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Some individuals are endowed with a biology that renders them more reactive to novelty and potential threat. When extreme, this anxious temperament (AT) confers elevated risk for the development of anxiety, depression and substance abuse. These disorders are highly prevalent, debilitating and can be challenging to treat. The high-risk AT phenotype is expressed similarly in children and young monkeys and mechanistic work demonstrates that the central (Ce) nucleus of the amygdala is an important substrate. Although it is widely believed that the flow of information across the structural network connecting the Ce nucleus to other brain regions underlies primates' capacity for flexibly regulating anxiety, the functional architecture of this network has remained poorly understood. Here we used functional magnetic resonance imaging (fMRI) in anesthetized young monkeys and quietly resting children with anxiety disorders to identify an evolutionarily conserved pattern of functional connectivity relevant to early-life anxiety. Across primate species and levels of awareness, reduced functional connectivity between the dorsolateral prefrontal cortex, a region thought to play a central role in the control of cognition and emotion, and the Ce nucleus was associated with increased anxiety assessed outside the scanner. Importantly, high-resolution 18-fluorodeoxyglucose positron emission tomography imaging provided evidence that elevated Ce nucleus metabolism statistically mediates the association between prefrontal-amygdalar connectivity and elevated anxiety. These results provide new clues about the brain network underlying extreme early-life anxiety and set the stage for mechanistic work aimed at developing improved interventions for pediatric anxiety.

Racism is an insidious problem with far-reaching effects on the lives of Black, Indigenous, and People of Color (BIPOC). The pervasive negative impact of racism on mental health is well documented. However, less is known about the potential downstream impacts of maternal experiences of racism on offspring neurodevelopment. This study sought to examine evidence for a biological pathway of intergenerational transmission of racism-related trauma. This study examined the effects of self-reported maternal experiences of racism on resting state functional connectivity (rsFC) in n = 25 neonates (13 female, 12 male) birthed by BIPOC mothers. Amygdala and hippocampus are brain regions involved in fear, memory, and anxiety, and are central nodes in brain networks associated with trauma-related change. We used average scores on the Experiences of Racism Scale as a continuous, voxel-wise regressor in seed-based, whole-brain connectivity analysis of anatomically defined amygdala and hippocampus seed regions of interest. All analyses controlled for infant sex and gestational age at the 2-week scanning session. More maternal racism-related experiences were associated with (1) stronger right amygdala rsFC with visual cortex and thalamus; and (2) stronger hippocampus rsFC with visual cortex and a temporo-parietal network, in neonates. The results of this research have implications for understanding how maternal experiences of racism may alter neurodevelopment, and for related social policy., (© 2024. The Author(s).)

Abnormalities within the hypothalamic-pituitary-adrenal (HPA) axis and autonomic nervous system have been implicated in depression. Studies have reported glucocorticoid insensitivity and reduced heart rate variability (HRV) in depressive disorders. However, little is known about the effects of cortisol on HRV and resting-state functional connectivity (rsFC) of the central autonomic network (CAN) in depression. We collected resting-state fMRI and cardiac data for women with different depression histories (n = 61) after administration of cortisol and placebo using a double-blind crossover design. We computed rsFC for R-amygdala and L-amygdala seeds and assessed the change in HRV after cortisol (cortisol-placebo). Analyses examined the effects of acute cortisol administration on HRV and rsFC of the R-amygdala and L-amygdala. There was a significant interaction between HRV and treatment for rsFC between the amygdala and CAN regions. We found lower rsFC between the L-amygdala and putamen for those with a greater decrease in HRV after cortisol. There was also reduced rsFC between the R-amygdala and dorsomedial prefrontal cortex, putamen, middle cingulate cortex, insula, and cerebellum in those with lower HRV after cortisol. These results remained significant after adjusting for depression symptoms, age, and race. Our findings suggest that the effect of cortisol on CAN connectivity is related to its effects on HRV. Overall, these results could inform transdiagnostic interventions targeting HRV and the stress response systems across clinical and non-clinical populations., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Previous resting-state functional connectivity (rsFC) research has identified several brain networks impacted by depression and cortisol, including default mode (DMN), frontoparietal (FPN), and salience networks (SN). In the present study, we examined the effects of cortisol administration on rsFC of these networks in individuals varying in depression history and severity. We collected resting-state fMRI scans and self-reported depression symptom severity for 74 women with and without a history of depression after cortisol and placebo administration using a double-blind, crossover design. We conducted seed-based rsFC analyses for DMN, FPN, and SN seeds to examine rsFC changes after cortisol vs. placebo administration in relation to depression history group and severity. Results revealed a main effect of depression group, with lower left amygdala (SN)-middle temporal gyrus connectivity in women with a history of depression. Cortisol administration increased insula (SN)-inferior frontal gyrus and superior temporal gyrus connectivity. We also found that greater depression severity was associated with increased PCC (DMN)-cerebellum connectivity after cortisol. These results did not survive Bonferroni correction for seed ROIs and should be interpreted with caution. Our findings indicate that acute cortisol elevation may normalize aberrant connectivity of DMN and SN regions, which could help inform clinical treatments for depression., Competing Interests: Declaration of Competing Interest Dr. Heather Abercrombie is the owner of Prana Mental Health LLC, a psychotherapy private practice. All other authors report no biomedical financial interests or potential conflicts of interest., (Copyright © 2023 Elsevier B.V. All rights reserved.)

Short-range functional connectivity in the limbic network is increased in patients with temporal lobe epilepsy (TLE), and recent studies have shown that cortical myelin content correlates with fMRI connectivity. We thus hypothesized that myelin may increase progressively in the epileptic network. We compared T1w/T2w gray matter myelin maps between TLE patients and age-matched controls and assessed relationships between myelin and aging. While both TLE patients and healthy controls exhibited increased T1w/T2w intensity with age, we found no evidence for significant group-level aberrations in overall myelin content or myelin changes through time in TLE., (© 2022 The Authors. Annals of Clinical and Translational Neurology published by Wiley Periodicals LLC on behalf of American Neurological Association.)

Background: Vagus nerve stimulation (VNS) is a FDA approved therapy regularly used to treat a variety of neurological disorders that impact the central nervous system (CNS) including epilepsy and stroke. Putatively, the therapeutic efficacy of VNS results from its action on neuromodulatory centers via projections of the vagus nerve to the solitary tract nucleus. Currently, there is not an established large animal model that facilitates detailed mechanistic studies exploring how VNS impacts the function of the CNS, especially during complex behaviors requiring motor action and decision making., Methods: We describe the anatomical organization, surgical methodology to implant VNS electrodes on the left gagus nerve and characterization of target engagement/neural interface properties in a non-human primate (NHP) model of VNS that permits chronic stimulation over long periods of time. Furthermore, we describe the results of pilot experiments in a small number of NHPs to demonstrate how this preparation might be used in an animal model capable of performing complex motor and decision making tasks., Results: VNS electrode impedance remained constant over months suggesting a stable interface. VNS elicited robust activation of the vagus nerve which resulted in decreases of respiration rate and/or partial pressure of carbon dioxide in expired air, but not changes in heart rate in both awake and anesthetized NHPs., Conclusions: We anticipate that this preparation will be very useful to study the mechanisms underlying the effects of VNS for the treatment of conditions such as epilepsy and depression, for which VNS is extensively used, as well as for the study of the neurobiological basis underlying higher order functions such as learning and memory., (© 2023. The Author(s).)

