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Aim The aim of this three-year retrospective study (2010-2012) is to determine the prevalence, trend and factors associated with HIV infections among pregnant women presenting to Olabisi Onabanjo University Teaching Hospital (OOUTH) and Redeemed Camp Maternity Centre (RCMC) both in Ogun state, south-west, Nigeria. Background HIV in pregnant women has become an important focus of HIV research because of its role in the spread of HIV infection, particularly, among children. Materials and methods A total of 4140 case records comprising 2272 at OOUTH and 1868 at the RCMC were reviewed. Of these, 3486 (84.2%) had records on HIV status. Test of association was carried out to determine factors associated with HIV infection in the two hospitals. Results Of 3486 ante-natal clinic attendees during the study period, 272 were confirmed HIV positive resulting in an overall prevalence rate of 7.7%. Analysis of records at the RCMC showed a prevalence of 0.41% (5/1212) while 11.5% (262/2272) was observed at the OOUTH. The prevalence was higher among older women than the younger ones. In RCMC, test of independence showed that HIV prevalence was dependent on parity and religion (p < 0.05) while in OOUTH, it was dependent on gravidity and parity (p < 0.05). Conclusions The overall HIV prevalence rate of 7.7% observed among pregnant women in this study was higher than the national average of 4.1%. Furthermore, HIV prevention campaigns need to be tailored towards older women as done for the younger ones in Nigeria, since prevalence was higher in the former than the latter. [ABSTRACT FROM AUTHOR]

The characterization of individual functional brain organization with Precision Functional Mapping has provided important insights in recent years in adults. However, little is known about the ontogeny of inter-individual differences in brain functional organization during human development. Precise characterization of systems organization during periods of high plasticity is likely to be essential for discoveries promoting lifelong health. Obtaining precision fMRI data during development has unique challenges that highlight the importance of establishing new methods to improve data acquisition, processing, and analysis. Here, we investigate two methods that can facilitate attaining this goal: multi-echo (ME) data acquisition and thermal noise removal with Noise Reduction with Distribution Corrected (NORDIC) principal component analysis. We applied these methods to precision fMRI data from adults, children, and newborn infants. In adults, both ME acquisitions and NORDIC increased temporal signal to noise ratio (tSNR) as well as the split-half reliability of functional connectivity matrices, with the combination helping more than either technique alone. The benefits of NORDIC denoising replicated in both our developmental samples. ME acquisitions revealed longer and more variable T2* relaxation times across the brain in infants relative to older children and adults, leading to major differences in the echo weighting for optimally combining ME data. This result suggests ME acquisitions may be a promising tool for optimizing developmental fMRI, albeit application in infants needs further investigation. The present work showcases methodological advances that improve Precision Functional Mapping in adults and developmental populations and, at the same time, highlights the need for further improvements in infant specific fMRI., Competing Interests: 8.Declaration of Competing Interests Damien A. Fair is a patent holder on the Framewise Integrated Real-Time Motion Monitoring (FIRMM) software. He is also a co-founder of Turing Medical Inc that licenses this software. The nature of this financial interest and the design of the study have been reviewed by two committees at the University of Minnesota. They have put in place a plan to help ensure that this research study is not affected by the financial interest. Steven M. Nelson consults for Turing Medical, which commercializes FIRMM. This interest has been reviewed and managed by the University of Minnesota in accordance with its Conflict of Interest policies. Author Nico U. F. Dosenbach is a co-founder of Turing Medical Inc, has financial interest, and may benefit financially if the company is successful in marketing FIRMM motion monitoring software products. NUFD may receive royalty income based on FIRMM technology developed at Washington University School of Medicine (WUSOM) and licensed to Turing Medical Inc. Timothy O. Laumann holds a patent for taskless mapping of brain activity licensed to Sora Neurosciences and a patent for optimizing targets for neuromodulation, implant localization, and ablation is pending. TOL is also a consultant for Turing Medical Inc. Abraham Z. Snyder is a consultant for Sora Neurosciences. These potential conflicts of interest have been reviewed and are managed by Washington University School of Medicine. The other authors declare no competing interests.

