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Recent years have seen growing interest in characterizing the properties of regional brain dynamics and their relationship to other features of brain structure and function. In particular, multiple studies have observed regional differences in the timescale over which activity fluctuates during periods of quiet rest. In the cerebral cortex, these timescales have been associated with both local circuit properties as well as patterns of inter-regional connectivity, including the extent to which each region exhibits widespread connectivity to other brain areas. In the current study, we build on prior observations of an association between connectivity and dynamics in the cerebral cortex by investigating the relationship between BOLD fMRI timescales and the modular organization of structural and functional brain networks. We characterize network community structure across multiple scales and find that longer timescales are associated with greater within-community functional connectivity and diverse structural connectivity. We also replicate prior observations of a positive correlation between timescales and structural connectivity degree. Finally, we find evidence for preferential functional connectivity between cortical areas with similar timescales. We replicate these findings in an independent dataset. These results contribute to our understanding of functional brain organization and structure-function relationships in the human brain, and support the notion that regional differences in cortical dynamics may in part reflect the topological role of each region within macroscale brain networks.

OBJECTIVE: Resting-state functional MRI (RS-fMRI) enables the mapping of function within the brain and is emerging as an efficient tool for the presurgical evaluation of eloquent cortex. Models capable of reliable and precise mapping of resting-state networks (RSNs) with a reduced scanning time would lead to improved patient comfort while reducing the cost per scan. The aims of the present study were to develop a deep 3D convolutional neural network (3DCNN) capable of voxel-wise mapping of language (LAN) and motor (MOT) RSNs with minimal quantities of RS-fMRI data. METHODS: Imaging data were gathered from multiple ongoing studies at Washington University School of Medicine and other thoroughly characterized, publicly available data sets. All study participants (n = 2252 healthy adults) were cognitively screened and completed structural neuroimaging and RS-fMRI. Random permutations of RS-fMRI regions of interest were used to train a 3DCNN. After training, model inferences were compared using varying amounts of RS-fMRI data from the control data set as well as 5 patients with glioblastoma multiforme. RESULTS: The trained model achieved 96% out-of-sample validation accuracy on data encompassing a large age range collected on multiple scanner types and varying sequence parameters. Testing on out-of-sample control data showed 97.9% similarity between results generated using either 50 or 200 RS-fMRI time points, corresponding to approximately 2.5 and 10 minutes, respectively (96.9% LAN, 96.3% MOT true-positive rate). In evaluating data from patients with brain tumors, the 3DCNN was able to accurately map LAN and MOT networks despite structural and functional alterations. CONCLUSIONS: Functional maps produced by the 3DCNN can inform surgical planning in patients with brain tumors in a time-efficient manner. The authors present a highly efficient method for presurgical functional mapping and thus improved functional preservation in patients with brain tumors.

STUDY OBJECTIVES: Sleep duration and intraindividual variability in sleep duration undergo substantial changes in adolescence and impact brain and behavioral functioning. Although experimental work has linked acute sleep deprivation to heightened limbic responding and reduced regulatory control, there is limited understanding of how variability in sleep patterns might interact with sleep duration to influence adolescent functioning. This is important for optimal balancing of length and consistency of sleep. Here, we investigated how objective indices of sleep duration and variability relate to stress, restfulness, and intrinsic limbic network functioning in adolescents. METHODS: A sample of 101 adolescents ages 14-18 reported their stressors, after which they wore wrist actigraph watches to monitor their sleep and rated their restfulness every morning over a 2-week period. They also completed a resting-state fMRI scan. RESULTS: Adolescents reporting more stress experienced shorter sleep duration and greater sleep variability over the 2-week period. Longer nightly sleep duration was linked to feeling more rested the next morning, but this effect was reduced in adolescents with high cumulative sleep variability. Sleep variability showed both linear and quadratic effects on limbic connectivity: adolescents with high sleep variability exhibited more connectivity within the limbic network and less connectivity between the limbic and frontoparietal networks than their peers, effects which became stronger once variability exceeded an hour. CONCLUSIONS: Results suggest that cumulative sleep variability is related to stress and limbic network connectivity and shows interactive effects with sleep duration, highlighting the importance of balancing length and consistency of sleep for optimal functioning in adolescence.