The monitoring and assessment of data quality is an essential step in the acquisition and analysis of functional MRI (fMRI) data. Ideally data quality monitoring is performed while the data are being acquired and the subject is still in the MRI scanner so that any errors can be caught early and addressed. It is also important to perform data quality assessments at multiple points in the processing pipeline. This is particularly true when analyzing datasets with large numbers of subjects, coming from multiple investigators and/or institutions. These quality control procedures should monitor not only the quality of the original and processed data, but also the accuracy and consistency of acquisition parameters. Between-site differences in acquisition parameters can guide the choice of certain processing steps (e.g., resampling from oblique orientations, spatial smoothing). Various quality control metrics can determine what subjects to exclude from the group analyses, and can also guide additional processing steps that may be necessary. This paper describes a combination of qualitative and quantitative assessments to determine the quality of fMRI data. Processing is performed using the AFNI data analysis package. Qualitative assessments include visual inspection of the structural T1-weighted and fMRI echo-planar images, functional connectivity maps, functional connectivity strength, and temporal signal-to-noise maps concatenated from all subjects into a movie format. Quantitative metrics include the acquisition parameters, statistics about the level of subject motion, temporal signal-to-noise ratio, smoothness of the data, and the average functional connectivity strength. These measures are evaluated at different steps in the processing pipeline to catch gross abnormalities in the data, and to determine deviations in acquisition parameters, the alignment to template space, the level of head motion, and other sources of noise. We also evaluate the effect of different quantitative QC cutoffs, specifically the motion censoring threshold, and the impact of bandpass filtering. These qualitative and quantitative metrics can then provide information about what subjects to exclude and what subjects to examine more closely in the analysis of large datasets., Competing Interests: RB is a consultant for NOUS, Inc. The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Birn.)

Rumination is a common feature of depression and predicts the onset and maintenance of depressive episodes. Maladaptive and adaptive subtypes of rumination contribute to distinct outcomes, with brooding worsening negative mood and reflection related to fewer depression symptoms in healthy populations. Neuroimaging studies have implicated several cortical midline and lateral prefrontal brain regions in rumination. Recent research indicates that blood oxygen level-dependent (BOLD) signal variability may be a novel predictor of cognitive flexibility. However, no prior studies have investigated whether brooding and reflection are associated with distinct patterns of BOLD signal variability in depression. We collected resting-state fMRI data for 79 women with different depression histories: no history, past history, and current depression. We examined differences in BOLD signal variability (BOLD SD ) related to rumination subtypes for the following regions of interest previously implicated in rumination: amygdala, medial prefrontal, anterior cingulate, posterior cingulate, and dorsolateral prefrontal cortices (dlPFC). Rumination subtype was associated with BOLD SD in the dlPFC, with greater levels of brooding associated with lower BOLD SD in the dlPFC, even after controlling for depression severity. Depression history was related to BOLD SD in the dlPFC, with reduced BOLD SD in those with current depression versus no history of depression. These findings provide a novel demonstration of the neural circuitry associated with maladaptive rumination in depression and implicate decreased prefrontal neural signal variability in the pathophysiology of depression., Competing Interests: Declaration of competing interest The authors report no biomedical financial interests or potential conflicts of interest., (Copyright © 2022 Elsevier Ltd. All rights reserved.)

Introduction: Subject head motion is an ongoing challenge in functional magnetic resonance imaging, particularly in the estimation of functional connectivity. Infants (1-month old) scanned during nonsedated sleep often have occasional but large movements of several millimeters separated by periods with relatively little movement. This results in residual signal changes even after image realignment and can distort estimates of functional connectivity. A new motion correction technique, JumpCor, is introduced to reduce the effects of this motion and compared to other existing techniques. Methods: Different approaches for reducing residual motion artifacts after image realignment were compared both in actual and simulated data: JumpCor, regressing out the estimated subject motion, and regressing out the average white matter, cerebrospinal fluid (CSF), and global signals and their temporal derivatives. Results: Motion-related signal changes resulting from infrequent large motion were significantly reduced both by regressing out the estimated motion parameters and by JumpCor. Furthermore, JumpCor significantly reduced artifacts and improved the quality of functional connectivity estimates when combined with typical preprocessing approaches. Discussion: Motion-related signal changes resulting from occasional large motion can be effectively corrected using JumpCor and to a certain extent also by regressing out the estimated motion. This technique should reduce the data loss in studies where participants exhibit this type of motion, such as sleeping infants.

Aberrant activity and connectivity in default mode (DMN), frontoparietal (FPN), and salience (SN) network regions is well-documented in depression. Recent neuroimaging research suggests that altered variability in the blood oxygen level-dependent (BOLD) signal may disrupt normal network integration and be an important novel predictor of psychopathology. However, no studies have yet determined the relationship between resting-state BOLD signal variability and depressive disorders nor applied BOLD signal variability features to the classification of depression history using machine learning (ML). We collected resting-state fMRI data for 79 women with different depression histories: no history, past history, and current depressive disorder. We tested voxelwise differences in BOLD signal variability related to depression group and severity. We also investigated whether BOLD signal variability of DMN, FPN, and SN regions could predict depression history group using a supervised random forest ML model. Results indicated that individuals with any history of depression had significantly decreased BOLD signal variability in the left and right cerebellum and right parietal cortex (p FWE <0.05). Furthermore, greater depression severity was also associated with reduced BOLD signal variability in the cerebellum. A random forest model classified participant depression history with 74% accuracy, with the ventral anterior cingulate cortex of the DMN as the most important variable in the model. These findings provide novel support for resting-state BOLD signal variability as a marker of neural dysfunction in depression and implicate decreased neural signal variability in the pathophysiology of depression., (Copyright © 2022 Elsevier B.V. All rights reserved.)

Background: Depression is associated with altered functional connectivity and altered cortisol sensitivity, but the effects of cortisol on functional connectivity in depression are unknown. Previous research shows that brief cortisol augmentation (CORT) has beneficial neurocognitive effects in depression., Methods: We investigated the effects of CORT (20mg oral cortisol) on functional connectivity during emotion processing in women with depression. Participants included 75 women with no depression or a depressive disorder. In a double-blind, crossover study, we used functional magnetic resonance imaging to measure effects of CORT vs. placebo on task-based functional connectivity during presentation of emotionally-laden images. We performed psychophysiological interaction (PPI) to test interactions among depression severity, cortisol administration, and task-dependent functional connectivity using the hippocampus and amygdala as seeds., Results: During the presentation of negative images, CORT (vs. placebo) increased functional connectivity between the hippocampus and putamen in association with depression severity. During the presentation of positive pictures CORT increased functional connectivity between the hippocampus and middle frontal gyrus as well as superior temporal gyrus in association with depression., Limitations: Because cortisol was pharmacologically manipulated, results cannot be extrapolated to endogenous increases in cortisol levels. The sample did not permit investigation of differences due to race, ethnicity, or sex. Co-morbidities such as anxiety or PTSD were not accounted for., Conclusions: The results suggest that CORT has normalizing effects on task-dependent functional connectivity in women with depression during emotion processing. Increasing cortisol availability or signaling may have therapeutic benefits within affective disorders., (Copyright © 2021. Published by Elsevier B.V.)