Machine learning algorithms are increasingly being utilized to identify brain connectivity biomarkers linked to behavioral and clinical outcomes. However, research often prioritizes prediction accuracy at the expense of biological interpretability, and inconsistent implementation of ML methods may hinder model accuracy. To address this, our paper introduces a network-level enrichment approach, which integrates brain system organization in the context of connectome-wide statistical analysis to reveal network-level links between brain connectivity and behavior. To demonstrate the efficacy of this approach, we used linear support vector regression (LSVR) models to examine the relationship between resting-state functional connectivity networks and chronological age. We compared network-level associations based on raw LSVR weights to those produced from the forward and inverse models. Results indicated that not accounting for shared family variance inflated prediction performance, the k-best feature selection via Pearson correlation reduced accuracy and reliability, and raw LSVR model weights produced network-level associations that deviated from the significant brain systems identified by forward and inverse models. Our findings offer crucial insights for applying machine learning to neuroimaging data, emphasizing the value of network enrichment for biological interpretation., Competing Interests: Competing Interests: The authors have declared that no competing interests exist., (© 2024 Massachusetts Institute of Technology.)

Recent work identified single time points ("events") of high regional cofluctuation in functional Magnetic Resonance Imaging (fMRI) which contain more large-scale brain network information than other, low cofluctuation time points. This suggested that events might be a discrete, temporally sparse signal which drives functional connectivity (FC) over the timeseries. However, a different, not yet explored possibility is that network information differences between time points are driven by sampling variability on a constant, static, noisy signal. Using a combination of real and simulated data, we examined the relationship between cofluctuation and network structure and asked if this relationship was unique, or if it could arise from sampling variability alone. First, we show that events are not discrete - there is a gradually increasing relationship between network structure and cofluctuation; ∼50% of samples show very strong network structure. Second, using simulations we show that this relationship is predicted from sampling variability on static FC. Finally, we show that randomly selected points can capture network structure about as well as events, largely because of their temporal spacing. Together, these results suggest that, while events exhibit particularly strong representations of static FC, there is little evidence that events are unique timepoints that drive FC structure. Instead, a parsimonious explanation for the data is that events arise from a single static, but noisy, FC structure., Competing Interests: Declaration of Competing Interest None, (Copyright © 2022 The Author(s). Published by Elsevier Inc. All rights reserved.)

Diffusion imaging aims to non-invasively characterize the anatomy and integrity of the brain's white matter fibers. We evaluated the accuracy and reliability of commonly used diffusion imaging methods as a function of data quantity and analysis method, using both simulations and highly sampled individual-specific data (927-1442 diffusion weighted images [DWIs] per individual). Diffusion imaging methods that allow for crossing fibers (FSL's BedpostX [BPX], DSI Studio's Constant Solid Angle Q-Ball Imaging [CSA-QBI], MRtrix3's Constrained Spherical Deconvolution [CSD]) estimated excess fibers when insufficient data were present and/or when the data did not match the model priors. To reduce such overfitting, we developed a novel Bayesian Multi-tensor Model-selection (BaMM) method and applied it to the popular ball-and-stick model used in BedpostX within the FSL software package. BaMM was robust to overfitting and showed high reliability and the relatively best crossing-fiber accuracy with increasing amounts of diffusion data. Thus, sufficient data and an overfitting resistant analysis method enhance precision diffusion imaging. For potential clinical applications of diffusion imaging, such as neurosurgical planning and deep brain stimulation (DBS), the quantities of data required to achieve diffusion imaging reliability are lower than those needed for functional MRI., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022. Published by Elsevier Inc.)

Many recent developments surrounding the functional network organization of the human brain have focused on data that have been averaged across groups of individuals. While such group-level approaches have shed considerable light on the brain's large-scale distributed systems, they conceal individual differences in network organization, which recent work has demonstrated to be common and widespread. This individual variability produces noise in group analyses, which may average together regions that are part of different functional systems across participants, limiting interpretability. However, cost and feasibility constraints may limit the possibility for individual-level mapping within studies. Here our goal was to leverage information about individual-level brain organization to probabilistically map common functional systems and identify locations of high inter-subject consensus for use in group analyses. We probabilistically mapped 14 functional networks in multiple datasets with relatively high amounts of data. All networks show "core" (high-probability) regions, but differ from one another in the extent of their higher-variability components. These patterns replicate well across four datasets with different participants and scanning parameters. We produced a set of high-probability regions of interest (ROIs) from these probabilistic maps; these and the probabilistic maps are made publicly available, together with a tool for querying the network membership probabilities associated with any given cortical location. These quantitative estimates and public tools may allow researchers to apply information about inter-subject consensus to their own fMRI studies, improving inferences about systems and their functional specializations., (Copyright © 2021. Published by Elsevier Inc.)