BACKGROUND: While Alzheimers disease (AD) pathology is associated with altered brain structure, it is not clear whether gene expression changes mirror the onset and evolution of pathology in distinct brain regions. Deciphering the mechanisms which cause the differential manifestation of the disease across different regions has the potential to help early diagnosis. OBJECTIVE: We aimed to identify common and unique endotypes and their regulation in tangle-free neurons in sporadic AD (SAD) across six brain regions: entorhinal cortex (EC), hippocampus (HC), medial temporal gyrus (MTG), posterior cingulate (PC), superior frontal gyrus (SFG), and visual cortex (VCX). METHODS: To decipher the states of tangle-free neurons across different brain regions in human subjects afflicted with AD, we performed analysis of the neural transcriptome. We explored changes in differential gene expression, functional and transcription factor target enrichment, and co-expression gene module detection analysis to discern disease-state transcriptomic variances and characterize endotypes. Additionally, we compared our results to tangled AD neuron microarray-based study and the Allen Brain Atlas. RESULTS: We identified impaired neuron function in EC, MTG, PC, and VCX resulting from REST activation and reversal of mature neurons to a precursor-like state in EC, MTG, and SFG linked to SOX2 activation. Additionally, decreased neuron function and increased dedifferentiation were linked to the activation of SUZ12. Energetic deficit connected to NRF1 inactivation was found in HC, PC, and VCX. CONCLUSIONS: Our findings suggest that SAD manifestation varies in scale and severity in different brain regions. We identify endotypes, such as energetic shortfalls, impaired neuronal function, and dedifferentiation.

Previous studies indicate that the structure and function of medial prefrontal cortex (PFC) and lateral orbitofrontal cortex (OFC) are associated with heart rate variability (HRV). Typically, this association is assumed to reflect the PFCs role in controlling HRV and emotion regulation, with better prefrontal structural integrity supporting greater HRV and better emotion regulation. However, as a control system, the PFC must monitor and respond to heart rate oscillatory activity. Thus, engaging in regulatory feedback during heart rate oscillatory activity may over time help shape PFC structure, as relevant circuits and connections are modified. In the current study with younger and older adults, we tested whether 5 weeks of daily sessions of biofeedback to increase heart rate oscillations (Osc+ condition) vs. to decrease heart rate oscillations (Osc- condition) affected cortical volume in left OFC and right OFC, two regions particularly associated with HRV in prior studies. The left OFC showed significant differences in volume change across conditions, with Osc+ increasing volume relative to Osc-. The volume changes in left OFC were significantly correlated with changes in mood disturbance. In addition, resting low frequency HRV increased more in the Osc+ than in the Osc- condition. These findings indicate that daily biofeedback sessions regulating heart rate oscillatory activity can shape both resting HRV and the brain circuits that help control HRV and regulate emotion.

In this work, we focus on explicitly nonlinear relationships in functional networks. We introduce a technique using normalized mutual information (NMI) that calculates the nonlinear relationship between different brain regions. We demonstrate our proposed approach using simulated data and then apply it to a dataset previously studied by Damaraju et al. This resting-state fMRI data included 151 schizophrenia patients and 163 age- and gender-matched healthy controls. We first decomposed these data using group independent component analysis (ICA) and yielded 47 functionally relevant intrinsic connectivity networks. Our analysis showed a modularized nonlinear relationship among brain functional networks that was particularly noticeable in the sensory and visual cortex. Interestingly, the modularity appears both meaningful and distinct from that revealed by the linear approach. Group analysis identified significant differences in explicitly nonlinear functional network connectivity (FNC) between schizophrenia patients and healthy controls, particularly in the visual cortex, with controls showing more nonlinearity (i.e., higher normalized mutual information between time courses with linear relationships removed) in most cases. Certain domains, including subcortical and auditory, showed relatively less nonlinear FNC (i.e., lower normalized mutual information), whereas links between the visual and other domains showed evidence of substantial nonlinear and modular properties. Overall, these results suggest that quantifying nonlinear dependencies of functional connectivity may provide a complementary and potentially important tool for studying brain function by exposing relevant variation that is typically ignored. Beyond this, we propose a method that captures both linear and nonlinear effects in a "boosted" approach. This method increases the sensitivity to group differences compared to the standard linear approach, at the cost of being unable to separate linear and nonlinear effects.