Posttraumatic stress disorder (PTSD) is widely associated with deficits in implicit emotion regulation. Recently, adaptive fMRI neurofeedback (A-NF) has been developed as a methodology that offers a unique probe of brain networks that mediate implicit emotion regulation and their impairment in PTSD. We designed an A-NF paradigm in which difficulty of an emotional conflict task (i.e., embedding trauma distractors onto a neutral target stimulus) was controlled by a whole-brain classifier trained to differentiate attention to the trauma distractor vs. target. We exploited this methodology to test whether PTSD was associated with: (1) an altered brain state that differentiates attention towards vs. away from trauma cues; and (2) an altered ability to use concurrent feedback about brain states during an implicit emotion regulation task. Adult women with a current diagnosis of PTSD ( n = 10) and healthy control ( n = 9) women participated in this task during 3T fMRI. During two initial non-feedback runs used to train a whole-brain classifier, we observed: (1) poorer attention performance in PTSD; and (2) a linear relationship between brain state discrimination and attention performance, which was significantly attenuated among the PTSD group when the task contained trauma cues. During the A-NF phase, the PTSD group demonstrated poorer ability to regulate brain states as per attention instructions, and this poorer ability was related to PTSD symptom severity. Further, PTSD was associated with the heightened encoding of feedback in the insula and hippocampus. These results suggest a novel understanding of whole-brain states and their regulation that underlie emotion regulation deficits in PTSD., (Copyright © 2020 Weaver, Birn and Cisler.)

Mindfulness training has been shown to improve attention and change the underlying brain substrates in adults. Most mindfulness training programs involve a myriad of techniques, and it is difficult to attribute changes to any particular aspect of the program. Here, we created a video game, Tenacity, which models a specific mindfulness technique - focused attention on one's breathing - and assessed its potential to train an attentional network in adolescents. A combined analysis of resting state functional connectivity (rs-FC) and diffusion tensor imaging (DTI) yielded convergent results - change in communication within the left fronto-parietal network after two weeks of playing Tenacity compared to a control game. Rs-FC analysis showed greater connectivity between left dorsolateral prefrontal cortex (dlPFC) and left inferior parietal cortex (IPC) in the Tenacity group. Importantly, changes in left dlPFC - IPC rs-FC and changes in structural connectivity of the white matter tract that connects these regions -left superior longitudinal fasiculus (SLF) - were associated with changes in performance on an attention task. Finally, changes in left dlPFC - IPC rs-FC correlated with the change in left SLF structural connectivity as measured by fractional anisotropy (FA) in the Tenacity group only.

Dopamine (DA) levels in the striatum are increased by many therapeutic drugs, such as methylphenidate (MPH), which also alters behavioral and cognitive functions thought to be controlled by the PFC dose-dependently. We linked DA changes and functional connectivity (FC) using simultaneous [ 18 F]fallypride PET and resting-state fMRI in awake male rhesus monkeys after oral administration of various doses of MPH. We found a negative correlation between [ 18 F]fallypride nondisplaceable binding potential (BP ND ) and MPH dose in the head of the caudate (hCd), demonstrating increased extracellular DA resulting from MPH administration. The decreased BP ND was negatively correlated with FC between the hCd and the PFC. Subsequent voxelwise analyses revealed negative correlations with FC between the hCd and the dorsolateral PFC, hippocampus, and precuneus. These results, showing that MPH-induced changes in DA levels in the hCd predict resting-state FC, shed light on a mechanism by which changes in striatal DA could influence function in the PFC. SIGNIFICANCE STATEMENT Dopamine transmission is thought to play an essential role in shaping large scale-neural networks that underlie cognitive functions. It is the target of therapeutic drugs, such as methylphenidate (Ritalin), which blocks the dopamine transporter, thereby increasing extracellular dopamine levels. Methylphenidate is used extensively to treat attention deficit hyperactivity disorder, even though its effects on cognitive functions and their underlying neural mechanisms are not well understood. To date, little is known about the link between changes in dopamine levels and changes in functional brain organization. Using simultaneous PET/MR imaging, we show that methylphenidate-induced changes in endogenous dopamine levels in the head of the caudate predict changes in resting-state functional connectivity between this structure and the prefrontal cortex, precuneus, and hippocampus., (Copyright © 2019 the authors 0270-6474/19/391437-09$15.00/0.)

Importance: Maternal depression and anxiety can have deleterious and lifelong consequences on child development. However, many aspects of the association of early brain development with maternal symptoms remain unclear. Understanding the timing of potential neurobiological alterations holds inherent value for the development and evaluation of future therapies and interventions., Objective: To examine the association between exposure to prenatal maternal depression and anxiety symptoms and offspring white matter microstructure at 1 month of age., Design, Setting, and Participants: This cohort study of 101 mother-infant dyads used a composite of depression and anxiety symptoms measured in mothers during the third trimester of pregnancy and measures of white matter microstructure characterized in the mothers' 1-month offspring using diffusion tensor imaging and neurite orientation dispersion and density imaging performed from October 1, 2014, to November 30, 2016. Magnetic resonance imaging was performed at an academic research facility during natural, nonsedated sleep., Main Outcomes and Measures: Brain mapping algorithms and statistical models were used to evaluate the association between maternal depression and anxiety and 1-month infant white matter microstructure as measured by diffusion tensor imaging and neurite orientation dispersion and density imaging findings., Results: In the 101 mother-infant dyads (mean [SD] age of mothers, 33.22 [3.99] years; mean age of infants at magnetic resonance imaging, 33.07 days [range, 18-50 days]; 92 white mothers [91.1%]; 53 male infants [52.5%]), lower 1-month white matter microstructure (decreased neurite density and increased mean, radial, and axial diffusivity) was associated in right frontal white matter microstructure with higher prenatal maternal symptoms of depression and anxiety. Significant sex × symptom interactions with measures of white matter microstructure were also observed, suggesting that white matter development may be differentially sensitive to maternal depression and anxiety symptoms in males and females during the prenatal period., Conclusions and Relevance: These data highlight the importance of the prenatal period to early brain development and suggest that the underlying white matter microstructure is associated with the continuum of prenatal maternal depression and anxiety symptoms.

Children with an extremely inhibited, anxious temperament (AT) are at increased risk for anxiety disorders and depression. Using a rhesus monkey model of early-life AT, we previously demonstrated that metabolism in the central extended amygdala (EAc), including the central nucleus of the amygdala (Ce) and bed nucleus of the stria terminalis (BST), is associated with trait-like variation in AT. Here, we use fMRI to examine relationships between Ce-BST functional connectivity and AT in a large multigenerational family pedigree of rhesus monkeys ( n = 170 females and 208 males). Results demonstrate that Ce-BST functional connectivity is heritable, accounts for a significant but modest portion of the variance in AT, and is coheritable with AT. Interestingly, Ce-BST functional connectivity and AT-related BST metabolism were not correlated and accounted for non-overlapping variance in AT. Exploratory analyses suggest that Ce-BST functional connectivity is associated with metabolism in the hypothalamus and periaqueductal gray. Together, these results suggest the importance of coordinated function within the EAc for determining individual differences in AT and metabolism in brain regions associated with its behavioral and neuroendocrine components. SIGNIFICANCE STATEMENT Anxiety disorders directly impact the lives of nearly one in five people, accounting for substantial worldwide suffering and disability. Here, we use a nonhuman primate model of anxious temperament (AT) to understand the neurobiology underlying the early-life risk to develop anxiety disorders. Leveraging the same kinds of neuroimaging measures routinely used in human studies, we demonstrate that coordinated activation between the central nucleus of the amygdala and the bed nucleus of the stria terminalis is correlated with, and coinherited with, early-life AT. Understanding how these central extended amygdala regions work together to produce extreme anxiety provides a neural target for early-life interventions with the promise of preventing lifelong disability in at-risk children., (Copyright © 2018 the authors 0270-6474/18/387611-11$15.00/0.)