Whole-brain resting-state functional MRI (rs-fMRI) during 2 wk of upper-limb casting revealed that disused motor regions became more strongly connected to the cingulo-opercular network (CON), an executive control network that includes regions of the dorsal anterior cingulate cortex (dACC) and insula. Disuse-driven increases in functional connectivity (FC) were specific to the CON and somatomotor networks and did not involve any other networks, such as the salience, frontoparietal, or default mode networks. Censoring and modeling analyses showed that FC increases during casting were mediated by large, spontaneous activity pulses that appeared in the disused motor regions and CON control regions. During limb constraint, disused motor circuits appear to enter a standby mode characterized by spontaneous activity pulses and strengthened connectivity to CON executive control regions., Competing Interests: Competing interest statement: N.U.F.D. is a cofounder of NOUS Imaging.

Background: Autism spectrum disorder (ASD) is characterized by high population-level heritability and a three-to-one male-to-female ratio that occurs independent of sex linkage. Prior research in a mixed-sex pediatric sample identified neural signatures of familial risk elicited by passive viewing of point light motion displays, suggesting the possibility that both resilience and risk of autism might be associated with brain responses to biological motion. To confirm a relationship between these signatures and inherited risk of autism, we tested them in families enriched for genetic loading through undiagnosed ("carrier") females., Methods: Using functional magnetic resonance imaging, we examined brain responses to passive viewing of point light displays-depicting biological versus non-biological motion-in a sample of undiagnosed adult females enriched for inherited susceptibility to ASD on the basis of affectation in their respective family pedigrees. Brain responses in carrier females were compared to responses in age-, SRS-, and IQ-matched non-carrier-females-i.e., females unrelated to individuals with ASD. We conducted a hypothesis-driven analysis focused on previously published regions of interest as well as exploratory, brain-wide analyses designed to characterize more fully the rich responses to this paradigm., Results: We observed robust responses to biological motion. Notwithstanding, the 12 regions implicated by prior research did not exhibit the hypothesized interaction between group (carriers vs. controls) and point light displays (biological vs. non-biological motion). Exploratory, brain-wide analyses identified this interaction in three novel regions. Post hoc analyses additionally revealed significant variations in the time course of brain activation in 20 regions spanning occipital and temporal cortex, indicating group differences in response to point light displays (irrespective of the nature of motion) for exploration in future studies., Limitations: We were unable to successfully eye-track all participants, which prevented us from being able to control for potential differences in eye gaze position., Conclusions: These methods confirmed pronounced neural signatures that differentiate brain responses to biological and scrambled motion. Our sample of undiagnosed females enriched for family genetic loading enabled discovery of numerous contrasts between carriers and non-carriers of risk of ASD that may index variations in visual attention and motion processing related to genetic susceptibility and inform our understanding of mechanisms incurred by inherited liability for ASD.

This article advances two parallel lines of argument about resting-state functional magnetic resonance imaging (fMRI) signals, one empirical and one conceptual. The empirical line creates a four-part organization of the text: (1) head motion and respiration commonly cause distinct, major, unwanted influences (artifacts) in fMRI signals; (2) head motion and respiratory changes are, confoundingly, both related to psychological and clinical and biological variables of interest; (3) many fMRI denoising strategies fail to identify and remove one or the other kind of artifact; and (4) unremoved artifact, due to correlations of artifacts with variables of interest, renders studies susceptible to identifying variance of noninterest as variance of interest. Arising from these empirical observations is a conceptual argument: that an event-related approach to task-free scans, targeting common behaviors during scanning, enables fundamental distinctions among the kinds of signals present in the data, information which is vital to understanding the effects of denoising procedures. This event-related perspective permits statements like "Event X is associated with signals A, B, and C, each with particular spatial, temporal, and signal decay properties". Denoising approaches can then be tailored, via performance in known events, to permit or suppress certain kinds of signals based on their desirability., (© The Author(s) 2020. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permission@oup.com.)