INTRODUCTION: Growing evidence suggests that impairment in rest-activity rhythms may be a risk factor for cognitive decline and impairment in the aging population. However, previous studies included only a limited set of rest-activity metrics and produced mixed findings. We studied a comprehensive set of parametric and nonparametric characteristics of rest-activity rhythms in relation to mild cognitive impairment (MCI) and probable dementia in a cohort of older women. METHODS: The prospective analysis included 763 women enrolled in two ancillary studies of the Womens Health Initiative (WHI): the WHI Memory Study-Epidemiology of Cognitive Health Outcomes and Objective Physical Activity and Cardiovascular Health studies. The association between accelerometry-based rest-activity parameters and centrally adjudicated MCI and probable dementia were determined using Cox regression models adjusted for sociodemographic characteristics, lifestyle factors, and comorbidities. RESULTS: Overall, the results support a prospective association between weakened rest-activity rhythms (e.g., reduced amplitude and overall rhythmicity) and adverse cognitive outcomes. Specifically, reduced overall rhythmicity (pseudo F statistic), lower amplitude and activity level (amplitude/relative amplitude, mesor, and activity level during active periods of the day [M10]), and later activity timing (acrophase and midpoint of M10) were associated with a higher risk for MCI and probable dementia. Women with lower amplitude and mesor also exhibited faster cognitive decline over follow-up. CONCLUSION: Weakened rest-activity rhythms may be predictive markers for cognitive decline, MCI, and dementia among older women.

Background: While functional connectivity is widely studied, there has been little work studying functional connectivity at different spatial scales. Likewise, the relationship of functional connectivity between spatial scales is unknown. Methods: We proposed an independent component analysis (ICA)-based approach to capture information at multiple-model orders (component numbers), and to evaluate functional network connectivity (FNC) both within and between model orders. We evaluated the approach by studying group differences in the context of a study of resting-state functional magnetic resonance imaging (rsfMRI) data collected from schizophrenia (SZ) individuals and healthy controls (HC). The predictive ability of FNC at multiple spatial scales was assessed using support vector machine-based classification. Results: In addition to consistent predictive patterns at both multiple-model orders and single-model orders, unique predictive information was seen at multiple-model orders and in the interaction between model orders. We observed that the FNC between model orders 25 and 50 maintained the highest predictive information between HC and SZ. Results highlighted the predictive ability of the somatomotor and visual domains both within and between model orders compared with other functional domains. Also, subcortical-somatomotor, temporal-somatomotor, and temporal-subcortical FNCs had relatively high weights in predicting SZ. Conclusions: In sum, multimodel order ICA provides a more comprehensive way to study FNC, produces meaningful and interesting results, which are applicable to future studies. We shared the spatial templates from this work at different model orders to provide a reference for the community, which can be leveraged in regression-based or fully automated (spatially constrained) ICA approaches. Impact statement Multimodel order independent component analysis (ICA) provides a comprehensive way to study brain functional network connectivity within and between multiple spatial scales, highlighting findings that would have been ignored in single-model order analysis. This work expands upon and adds to the relatively new literature on resting functional magnetic resonance imaging-based classification and prediction. Results highlighted the differentiating power of specific intrinsic connectivity networks on classifying brain disorders of schizophrenia patients and healthy participants, at different spatial scales. The spatial templates from this work provide a reference for the community, which can be leveraged in regression-based or fully automated ICA approaches.