The ability to understand emotional experiences of others, empathy, is a valuable skill for effective social interactions. Various types of training increase empathy in adolescents, but their impact on brain circuits underlying empathy has not been examined. Video games provide a unique medium familiar and engaging to adolescents and can be used to deliver training at scale. We developed an empathy training video game, Crystals of Kaydor (Crystals), and investigated whether playing Crystals increases empathic accuracy (EA) and related brain activation in adolescents ( N  = 74; 27 female; mean age(sd) = 12.8(0.7) years; age range 11-14 years). Participants completed a resting state functional MRI (rs-fMRI) scan and an EA task during an fMRI scan before and after 2 weeks of daily gameplay with either the empathy training game, Crystals ( N  = 34), or the commercial video game Bastion ( N  = 40), an active control condition. There were no group differences in EA improvement following gameplay, however, engagement with training aspects of Crystals was associated with a higher increase in EA-related activation in right temporoparietal junction following gameplay. Moreover, rs-fMRI connectivity in empathy-related brain circuits (posterior cingulate-medial prefrontal cortex; MPFC) was stronger after Crystals gameplay compared to Bastion. The more individuals' EA increased following Crystals versus Bastion, the stronger their rs-fMRI connectivity in brain circuits relevant for emotion regulation (amygdala-MPFC). These findings suggest that a video game designed to increase empathic accuracy produces behaviorally-relevant, functional neural changes in fewer than 6 h of gameplay in adolescents., Competing Interests: Dr. Richard J. Davidson is the founder, president, and serves on the board of directors for the non-profit organization, Healthy Minds Innovations, Inc. No donors, either anonymous or identified, have participated in the design, conduct, or reporting of research results in this manuscript. The remaining authors declare no competing interests.

Brain extraction or skull stripping of magnetic resonance images (MRI) is an essential step in neuroimaging studies, the accuracy of which can severely affect subsequent image processing procedures. Current automatic brain extraction methods demonstrate good results on human brains, but are often far from satisfactory on nonhuman primates, which are a necessary part of neuroscience research. To overcome the challenges of brain extraction in nonhuman primates, we propose a fully-automated brain extraction pipeline combining deep Bayesian convolutional neural network (CNN) and fully connected three-dimensional (3D) conditional random field (CRF). The deep Bayesian CNN, Bayesian SegNet, is used as the core segmentation engine. As a probabilistic network, it is not only able to perform accurate high-resolution pixel-wise brain segmentation, but also capable of measuring the model uncertainty by Monte Carlo sampling with dropout in the testing stage. Then, fully connected 3D CRF is used to refine the probability result from Bayesian SegNet in the whole 3D context of the brain volume. The proposed method was evaluated with a manually brain-extracted dataset comprising T1w images of 100 nonhuman primates. Our method outperforms six popular publicly available brain extraction packages and three well-established deep learning based methods with a mean Dice coefficient of 0.985 and a mean average symmetric surface distance of 0.220 mm. A better performance against all the compared methods was verified by statistical tests (all p-values < 10 -4 , two-sided, Bonferroni corrected). The maximum uncertainty of the model on nonhuman primate brain extraction has a mean value of 0.116 across all the 100 subjects. The behavior of the uncertainty was also studied, which shows the uncertainty increases as the training set size decreases, the number of inconsistent labels in the training set increases, or the inconsistency between the training set and the testing set increases., (Copyright © 2018 Elsevier Inc. All rights reserved.)

Acute mood disturbance following sport-related concussion is common and is known to adversely affect post-concussion symptoms and recovery. The physiological underpinnings of depressive symptoms following concussion, however, are relatively understudied. We hypothesized that functional connectivity of the emotional processing network would be altered in concussed athletes and associated with the severity of depressive symptoms following concussion. Forty-three concussed collegiate athletes were assessed at approximately one day ( N  = 34), one week ( N  = 34), and one month post-concussion ( N  = 30). Fifty-one healthy contact-sport athletes served as controls and completed a single visit. The Hamilton Rating Scale for Depression (HAM-D) was used to measure depressive symptoms. Resting state fMRI data was collected on a 3 T scanner (TR = 2 s) and functional connectivity was calculated in a meta-analytically derived network of regions associated with emotional processing. Concussed athletes had elevated depressive symptoms across the first month post-concussion relative to control athletes, but showed partial recovery by one month relative to more acute visits ( p s < 0.05). Concussed athletes had significantly different connectivity in regions associated with emotional processing at one month post-concussion relative to one day post-concussion ( p  = 0.002) and relative to controls ( p  = 0.003), with higher connectivity between default mode and attention regions being common across analyses. Additionally, depressive symptoms in concussed athletes at one day ( p  = 0.003) and one week post-concussion ( p  = 7 × 10 -8 ) were inversely correlated with connectivity between attention (e.g., right anterior insula) and default mode regions (e.g., medial prefrontal cortex). Finally, the relationships with HAM-D scores were not driven by a general increase in somatic complaints captured by the HAM-D, but were strongly associated with mood-specific HAM-D items. These results suggest that connectivity of emotional processing regions is associated with acute mood disturbance following sport-related concussion. Increased connectivity between attention and default mode regions may reflect compensatory mechanisms.

Adverse caregiving during development can produce long-lasting changes to neural, endocrine, and behavioral responses to stress, and is strongly related to elevated risk of adult psychopathology. While prior experience of adversity is associated with altered sympathetic nervous system (SNS) and hypothalamic-pituitary-adrenal (HPA) axis activity, the underlying neural pathways are not completely understood. In a double-blind crossover study, we used diffusion tensor imaging (DTI) to examine whether variation in white matter structure predicts differences in HPA-SNS interactions as a function of early adversity. Participants included 74 women who exhibited a wide range of depression severity and/or childhood emotional abuse (EA). Participants attended two experimental sessions during which they were administered 20 mg cortisol (CORT) or placebo and after 90 min, viewed emotionally laden pictures while undergoing MRI scanning. Immediately after emotional picture-viewing, we collected salivary alpha-amylase (sAA) to index SNS activation. We tested whether EA moderated the relation between fractional anisotropy (FA), a measure of white matter fiber structure, and sAA. In the placebo condition, for participants with minimal history of EA, higher FA in corticomotor projections was negatively correlated with sAA, whereas in participants with severe EA, the correlation was trending in the opposite direction. Following CORT administration, FA and sAA were not related, suggesting that SNS tone during acute cortisol elevation may depend on neural pathways other than corticomotor projections. The results suggest that at baseline-though not during cortisol elevation-increased FA in these tracts is associated with lower levels of SNS activity in women with minimal EA, but not in women with severe EA. These findings provide evidence that corticomotor projections may be a key component of altered neural circuitry in adults with history of maltreatment, and may be related to alterations in stress neuromodulators in psychopathology.

Background: Cortisol has potent effects on learning and neuroplasticity, but little is known about its effects on negative memory biases in depression. Animal models show that aversive caregiving alters effects of glucocorticoids (primarily corticosterone in rodents and cortisol in primates) on learning and neuroplasticity into adulthood., Methods: We investigated whether history of childhood emotional abuse (EA) moderated effects of cortisol administration (CORT) versus placebo on emotional memory formation in depression. Participants included 75 unmedicated women with varying levels of depression severity and/or EA history. In a double-blind crossover investigation, we used functional magnetic resonance imaging to measure effects of CORT (vs. placebo) on neural function during emotional memory formation., Results: CORT eliminated the well-known relationship between depression severity and negative memory bias, a finding explained by EA severity. For women with a history of severe EA, CORT reduced depression-related negative memory bias and normalized recall for pleasant stimuli. EA severity also moderated CORT effects on neural function: in women with history of severe EA, CORT increased activation in the supplementary motor area during viewing of unpleasant relative to pleasant pictures. Additionally, supplementary motor area activation predicted reduced negative bias for pictures encoded during CORT., Conclusions: These results suggest that increasing cortisol signaling may be neurocognitively beneficial in depressed women with a history of maltreatment. The findings corroborate prior research suggesting that presence or absence of adverse caregiving is etiologically important in depression. These findings suggest potential neurocognitive mechanisms of therapeutics targeting cortisol signaling, which show promise in treating affective disorders., (Copyright © 2017 Society of Biological Psychiatry. Published by Elsevier Inc. All rights reserved.)