Denoising fMRI data requires assessment of frame-to-frame head motion and removal of the biases motion introduces. This is usually done through analysis of the parameters calculated during retrospective head motion correction (i.e., 'motion' parameters). However, it is increasingly recognized that respiration introduces factitious head motion via perturbations of the main (B0) field. This effect appears as higher-frequency fluctuations in the motion parameters (>0.1 ​Hz, here referred to as 'HF-motion'), primarily in the phase-encoding direction. This periodicity can sometimes be obscured in standard single-band fMRI (TR 2.0-2.5 ​s) due to aliasing. Here we examined (1) how prevalent HF-motion effects are in seven single-band datasets with TR from 2.0 to 2.5 ​s and (2) how HF-motion affects functional connectivity. We demonstrate that HF-motion is more common in older adults, those with higher body mass index, and those with lower cardiorespiratory fitness. We propose a low-pass filtering approach to remove the contamination of high frequency effects from motion summary measures, such as framewise displacement (FD). We demonstrate that in most datasets this filtering approach saves a substantial amount of data from FD-based frame censoring, while at the same time reducing motion biases in functional connectivity measures. These findings suggest that filtering motion parameters is an effective way to improve the fidelity of head motion estimates, even in single band datasets. Particularly large data savings may accrue in datasets acquired in older and less fit participants., (Copyright © 2020 The Authors. Published by Elsevier Inc. All rights reserved.)

To induce brain plasticity in humans, we casted the dominant upper extremity for 2 weeks and tracked changes in functional connectivity using daily 30-min scans of resting-state functional MRI (rs-fMRI). Casting caused cortical and cerebellar regions controlling the disused extremity to functionally disconnect from the rest of the somatomotor system, while internal connectivity within the disused sub-circuit was maintained. Functional disconnection was evident within 48 h, progressed throughout the cast period, and reversed after cast removal. During the cast period, large, spontaneous pulses of activity propagated through the disused somatomotor sub-circuit. The adult brain seems to rely on regular use to maintain its functional architecture. Disuse-driven spontaneous activity pulses may help preserve functionally disconnected sub-circuits., Competing Interests: Declaration Of Interests The authors declare the following competing financial interest: N.U.F.D. is co-founder of NOUS Imaging., (Copyright © 2020 Elsevier Inc. All rights reserved.)

Head motion represents one of the greatest technical obstacles in magnetic resonance imaging (MRI) of the human brain. Accurate detection of artifacts induced by head motion requires precise estimation of movement. However, head motion estimates may be corrupted by artifacts due to magnetic main field fluctuations generated by body motion. In the current report, we examine head motion estimation in multiband resting state functional connectivity MRI (rs-fcMRI) data from the Adolescent Brain and Cognitive Development (ABCD) Study and comparison 'single-shot' datasets. We show that respirations contaminate movement estimates in functional MRI and that respiration generates apparent head motion not associated with functional MRI quality reductions. We have developed a novel approach using a band-stop filter that accurately removes these respiratory effects from motion estimates. Subsequently, we demonstrate that utilizing a band-stop filter improves post-processing fMRI data quality. Lastly, we demonstrate the real-time implementation of motion estimate filtering in our FIRMM (Framewise Integrated Real-Time MRI Monitoring) software package., Competing Interests: Declaration of competing interest D.A.F., E.A.E., J.M.K., A. v.d.K., A.E.M. and N.U.F.D. have a financial interest in NOUS Imaging Inc. and may financially benefit if the company is successful in marketing FIRMM software products related to this research. D.A.F., O.M.-D., A.Z.S., A.P., E.A.E., A.N.V., J.M.K., R.L.K., A.E.M., N.U.F.D. may receive royalty income based on FIRMM technology developed at Oregon Health and Sciences University and Washington University and licensed to NOUS Imaging Inc. Part of that technology is evaluated in this research., (Copyright © 2019 The Authors. Published by Elsevier Inc. All rights reserved.)