Circadian rhythms (lasting approximately 24 h) control and entrain various physiological processes, ranging from neural activity and hormone secretion to sleep cycles and eating habits. Several studies have shown that time of day (TOD) is associated with human cognition and brain functions. In this study, utilizing a chronotype-based paradigm, we applied a graph theory approach on resting-state functional MRI (rs-fMRI) data to compare whole-brain functional network topology between morning and evening sessions and between morning-type (MT) and evening-type (ET) participants. Sixty-two individuals (31 MT and 31 ET) underwent two fMRI sessions, approximately 1 hour (morning) and 10 h (evening) after their wake-up time, according to their declared habitual sleep-wake pattern on a regular working day. In the global analysis, the findings revealed the effect of TOD on functional connectivity (FC) patterns, including increased small-worldness, assortativity, and synchronization across the day. However, we identified no significant differences based on chronotype categories. The study of the modular structure of the brain at mesoscale showed that functional networks tended to be more integrated with one another in the evening session than in the morning session. Local/regional changes were affected by both factors (i.e., TOD and chronotype), mostly in areas associated with somatomotor, attention, frontoparietal, and default networks. Furthermore, connectivity and hub analyses revealed that the somatomotor, ventral attention, and visual networks covered the most highly connected areas in the morning and evening sessions: the latter two were more active in the morning sessions, and the first was identified as being more active in the evening. Finally, we performed a correlation analysis to determine whether global and nodal measures were associated with subjective assessments across participants. Collectively, these findings contribute to an increased understanding of diurnal fluctuations in resting brain activity and highlight the role of TOD in future studies on brain function and the design of fMRI experiments.

Parental socialization may influence the development of children's autonomic nervous system (ANS), a key stress-response system. However, to date no quantitative synthesis of the literature linking parenting and child ANS physiology has been conducted. To address this gap, we conducted a pre-registered meta-analysis. A systematic review of the literature identified 103 studies (n = 13,044 participants) with available effect sizes describing the association between parenting and either parasympathetic nervous system (PNS) or sympathetic nervous system (SNS) activity in children. The overall analysis revealed non-significant associations between parenting and child ANS physiology on average. However, moderation analyses revealed a positive association between more positive parenting and higher resting PNS activity that was stronger when a study was experimental rather than correlational, and when the sample included children with a clinical condition. In conclusion, well-controlled experimental studies show that positive parenting is associated with the development of higher resting PNS activity, an effect that may be stronger among children who are at elevated developmental risk.

Resting state (RS) fMRI is now widely used for gaining insight into the organization of brain networks. Functional connectivity (FC) inferred from RS-fMRI is typically at macroscale, which is too coarse for much of the detail in cortical architecture. Here, we examined whether imaging RS at higher contrast and resolution could reveal cortical connectivity with columnar granularity. In longitudinal experiments (~1.5 years) in squirrel monkeys, we partitioned sensorimotor cortex using dense microelectrode mapping and then recorded RS with intrinsic signal optical imaging (RS-ISOI, 20 µm/pixel). FC maps were benchmarked against microstimulation-evoked activation and traced anatomical connections. These direct comparisons showed high correspondence in connectivity patterns across methods. The fidelity of FC maps to cortical connections indicates that granular details of network organization are embedded in RS. Thus, for recording RS, the field-of-view and effective resolution achieved with ISOI fills a wide gap between fMRI and invasive approaches (2-photon imaging, electrophysiology). RS-ISOI opens exciting opportunities for high resolution mapping of cortical networks in living animals.

Altered 24-hour rest-activity rhythms may be associated with cognitive impairment in older adults, but evidence from prospective studies is limited. Nonparametric methods were used to assess actigraphy-based activity patterns in 2 496 older men. Incident cognitive impairment was assessed 4 times over 12 years using the Modified Mini-Mental State Examination (3MS) and Trails B tests, self-reported medication use, and clinical diagnosis. The highest quartile (vs the lowest) of intradaily variability and the lowest quartiles (vs the highest) of interdaily stability and relative amplitude were associated with incident cognitive impairment (hazard ratio [95% confidence interval]: 1.82 [1.31-2.53], 1.36 [0.99-1.86], and 1.85 [1.33-2.56], respectively). A larger increase in intradaily variability over 7.5 years was associated with a greater subsequent decline in 3MS scores but not in Trails B performance. In conclusion, less stable and more variable rest-activity rhythms may represent early biomarkers of cognitive impairment in older men.

