain circuits. While the contributions of individual regions to learning have been extensively investigated, understanding how distributed brain networks orchestrate their activity over the course of learning remains elusive. To address this gap, we used fMRI combined with tools from dynamic network neuroscience to obtain time--resolved descriptions of network coordination during reinforcement learning. We found that learning to associate visual cues with reward involves dynamic changes in network coupling between the striatum and distributed brain regions, including visual, orbitofrontal, and ventromedial prefrontal cortex. Moreover, we found that flexibility in striatal network dynamics correlates with participants' learning rate and inverse temperature, two parameters derived from reinforcement learning models. Finally, we found that not all forms of learning relate to this circuit: episodic memory, measured in the same participants at the same time, was related to dynamic connectivity in distinct brain networks. These results suggest that dynamic changes in striatal--centered networks provide a mechanism for information integration during reinforcement learning. Significance Statement Learning from the outcomes of actions ----referred to as reinforcement learning ----is an essential part of life. The roles of individual brain regions in reinforcement learning have been well characterized in terms of the updating of values for actions or sensory stimuli. Missing from this account, however, is a description of the manner in which different brain areas interact during learning to integrate sensory and value information. Here we characterize flexible striatal--cortical network dynamics that relate to reinforcement learning behavior.. CC-BY-NC-ND 4.0 International license peer-reviewed) is the author/funder. It is made available under a The copyright holder for this preprint (which was not . http://dx.doi.org/10.1101/094383 doi: bioRxiv preprint first posted online Dec. 15, 2016; 1 Introduction Learning from reinforcement is central to adaptive behavior and requires continuous and dynamic integration of sensory, motor, and reward information over time. Major progress has been made in understanding how individual brain regions support reinforcement learning. However, remarkably little is known about how these brain regions interact during learning, how their interactions change over time, and how these dynamic circuit--level changes relate to successful learning. In a typical reinforcement learning task, participants use reinforcement over hundreds of trials to associate cues or actions with their most probable outcome (e.g. (1--4)). Computationally, this is captured by so--called "model--free" reinforcement learning algorithms, a class of models that provide a quantitative and mechanistic framework for describing behavior (1, 5, 6). These models have also been successful in accounting for neuronal signals underlying learning b...