Resting-state functional magnetic resonance imaging (fMRI) has provided converging descriptions of group-level functional brain organization. Recent work has revealed that functional networks identified in individuals contain local features that differ from the group-level description. We define these features as network variants. Building on these studies, we ask whether distributions of network variants reflect stable, trait-like differences in brain organization. Across several datasets of highly-sampled individuals we show that 1) variants are highly stable within individuals, 2) variants are found in characteristic locations and associate with characteristic functional networks across large groups, 3) task-evoked signals in variants demonstrate a link to functional variation, and 4) individuals cluster into subgroups on the basis of variant characteristics that are related to differences in behavior. These results suggest that distributions of network variants may reflect stable, trait-like, functionally relevant individual differences in functional brain organization., Competing Interests: The authors declare no competing interest.

Neuroimaging studies have implicated a set of striatal and orbitofrontal cortex (OFC) regions that are commonly activated during reward processing tasks. Resting-state functional connectivity (RSFC) studies have demonstrated that the human brain is organized into several functional systems that show strong temporal coherence in the absence of goal-directed tasks. Here we use seed-based and graph-theory RSFC approaches to characterize the systems-level organization of putative reward regions of at rest. Peaks of connectivity from seed-based RSFC patterns for the nucleus accumbens (NAcc) and orbitofrontal cortex (OFC) were used to identify candidate reward regions which were merged with a previously used set of regions (Power et al., 2011). Graph-theory was then used to determine system-level membership for all regions. Several regions previously implicated in reward-processing (NAcc, lateral and medial OFC, and ventromedial prefrontal cortex) comprised a distinct, preferentially coupled system. This RSFC system is stable across a range of connectivity thresholds and shares strong overlap with meta-analyses of task-based reward studies. This reward system shares between-system connectivity with systems implicated in cognitive control and self-regulation, including the fronto-parietal, cingulo-opercular, and default systems. Differences may exist in the pathways through which control systems interact with reward system components. Whereas NAcc is functionally connected to cingulo-opercular and default systems, OFC regions show stronger connectivity with the fronto-parietal system. We propose that future work may be able to interrogate group or individual differences in connectivity profiles using the regions delineated in this work to explore potential relationships to appetitive behaviors, self-regulation failure, and addiction., (© 2018 Wiley Periodicals, Inc.)

The cerebellum contains the majority of neurons in the human brain and is unique for its uniform cytoarchitecture, absence of aerobic glycolysis, and role in adaptive plasticity. Despite anatomical and physiological differences between the cerebellum and cerebral cortex, group-average functional connectivity studies have identified networks related to specific functions in both structures. Recently, precision functional mapping of individuals revealed that functional networks in the cerebral cortex exhibit measurable individual specificity. Using the highly sampled Midnight Scan Club (MSC) dataset, we found the cerebellum contains reliable, individual-specific network organization that is significantly more variable than the cerebral cortex. The frontoparietal network, thought to support adaptive control, was the only network overrepresented in the cerebellum compared to the cerebral cortex (2.3-fold). Temporally, all cerebellar resting state signals lagged behind the cerebral cortex (125-380 ms), supporting the hypothesis that the cerebellum engages in a domain-general function in the adaptive control of all cortical processes., (Copyright © 2018 Elsevier Inc. All rights reserved.)

Objectives: Specific changes in the functional connectivity of brain networks occur in patients with epilepsy. Yet whether such changes reflect a stable disease effect or one that is a function of active seizure burden remains unclear. Here, we longitudinally assessed the connectivity of canonical cognitive functional networks in patients with intractable temporal lobe epilepsy (TLE), both before and after patients underwent epilepsy surgery and achieved seizure freedom., Methods: Seventeen patients with intractable TLE who underwent epilepsy surgery with Engel class I outcome and 17 matched healthy controls took part in the study. The functional connectivity of a set of cognitive functional networks derived from typical cognitive tasks was assessed in patients, preoperatively and postoperatively, as well as in controls, using stringent methods of artifact reduction., Results: Preoperatively, functional networks in TLE patients differed significantly from healthy controls, with differences that largely, but not exclusively, involved the default mode and temporal/auditory subnetworks. However, undergoing epilepsy surgery and achieving seizure freedom did not lead to significant changes in network connectivity, with postoperative functional network abnormalities closely mirroring the preoperative state., Significance: This result argues for a stable chronic effect of the disease on brain connectivity, with changes that are largely "burned in" by the time a patient with intractable TLE undergoes epilepsy surgery, which typically occurs years after the initial diagnosis. The result has potential implications for the treatment of intractable epilepsy, suggesting that delaying surgical intervention that may achieve seizure freedom may lead to functional network changes that are no longer reversible by the time of epilepsy surgery., (Wiley Periodicals, Inc. © 2017 International League Against Epilepsy.)