Background: Excessive daytime sleepiness (EDS) is a common and significant problem encountered in affective illness, however, the biological underpinnings of EDS in persons with psychiatric disorders are not clear. This study evaluated the associations between thalamic connectivity with cortical and subcortical brain regions with EDS in persons with and without depressive disorders (DD)., Methods: Resting-state functional connectivity magnetic resonance imaging scans from 67 unmedicated young to middle-aged women with current DD (n = 30), remitted DD (n = 13), and healthy controls (n = 24) were utilized to examine the associations between thalamic connectivity with cortical/subcortical structures and EDS., Results: After correction for multiple comparisons and adjustment for age, habitual sleep duration, and depressive symptomatology, reduced resting-state connectivity between the bilateral thalamus and left rostral striatum (caudate/putamen) was significantly associated with EDS., Limitations: Causal inferences between thalamostriatal connectivity and EDS could not be determined., Conclusions: These results further implicate the role of the striatum and thalamus as central components of the experience of EDS. Further research is indicated to clarify the specific role these structures play in EDS in psychiatric disorders., (Copyright © 2017 Elsevier B.V. All rights reserved.)

Individuals who have experienced chronic and high levels of stress during their childhoods are at increased risk for a wide range of behavioral problems, yet the neurobiological mechanisms underlying this association are poorly understood. We measured the life circumstances of a community sample of school-aged children and then followed these children for a decade. Those from the highest and lowest quintiles of childhood stress exposure were invited to return to our laboratory as young adults, at which time we reassessed their life circumstances, acquired fMRI data during a reward-processing task, and tested their judgment and decision making. Individuals who experienced high levels of early life stress showed lower levels of brain activation when processing cues signaling potential loss and increased responsivity when actually experiencing losses. Specifically, those with high childhood stress had reduced activation in the posterior cingulate/precuneus, middle temporal gyrus, and superior occipital cortex during the anticipation of potential rewards; reduced activation in putamen and insula during the anticipation of potential losses; and increased left inferior frontal gyrus activation when experiencing an actual loss. These patterns of brain activity were associated with both laboratory and real-world measures of individuals' risk taking in adulthood. Importantly, these effects were predicated only by childhood stress exposure and not by current levels of life stress., Competing Interests: The authors declare no conflict of interest., (Copyright © 2017 the Author(s). Published by PNAS.)

Head motion is a significant source of noise in the estimation of functional connectivity from resting-state functional MRI (rs-fMRI). Current strategies to reduce this noise include image realignment, censoring time points corrupted by motion, and including motion realignment parameters and their derivatives as additional nuisance regressors in the general linear model. However, this nuisance regression approach assumes that the motion-induced signal changes are linearly related to the estimated realignment parameters, which is not always the case. In this study we develop an improved model of motion-related signal changes, where nuisance regressors are formed by first rotating and translating a single brain volume according to the estimated motion, re-registering the data, and then performing a principal components analysis (PCA) on the resultant time series of both moved and re-registered data. We show that these "Motion Simulated (MotSim)" regressors account for significantly greater fraction of variance, result in higher temporal signal-to-noise, and lead to functional connectivity estimates that are less affected by motion compared to the most common current approach of using the realignment parameters and their derivatives as nuisance regressors. This improvement should lead to more accurate estimates of functional connectivity, particularly in populations where motion is prevalent, such as patients and young children., (Copyright © 2016. Published by Elsevier Inc.)

The lateral division of the bed nucleus of the stria terminalis (BSTL) and central nucleus of the amygdala (Ce) form the two poles of the 'central extended amygdala', a theorized subcortical macrostructure important in threat-related processing. Our previous work in nonhuman primates, and humans, demonstrating strong resting fMRI connectivity between the Ce and BSTL regions, provides evidence for the integrated activity of these structures. To further understand the anatomical substrates that underlie this coordinated function, and to investigate the integrity of the central extended amygdala early in life, we examined the intrinsic connectivity between the Ce and BSTL in non-human primates using ex vivo neuronal tract tracing, and in vivo diffusion-weighted imaging and resting fMRI techniques. The tracing studies revealed that BSTL receives strong input from Ce; however, the reciprocal pathway is less robust, implying that the primate Ce is a major modulator of BSTL function. The sublenticular extended amygdala (SLEAc) is strongly and reciprocally connected to both Ce and BSTL, potentially allowing the SLEAc to modulate information flow between the two structures. Longitudinal early-life structural imaging in a separate cohort of monkeys revealed that extended amygdala white matter pathways are in place as early as 3 weeks of age. Interestingly, resting functional connectivity between Ce and BSTL regions increases in coherence from 3 to 7 weeks of age. Taken together, these findings demonstrate a time period during which information flow between Ce and BSTL undergoes postnatal developmental changes likely via direct Ce → BSTL and/or Ce ↔ SLEAc ↔ BSTL projections., Competing Interests: Dr. Kalin has received honoraria from CME Outfitters, Elsevier, and the Pritzker Neuropsychiatric Disorders Research Consortium. He is on the Advisory Boards for Corcept Therapeutics and Skyland Trail - George West Mental Health Foundation. Dr. Kalin is a Stockholder in Corcept Therapeutics, and owns several patents including: promoter sequences for corticotropin-releasing factor alpha (U.S. Patent #7071323, issued on 07-04-06); a method of identifying agents that alter the activity of the promoter sequences (U.S. Patent #7531356 issued on 05-12-09); promoter sequences for urocortin II and the use thereof (U.S. Patent #7087385 issued on 08-08-06); and promoter sequences for corticotropin-releasing factor binding protein and use thereof (U.S. Patent #7122650, issued on 10-17-06). All other authors declare no conflicts of interest.

Stress and emotion involve diverse developmental and individual differences. Partially attributed to the development of the prefrontal cortex (PFC), the amygdala, and hypothalamic-pituitary-adrenal axis, the precise genetic and experiential contributions remain unknown. In previous work, childhood basal cortisol function predicted adolescent resting-state functional connectivity (rs-FC) and psychopathology. To parse experience-driven (non-genetic) contributions, we investigated these relations with a monozygotic (MZ) twin design. Specifically, we examined whether intrapair differences in childhood afternoon cortisol levels predicted cotwin differences in adolescent brain function and coping. As expected, intrapair differences in childhood cortisol forecast amygdala-perigenual PFC rs-FC (R 2  = 0.84, FWE-corrected p = 0.01), and amygdala recovery following unpleasant images (R 2  = 0.40, FWE-corrected p < 0.05), such that the cotwin with higher childhood cortisol evinced relatively lower rs-FC and poorer amygdala recovery in adolescence. Cotwin differences in amygdala recovery also predicted coping styles. These data highlight experience-dependent change in childhood and adolescence.

Functional magnetic resonance imaging studies have significantly expanded the field's understanding of functional brain activity of healthy and patient populations. Resting state (rs-) fMRI, which does not require subjects to perform a task, eliminating confounds of task difficulty, allows examination of neural activity and offers valuable functional mapping information. The purpose of this work was to develop an automatic resting state network (RSN) labeling method which offers value in clinical workflow during rs-fMRI mapping by organizing and quickly labeling spatial maps into functional networks. Here independent component analysis (ICA) and machine learning were applied to rs-fMRI data with the goal of developing a method for the clinically oriented task of extracting and classifying spatial maps into auditory, visual, default-mode, sensorimotor, and executive control RSNs from 23 epilepsy patients (and for general comparison, separately for 30 healthy subjects). ICA revealed distinct and consistent functional network components across patients and healthy subjects. Network classification was successful, achieving 88% accuracy for epilepsy patients with a naïve Bayes algorithm (and 90% accuracy for healthy subjects with a perceptron). The method's utility to researchers and clinicians is the provided RSN spatial maps and their functional labeling which offer complementary functional information to clinicians' expert interpretation.