Measurement of correlations between brain regions (functional connectivity) using blood oxygen level dependent (BOLD) fMRI has proven to be a powerful tool for studying the functional organization of the brain. Recently, dynamic functional connectivity has emerged as a major topic in the resting-state BOLD fMRI literature. Here, using simulations and multiple sets of empirical observations, we confirm that imposed task states can alter the correlation structure of BOLD activity. However, we find that observations of "dynamic" BOLD correlations during the resting state are largely explained by sampling variability. Beyond sampling variability, the largest part of observed "dynamics" during rest is attributable to head motion. An additional component of dynamic variability during rest is attributable to fluctuating sleep state. Thus, aside from the preceding explanatory factors, a single correlation structure-as opposed to a sequence of distinct correlation structures-may adequately describe the resting state as measured by BOLD fMRI. These results suggest that resting-state BOLD correlations do not primarily reflect moment-to-moment changes in cognitive content. Rather, resting-state BOLD correlations may predominantly reflect processes concerned with the maintenance of the long-term stability of the brain's functional organization., (© The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.)

Recent functional magnetic resonance imaging-based resting-state functional connectivity analyses of group average data have characterized large-scale systems that represent a high level in the organizational hierarchy of the human brain. These systems are likely to vary spatially across individuals, even after anatomical alignment, but the characteristics of this variance are unknown. Here, we characterized large-scale brain systems across two independent datasets of young adults. In these individuals, we were able to identify brain systems that were similar to those described in the group average, and we observed that individuals had consistent topological arrangement of the system features present in the group average. However, the size of system features varied across individuals in systematic ways, such that expansion of one feature of a given system predicted expansion of other parts of the system. Individual-specific systems also contained unique topological features not present in group average systems; some of these features were consistent across a minority of individuals. These effects were observed even after controlling for data quality and for the accuracy of anatomical registration. The variability characterized here has important implications for cognitive neuroscience investigations, which often assume the functional equivalence of aligned brain regions across individuals., (© The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.)

Humans easily and flexibly complete a wide variety of tasks. To accomplish this feat, the brain appears to subtly adjust stable brain networks. Here, we investigate what regional factors underlie these modifications, asking whether networks are either altered at (1) regions activated by a given task or (2) hubs that interconnect different networks. We used fMRI "functional connectivity" (FC) to compare networks during rest and three distinct tasks requiring semantic judgments, mental rotation, and visual coherence. We found that network modifications during these tasks were independently associated with both regional activation and network hubs. Furthermore, active and hub regions were associated with distinct patterns of network modification (differing in their localization, topography of FC changes, and variability across tasks), with activated hubs exhibiting patterns consistent with task control. These findings indicate that task goals modify brain networks through two separate processes linked to local brain function and network hubs., (Copyright © 2016 The Author(s). Published by Elsevier Inc. All rights reserved.)

The cortical surface is organized into a large number of cortical areas; however, these areas have not been comprehensively mapped in the human. Abrupt transitions in resting-state functional connectivity (RSFC) patterns can noninvasively identify locations of putative borders between cortical areas (RSFC-boundary mapping; Cohen et al. 2008). Here we describe a technique for using RSFC-boundary maps to define parcels that represent putative cortical areas. These parcels had highly homogenous RSFC patterns, indicating that they contained one unique RSFC signal; furthermore, the parcels were much more homogenous than a null model matched for parcel size when tested in two separate datasets. Several alternative parcellation schemes were tested this way, and no other parcellation was as homogenous as or had as large a difference compared with its null model. The boundary map-derived parcellation contained parcels that overlapped with architectonic mapping of areas 17, 2, 3, and 4. These parcels had a network structure similar to the known network structure of the brain, and their connectivity patterns were reliable across individual subjects. These observations suggest that RSFC-boundary map-derived parcels provide information about the location and extent of human cortical areas. A parcellation generated using this method is available at http://www.nil.wustl.edu/labs/petersen/Resources.html., (© The Author 2014. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.)

Psychiatric disorders are characterized by major fluctuations in psychological function over the course of weeks and months, but the dynamic characteristics of brain function over this timescale in healthy individuals are unknown. Here, as a proof of concept to address this question, we present the MyConnectome project. An intensive phenome-wide assessment of a single human was performed over a period of 18 months, including functional and structural brain connectivity using magnetic resonance imaging, psychological function and physical health, gene expression and metabolomics. A reproducible analysis workflow is provided, along with open access to the data and an online browser for results. We demonstrate dynamic changes in brain connectivity over the timescales of days to months, and relations between brain connectivity, gene expression and metabolites. This resource can serve as a testbed to study the joint dynamics of human brain and metabolic function over time, an approach that is critical for the development of precision medicine strategies for brain disorders.

Resting state functional MRI (fMRI) has enabled description of group-level functional brain organization at multiple spatial scales. However, cross-subject averaging may obscure patterns of brain organization specific to each individual. Here, we characterized the brain organization of a single individual repeatedly measured over more than a year. We report a reproducible and internally valid subject-specific areal-level parcellation that corresponds with subject-specific task activations. Highly convergent correlation network estimates can be derived from this parcellation if sufficient data are collected-considerably more than typically acquired. Notably, within-subject correlation variability across sessions exhibited a heterogeneous distribution across the cortex concentrated in visual and somato-motor regions, distinct from the pattern of intersubject variability. Further, although the individual's systems-level organization is broadly similar to the group, it demonstrates distinct topological features. These results provide a foundation for studies of individual differences in cortical organization and function, especially for special or rare individuals. VIDEO ABSTRACT., (Copyright © 2015 Elsevier Inc. All rights reserved.)

Human large-scale functional brain networks are hypothesized to undergo significant changes over development. Little is known about these functional architectural changes, particularly during the second half of the first year of life. We used multivariate pattern classification of resting-state functional connectivity magnetic resonance imaging (fcMRI) data obtained in an on-going, multi-site, longitudinal study of brain and behavioral development to explore whether fcMRI data contained information sufficient to classify infant age. Analyses carefully account for the effects of fcMRI motion artifact. Support vector machines (SVMs) classified 6 versus 12 month-old infants (128 datasets) above chance based on fcMRI data alone. Results demonstrate significant changes in measures of brain functional organization that coincide with a special period of dramatic change in infant motor, cognitive, and social development. Explorations of the most different correlations used for SVM lead to two different interpretations about functional connections that support 6 versus 12-month age categorization., (Copyright © 2015 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Context: Suprapubic cystostomy (SPC) catheterization is a common and important technique for the management of vesicular drainage, especially in patients with neurogenic bladder. Some serious complications include bowel perforation and obstruction., Findings: A 55-year-old man with C6 American Spinal Injury Association B tetraplegia and a urethral stricture requiring a chronic SPC was admitted for recurrent urosepsis. Computed tomography (CT) of the abdomen revealed severe right hydronephrosis and hydroureter due to obstruction of the right distal ureter by the SPC tip. The SPC (30 French/10-mm silicone catheter with a 10-ml balloon) was removed and replaced with a similar suprapubic catheter (30 French/10-mm silicone catheter with an 8-ml balloon). Symptoms recurred 2 months later and he was readmitted for urosepsis. CT of the abdomen again revealed severe right hydronephrosis and hydroureter due to obstruction of the right distal ureter by the SPC tip. The SPC was removed, and the patient was given a 14 French/4.67-mm urethral silicone catheter with a 5-ml balloon. Follow-up CT of the abdomen 2 months later showed complete resolution of the hydronephrosis and hydroureter. Of note, urodynamic studies 2 years earlier revealed an extremely small bladder with a capacity less than 20 ml., Conclusion: This case illustrates that obstruction of the ureter by the tip of an SPC can be a cause of recurrent hydronephrosis and urosepsis.