The processes of normal aging and aging-related pathologies subject the brain to an active re-organization of its brain networks. Among these, the default-mode network (DMN) is consistently implicated with a demonstrated reduction in functional connectivity within the network. However, no clear stipulation on the underlying mechanisms of the de-synchronization has yet been provided. In this study, we examined the spectral distribution of the intrinsic low-frequency oscillations (LFOs) of the DMN sub-networks in populations of young normals, older subjects, and acute and subacute ischemic stroke patients. The DMN sub-networks were derived using a mid-order group independent component analysis with 117 eyes-closed resting-state functional magnetic resonance imaging (rs-fMRI) sessions from volunteers in those population groups, isolating three robust components of the DMN among other resting-state networks. The posterior component of the DMN presented noticeable differences. Measures of amplitude of low-frequency fluctuation (ALFF) and fractional ALFF (fALFF) of the network component demonstrated a decrease in resting-state cortical oscillation power in the elderly (normal and patient), specifically in the slow-5 (0.01-0.027 Hz) range of oscillations. Furthermore, the contribution of the slow-5 oscillations during the resting state was diminished for a greater influence of the slow-4 (0.027-0.073 Hz) oscillations in the subacute stroke group, not only suggesting a vulnerability of the slow-5 oscillations to disruption but also indicating a change in the distribution of the oscillations within the resting-state frequencies. The reduction of network slow-5 fALFF in the posterior DMN component was found to present a potential association with behavioral measures, suggesting a brain-behavior relationship to those oscillations, with this change in behavior potentially resulting from an altered network integrity induced by a weakening of the slow-5 oscillations during the resting state. The repeated identification of those frequencies in the disruption of DMN stresses a critical role of the slow-5 oscillations in network disruption, and it accentuates the importance of managing those oscillations in the health of the DMN.

Objective: Resting-state functional magnetic resonance imaging (rs-fMRI) studies of adult posttraumatic stress disorder (PTSD) have identified default-mode network (DMN) abnormalities, including reduced within-network connectivity and reduced anticorrelation between the DMN and task-positive network (TPN). However, no prior studies have specifically examined DMN connectivity in pediatric PTSD, which may differ due to neurodevelopmental factors., Method: A total of 29 youth with PTSD and 30 nontraumatized healthy youth of comparable age and sex completed rs-fMRI. DMN properties were examined using posterior cingulate cortex (PCC) seed-based connectivity and independent component analysis (ICA)., Results: Contrary to findings in adult studies, youth with PTSD displayed increased connectivity within the DMN, including increased PCC-inferior parietal gyrus connectivity, and age-related increases in PCC-ventromedial prefrontal cortex connectivity. Strikingly, youth with PTSD also displayed greater anticorrelation between the PCC and multiple nodes within salience and attentional control networks of the TPN. ICA revealed greater anticorrelation between the entire DMN and TPN networks in youth with PTSD. Furthermore, DMN and TPN connectivity strength were positively and negatively associated, respectively, with re-experiencing symptoms of PTSD., Conclusion: Pediatric PTSD is characterized by heightened within-DMN connectivity, which may contribute to re-experiencing symptoms of PTSD and is consistent with the role of the DMN in autobiographical memory. At the same time, greater anticorrelation between the DMN and attentional control networks may represent compensatory mechanisms aimed at suppressing trauma-related thought, a notion supported by the inverse relationship between TPN strength and re-experiencing. These findings provide new insights into large-scale network abnormalities underlying pediatric PTSD, which could serve as biomarkers of illness and treatment response., (Copyright © 2016 American Academy of Child and Adolescent Psychiatry. Published by Elsevier Inc. All rights reserved.)

Objectives: Application of fMRI connectivity metrics as diagnostic biomarkers at the individual level will require reliability, sensitivity and specificity to longitudinal changes in development, aging, neurocognitive, and behavioral performance and pathologies. Such metrics have not been well characterized for recent advances in BOLD acquisition., Experimental Design: Analysis of multiband BOLD data from the HCP 500 Subjects Release was performed with FIX ICA and with WM, CSF and motion parameter regression. Analysis with ROIs covering the gray matter at 5 mm resolution was performed to assess functional connectivity. ROIs in key areas were used to demonstrate statistical differences between specific connections. Reproducibility of group-mean functional connectivity and for single connections for individuals was evaluated for both resting state and task acquisitions., Principal Observations: Systematic differences in group-mean connectivity were demonstrated during task and rest and during different tasks, although individual differences in connectivity were maintained. Reproducibility of a single connection for a subject and across subjects for resting and task acquisition was demonstrated to be a linear function of the square root of imaging time. Randomly removing up to 50% of time points had little effect on reliability, while truncating an acquisition was associated with decreased reliability. Reliability was highest within the cortex, and lowest for deep gray nuclei, gray-white junction, and near large sulci., Conclusions: This study found systematic differences in group-mean connectivity acquired during task and rest acquitisions and preserved individual differences in connectivity due to intrinsic differences in an individual's brain activity and structural brain architecture. We also show that longer scan times are needed to acquire data on single subjects for information on connections between specific ROIs. Longer scans may be facilitated by acquisition during task paradigms, which will systematically affect functional connectivity but may preserve individual differences in connectivity on top of task modulations.

Recent fMRI studies have outlined the critical impact of in-scanner head motion, particularly on estimates of functional connectivity. Common strategies to reduce the influence of motion include realignment as well as the inclusion of nuisance regressors, such as the 6 realignment parameters, their first derivatives, time-shifted versions of the realignment parameters, and the squared parameters. However, these regressors have limited success at noise reduction. We hypothesized that using nuisance regressors consisting of the principal components (PCs) of edge voxel time series would be better able to capture slice-specific and nonlinear signal changes, thus explaining more variance, improving data quality (i.e., lower DVARS and temporal SNR), and reducing the effect of motion on default-mode network connectivity. Functional MRI data from 22 healthy adult subjects were preprocessed using typical motion regression approaches as well as nuisance regression derived from edge voxel time courses. Results were evaluated in the presence and absence of both global signal regression and motion censoring. Nuisance regressors derived from signal intensity time courses at the edge of the brain significantly improved motion correction compared to using only the realignment parameters and their derivatives. Of the models tested, only the edge voxel regression models were able to eliminate significant differences in default-mode network connectivity between high- and low-motion subjects regardless of the use of global signal regression or censoring.

Efforts to identify meaningful functional imaging-based biomarkers are limited by the ability to reliably characterize inter-individual differences in human brain function. Although a growing number of connectomics-based measures are reported to have moderate to high test-retest reliability, the variability in data acquisition, experimental designs, and analytic methods precludes the ability to generalize results. The Consortium for Reliability and Reproducibility (CoRR) is working to address this challenge and establish test-retest reliability as a minimum standard for methods development in functional connectomics. Specifically, CoRR has aggregated 1,629 typical individuals' resting state fMRI (rfMRI) data (5,093 rfMRI scans) from 18 international sites, and is openly sharing them via the International Data-sharing Neuroimaging Initiative (INDI). To allow researchers to generate various estimates of reliability and reproducibility, a variety of data acquisition procedures and experimental designs are included. Similarly, to enable users to assess the impact of commonly encountered artifacts (for example, motion) on characterizations of inter-individual variation, datasets of varying quality are included.

The human brain undergoes both morphological and functional modifications across the human lifespan. It is important to understand the aspects of brain reorganization that are critical in normal aging. To address this question, one approach is to investigate age-related topological changes of the brain. In this study, we developed a brain network model using graph theory methods applied to the resting-state functional magnetic resonance imaging data acquired from two groups of normal healthy adults classified by age. We found that brain functional networks demonstrated modular organization in both groups with modularity decreased with aging, suggesting less distinct functional divisions across whole brain networks. Local efficiency was also decreased with aging but not with global efficiency. Besides these brain-wide observations, we also observed consistent alterations of network properties at the regional level in the elderly, particularly in two major functional networks-the default mode network (DMN) and the sensorimotor network. Specifically, we found that measures of regional strength, local and global efficiency of functional connectivity were increased in the sensorimotor network while decreased in the DMN with aging. These results indicate that global reorganization of brain functional networks may reflect overall topological changes with aging and that aging likely alters individual brain networks differently depending on the functional properties. Moreover, these findings highly correspond to the observation of decline in cognitive functions but maintenance of primary information processing in normal healthy aging, implying an underlying compensation mechanism evolving with aging to support higher-level cognitive functioning.

Background: Adult posttraumatic stress disorder (PTSD) has been characterized by altered fear-network connectivity. Childhood trauma is a major risk factor for adult PTSD, yet its contribution to fear-network connectivity in PTSD remains unexplored. We examined, within a single model, the contribution of childhood maltreatment, combat exposure, and combat-related posttraumatic stress symptoms (PTSS) to resting-state connectivity (rs-FC) of the amygdala and hippocampus in military veterans., Methods: Medication-free male veterans (n = 27, average 26.6 years) with a range of PTSS completed resting-state fMRI. Measures including the Clinician-Administered PTSD Scale (CAPS), Childhood Trauma Questionnaire (CTQ), and Combat Exposure Scale (CES) were used to predict rs-FC using multilinear regression. Fear-network seeds included the amygdala and hippocampus., Results: Amygdala: CTQ predicted lower connectivity to ventromedial prefrontal cortex (vmPFC), but greater anticorrelation with dorsal/lateral PFC. CAPS positively predicted connectivity to insula, and loss of anticorrelation with dorsomedial/dorsolateral (dm/dl)PFC. Hippocampus: CTQ predicted lower connectivity to vmPFC, but greater anticorrelation with dm/dlPFC. CES predicted greater anticorrelation, whereas CAPS predicted less anticorrelation with dmPFC., Conclusions: Childhood trauma, combat exposure, and PTSS differentially predict fear-network rs-FC. Childhood maltreatment may weaken ventral prefrontal-subcortical circuitry important in automatic fear regulation, but, in a compensatory manner, may also strengthen dorsal prefrontal-subcortical pathways involved in more effortful emotion regulation. PTSD symptoms, in turn, appear to emerge with the loss of connectivity in the latter pathway. These findings suggest potential mechanisms by which developmental trauma exposure leads to adult PTSD, and which brain mechanisms are associated with the emergence of PTSD symptoms., (© 2014 Wiley Periodicals, Inc.)

The utility and success of resting-state functional connectivity MRI (rs-fcMRI) depend critically on the reliability of this technique and the extent to which it accurately reflects neuronal function. One challenge is that rs-fcMRI is influenced by various sources of noise, particularly cardiac- and respiratory-related signal variations. The goal of the current study was to evaluate the impact of various physiological noise correction techniques, specifically those that use independent cardiac and respiration measures, on the test-retest reliability of rs-fcMRI. A group of 25 subjects were each scanned at three time points--two within the same imaging session and another 2-3 months later. Physiological noise corrections accounted for significant variance, particularly in blood vessels, sagittal sinus, cerebrospinal fluid, and gray matter. The fraction of variance explained by each of these corrections was highly similar within subjects between sessions, but variable between subjects. Physiological corrections generally reduced intrasubject (between-session) variability, but also significantly reduced intersubject variability, and thus reduced the test-retest reliability of estimating individual differences in functional connectivity. However, based on known nonneuronal mechanisms by which cardiac pulsation and respiration can lead to MRI signal changes, and the observation that the physiological noise itself is highly stable within individuals, removal of this noise will likely increase the validity of measured connectivity differences. Furthermore, removal of these fluctuations will lead to better estimates of average or group maps of connectivity. It is therefore recommended that studies apply physiological noise corrections but also be mindful of potential correlations with measures of interest.

In this study, we investigated the neural correlates of age-related differences in the binding of verbal and spatial information utilizing event-related working memory tasks. Twenty-one right handed younger adults and twenty-one right handed older adults performed two versions of a dual task of verbal and spatial working memory. In the unbound dual task version letters and locations were presented simultaneously in separate locations, while in the bound dual task version each letter was paired with a specific location. In order to identify binding-specific differences, mixed-effects ANOVAs were run with the interaction of age and task as the effect of interest. Although older adults performed worse in the bound task than younger adults, there was no significant interaction between task and age on working memory performance. However, interactions of age and task were observed in brain activity analyses. Older adults did not display the greater unbound than bound task activity that younger adults did at the encoding phase in bilateral inferior parietal lobule, right putamen, and globus pallidus as well as at the maintenance phase in the cerebellum. We conclude that the binding of letters and locations in working memory is not as efficient in older adults as it is in younger adults, possibly due to the decline of cognitive control processes that are specific to working memory binding., (Copyright © 2014 Elsevier B.V. All rights reserved.)

Introduction: Support vector machines (SVM) have recently been demonstrated to be useful for voxel-based MR image classification. In the present study we sought to evaluate whether this method is feasible in the classification of childhood epilepsy intractability based on diffusion tensor imaging (DTI), with adequate accuracy. We applied SVM in conjunction DTI indices of fractional anisotropy (FA), mean diffusivity (MD), radial diffusivity (RD) and axial diffusivity (AD). DTI studies have reported white matter abnormalities in childhood-onset epilepsy, but the mechanisms underlying these abnormalities are not well understood. The aim of this study was to examine the relationship between epileptic seizures and cerebral white matter abnormalities identified by DTI in children with active compared to remitted epilepsy utilizing an automated and unsupervised classification method., Methods: The DTI data were tensor-derived indices including FA, MD, AD and RD in 49 participants including 20 children with epilepsy 5-6 years after seizure onset as compared to healthy controls. To determine whether there was normalization of white matter diffusion behavior following cessation of seizures and treatment, the epilepsy subjects were grouped into those with active versus remitted epilepsy. Group comparisons were previously made examining FA, MD and RD via whole-brain tract-based spatial statistics (TBSS). The SVM analysis was undertaken with the WEKA software package with 10-fold cross validation. Weighted sensitivity, specificity and accuracy were measured for all the DTI indices for two classifications: (1) controls vs. all children with epilepsy and (2) controls vs. children with remitted epilepsy vs. children with active epilepsy., Results: Using TBSS, significant differences were identified between controls and all children with epilepsy, between controls and children with active epilepsy, and also between the active and remitted epilepsy groups. There were no significant differences between the remitted epilepsy and controls on any DTI measure. In the SVM analysis, the best predictor between controls and all children with epilepsy was MD, with a sensitivity of 90-100% and a specificity between 96.6 and 100%. For the three-way classification, the best results were for FA with 100% sensitivity and specificity., Conclusion: DTI-based SVM classification appears promising for distinguishing children with active epilepsy from either those with remitted epilepsy or controls, and the question that arises is whether it will prove useful as a prognostic index of seizure remission. While SVM can correctly identify children with active epilepsy from other groups' diagnosis, further research is needed to determine the efficacy of SVM as a prognostic tool in longitudinal clinical studies.