Humans and monkeys use both vestibular and visual motion (optic flow) cues to discriminate their direction of self-motion during navigation. A striking property of heading perception from optic flow is that discrimination is most precise when subjects judge small variations in heading around straight ahead, whereas thresholds rise precipitously when subjects judge heading around an eccentric reference. We show that vestibular heading discrimination thresholds in both humans and macaques also show a consistent, but modest, dependence on reference direction. We used computational methods (Fisher information, maximum likelihood estimation, and population vector decoding) to show that population activity in area MSTd predicts the dependence of heading thresholds on reference eccentricity. This dependence arises because the tuning functions for most neurons have a steep slope for directions near straight forward. Our findings support the notion that population activity in extrastriate cortex limits the precision of both visual and vestibular heading perception., (Copyright 2010 Elsevier Inc. All rights reserved.)

The hepadnaviral polymerase (P) functions in a complex with viral nucleic acids and cellular chaperones. To begin to identify contacts between P and its partners, we assessed the exposure of the epitopes of six monoclonal antibodies (MAbs) to the terminal protein domain of the duck hepatitis B virus P protein in a partially denaturing buffer (RIPA) and a physiological buffer (IPP150). All MAbs immunoprecipitated in vitro translated P well in RIPA, but three immunoprecipitated P poorly in IPP150. Therefore, the epitopes for these MAbs were obscured in the native conformation of P but were exposed when P was in RIPA. Epitopes for MAbs that immunoprecipitated P poorly in IPP150 were between amino acids (aa) 138 and 202. Mutation of a highly conserved motif within this region (T3; aa 176 to 183) improved the immunoprecipitation of P by these MAbs and simultaneously inhibited DNA priming by P. Peptides containing the T3 motif inhibited DNA priming in a dose-dependent manner, whereas eight irrelevant peptides did not. T3 function appears to be conserved among the hepadnaviruses because mutating T3 ablated DNA synthesis in both duck hepatitis B virus and hepatitis B virus. These results indicate that (i) the conserved T3 motif is a molecular contact point whose ligand can be competed by soluble T3 peptides, (ii) the occupancy of T3 obscures the epitopes for three MAbs, and (iii) proper occupancy of T3 by its ligand is essential for DNA priming. Therefore, small-molecule ligands that compete for binding to T3 with its natural ligand could form a novel class of antiviral drugs.

Ocular following (OFR) is a short-latency visual stabilization response to the optic flow experienced during self-motion. It has been proposed that it represents the early component of optokinetic nystagmus (OKN) and that it is functionally linked to the vestibularly driven stabilization reflex during translation (translational vestibuloocular reflex, TVOR). Because no single eye movement can eliminate slip from the whole retina during translation, the OFR and the TVOR appear to be functionally related to maintaining visual acuity on the fovea. Other foveal-specific eye movements, like smooth pursuit and saccades, exhibit an eye-position-dependent torsional component, as dictated by what is known as the "half-angle rule" of Listing's law. In contrast, eye movements that stabilize images on the whole retina, such as the rotational vestibuloocular reflex (RVOR) and steady-state OKN do not. Consistent with the foveal stabilization hypothesis, it was recently shown that the TVOR is indeed characterized by an eye-position-dependent torsion, similar to pursuit eye movements. Here we have investigated whether the OFR exhibits three-dimensional kinematic properties consistent with a foveal response (i.e., similar to the TVOR and smooth pursuit eye movements) or with a whole-field stabilization function (similar to steady-state OKN). The OFR was elicited using 100-ms ramp motion of a full-field random dot pattern that moved horizontally at 20, 62, or 83 degrees/s. To study if an eye-position-dependent torsion is generated during the OFR, we varied the initial fixation position vertically within a range of +/-20 degrees . As a control, horizontal smooth pursuit eye movements were also elicited using step-ramp target motion (10, 20, or 30 degrees/s) at similar eccentric positions. We found that the OFR followed kinematic properties similar to those seen in pursuit and the TVOR with the eye-position-dependent torsional tilt of eye velocity having slopes that averaged 0.73 +/- 0.16 for OFR and 0.57 +/- 0.12 (means +/- SD) for pursuit. These findings support the notion that the OFR, like the TVOR and pursuit, are foveal image stabilization systems.