Functional MRI blood oxygen level-dependent (BOLD) signal changes can be subtle, motivating the use of imaging parameters and processing strategies that maximize the temporal signal-to-noise ratio (tSNR) and thus the detection power of neuronal activity-induced fluctuations. Previous studies have shown that acquiring data at higher spatial resolutions results in greater percent BOLD signal changes, and furthermore that spatially smoothing higher resolution fMRI data improves tSNR beyond that of data originally acquired at a lower resolution. However, higher resolution images come at the cost of increased acquisition time, and the number of image volumes also influences detectability. The goal of our study is to determine how the detection power of neuronally induced BOLD fluctuations acquired at higher spatial resolutions and then spatially smoothed compares to data acquired at the lower resolutions with the same imaging duration. The number of time points acquired during a given amount of imaging time is a practical consideration given the limited ability of certain populations to lie still in the MRI scanner. We compare acquisitions at three different in-plane spatial resolutions (3.50×3.50mm(2), 2.33×2.33mm(2), 1.75×1.75mm(2)) in terms of their tSNR, contrast-to-noise ratio, and the power to detect both task-related activation and resting-state functional connectivity. The impact of SENSE acceleration, which speeds up acquisition time increasing the number of images collected, is also evaluated. Our results show that after spatially smoothing the data to the same intrinsic resolution, lower resolution acquisitions have a slightly higher detection power of task-activation in some, but not all, brain areas. There were no significant differences in functional connectivity as a function of resolution after smoothing. Similarly, the reduced tSNR of fMRI data acquired with a SENSE factor of 2 is offset by the greater number of images acquired, resulting in few significant differences in detection power of either functional activation or connectivity after spatial smoothing., (© 2013.)

There has been an increasing use of functional magnetic resonance imaging (fMRI) by the neuroscience community to examine differences in functional connectivity between normal control groups and populations of interest. Understanding the reliability of these functional connections is essential to the study of neurological development and degenerate neuropathological conditions. To date, most research assessing the reliability with which resting-state functional connectivity characterizes the brain's functional networks has been on scans between 3 and 11 min in length. In our present study, we examine the test-retest reliability and similarity of resting-state functional connectivity for scans ranging in length from 3 to 27 min as well as for time series acquired during the same length of time but excluding half the time points via sampling every second image. Our results show that reliability and similarity can be greatly improved by increasing the scan lengths from 5 min up to 13 min, and that both the increase in the number of volumes as well as the increase in the length of time over which these volumes was acquired drove this increase in reliability. This improvement in reliability due to scan length is much greater for scans acquired during the same session. Gains in intersession reliability began to diminish after 9-12 min, while improvements in intrasession reliability plateaued around 12-16 min. Consequently, new techniques that improve reliability across sessions will be important for the interpretation of longitudinal fMRI studies., (Copyright © 2013. Published by Elsevier Inc.)

Maltreatment during childhood is a major risk factor for anxiety and depression, which are major public health problems. However, the underlying brain mechanism linking maltreatment and internalizing disorders remains poorly understood. Maltreatment may alter the activation of fear circuitry, but little is known about its impact on the connectivity of this circuitry in adolescence and whether such brain changes actually lead to internalizing symptoms. We examined the associations between experiences of maltreatment during childhood, resting-state functional brain connectivity (rs-FC) of the amygdala and hippocampus, and internalizing symptoms in 64 adolescents participating in a longitudinal community study. Childhood experiences of maltreatment were associated with lower hippocampus-subgenual cingulate rs-FC in both adolescent females and males and lower amygdala-subgenual cingulate rs-FC in females only. Furthermore, rs-FC mediated the association of maltreatment during childhood with adolescent internalizing symptoms. Thus, maltreatment in childhood, even at the lower severity levels found in a community sample, may alter the regulatory capacity of the brain's fear circuit, leading to increased internalizing symptoms by late adolescence. These findings highlight the importance of fronto-hippocampal connectivity for both sexes in internalizing symptoms following maltreatment in childhood. Furthermore, the impact of maltreatment during childhood on both fronto-amygdala and -hippocampal connectivity in females may help explain their higher risk for internalizing disorders such as anxiety and depression.

The goal of resting-state functional magnetic resonance imaging (fMRI) is to investigate the brain's functional connections by using the temporal similarity between blood oxygenation level dependent (BOLD) signals in different regions of the brain "at rest" as an indicator of synchronous neural activity. Since this measure relies on the temporal correlation of fMRI signal changes between different parts of the brain, any non-neural activity-related process that affects the signals will influence the measure of functional connectivity, yielding spurious results. To understand the sources of these resting-state fMRI confounds, this article describes the origins of the BOLD signal in terms of MR physics and cerebral physiology. Potential confounds arising from motion, cardiac and respiratory cycles, arterial CO₂ concentration, blood pressure/cerebral autoregulation, and vasomotion are discussed. Two classes of techniques to remove confounds from resting-state BOLD time series are reviewed: 1) those utilising external recordings of physiology and 2) data-based cleanup methods that only use the resting-state fMRI data itself. Further methods that remove noise from functional connectivity measures at a group level are also discussed. For successful interpretation of resting-state fMRI comparisons and results, noise cleanup is an often over-looked but essential step in the analysis pipeline., (Copyright © 2013 Elsevier Inc. All rights reserved.)

Resting-state fMRI (rs-fMRI) has been demonstrated to have moderate to high reliability and produces consistent patterns of connectivity across a wide variety of subjects, sites, and scanners. However, there is no one agreed upon method to acquire rs-fMRI data. Some sites instruct their subjects, or patients, to lie still with their eyes closed, while other sites instruct their subjects to keep their eyes open or even fixating on a cross during scanning. Several studies have compared those three resting conditions based on connectivity strength. In our study, we assess differences in metrics of test-retest reliability (using an intraclass correlation coefficient), and consistency of the rank-order of connections within a subject and the ranks of subjects for a particular connection from one session to another (using Kendall's W tests). Twenty-five healthy subjects were scanned at three different time points for each resting condition, twice the same day and another time two to three months later. Resting-state functional connectivity measures were evaluated in motor, visual, auditory, attention, and default-mode networks, and compared between the different resting conditions. Of the networks examined, only the auditory network resulted in significantly higher connectivity in the eyes closed condition compared to the other two conditions. No significant between-condition differences in connectivity strength were found in default mode, attention, visual, and motor networks. Overall, the differences in reliability and consistency between different resting conditions were relatively small in effect size but results were found to be significant. Across all within-network connections, and within default-mode, attention, and auditory networks statistically significant greater reliability was found when the subjects were lying with their eyes fixated on a cross. In contrast, primary visual network connectivity was most reliable when subjects had their eyes open (and not fixating on a cross)., (Copyright © 2013 Elsevier Inc. All rights reserved.)

The brain at rest consists of spatially distributed but functionally connected regions, called intrinsic connectivity networks (ICNs). Resting state functional magnetic resonance imaging (rs-fMRI) has emerged as a way to characterize brain networks without confounds associated with task fMRI such as task difficulty and performance. Here we applied a Support Vector Machine (SVM) linear classifier as well as a support vector machine regressor to rs-fMRI data in order to compare age-related differences in four of the major functional brain networks: the default, cingulo-opercular, fronto-parietal, and sensorimotor. A linear SVM classifier discriminated between young and old subjects with 84% accuracy (p-value < 1 × 10(-7)). A linear SVR age predictor performed reasonably well in continuous age prediction (R (2) = 0.419, p-value < 1 × 10(-8)). These findings reveal that differences in intrinsic connectivity as measured with rs-fMRI exist between subjects, and that SVM methods are capable of detecting and utilizing these differences for classification and prediction.