Chronic increases in pro-inflammatory cytokines in older adults, known as inflammaging, are an important risk factor for morbidity and mortality in the aging population. It has been suggested that circadian disruption may play a role in chronic inflammation, but there has been limited study that investigated the overall profile of 24-hour rest-activity rhythms in relation to inflammation using longitudinal data. In the Outcomes of Sleep Disorders in Older Men Study, we applied the extended cosine model to derive multiple rest-activity rhythm characteristics using multiday actigraphy, and examined their associations with 6 inflammatory markers (ie, C-reactive protein [CRP], interleukin 6 [IL-6], tumor necrosis factor alpha [TNF-α], tumor necrosis factor alpha soluble receptor II [TNF-α-sRII], interleukin-1β [IL-1β], interferon gamma [IFN-γ]) measured from fasting blood. We assessed both the cross-sectional association between rest-activity rhythms and inflammatory markers measured at baseline, and the prospective association between baseline rest-activity rhythms and changes in inflammatory markers over 3.5 years of follow-up. We found that multiple rest-activity characteristics, including lower amplitude and relative amplitude, and decreased overall rhythmicity, were associated with higher levels of CRP, IL-6, TNF-α, and TNF-α-sRII, but not IL-1β and IFN-γ at baseline. Moreover, the lowest quartile of these 3 rest-activity characteristics was associated with an approximately 2-fold increase in the odds of having elevated inflammation (ie, having 3 or more markers in the highest quartile) at baseline. However, we found little evidence supporting a relationship between rest-activity rhythm characteristics and changes in inflammatory markers. Future studies should clarify the dynamic relationship between rest-activity rhythms and inflammation in different populations, and evaluate the effects of improving rest-activity profiles on inflammation and related disease outcomes.

The Lactate Shuttle hypothesis is supported by a variety of techniques including mass spectrometry analytics following infusion of carbon-labeled isotopic tracers. However, there has been controversy over whether lactate tracers measure lactate (L) or pyruvate (P) turnover. Here, we review the analytical errors, use of inappropriate tissue and animal models, failure to consider L and P pool sizes in modeling results, inappropriate tracer and blood sampling sites, and failure to anticipate roles of heart and lung parenchyma on L⇔P interactions. With support from magnetic resonance spectroscopy (MRS) and immunocytochemistry, we conclude that carbon-labeled lactate tracers can be used to quantitate lactate fluxes.

Epilepsy and autism spectrum disorders (ASD) frequently show comorbidity, suggesting shared or overlapping neurobiological basis underlying these conditions. R104Q is the first mutation in the PRICKLE 1(PK1) gene that was discovered in human patients with progressive myoclonus epilepsy (PME). Subsequently, a number of mutations in the PK1 gene were shown to be associated with either epilepsy, autism, or both, as well as other developmental disorders. Using CRISPR-Cas9-mediated gene editing, we generated a PK1R104Q mouse line. The mutant mice showed reduced density of excitatory synapses in hippocampus and impaired interaction between PK1 and the repressor element 1(RE-1) silencing transcription factor (REST). They also displayed reduced seizure threshold, impaired social interaction, and cognitive functions. Taken together, the PK1R104Q mice display characteristic behavioral features similar to the key symptoms of epilepsy and ASD, providing a useful model for studying the molecular and neural circuit mechanisms underlying the comorbidity of epilepsy and ASD.

Mammals must carefully balance rest with other behaviors that influence fitness (e.g., foraging, finding a mate) while minimizing predation risk. However, factors influencing resting strategies and the degree to which resting strategies are driven by the activities of predators and/or prey remain largely unknown. Our goal was to examine how mammalian resting strategies varied with trophic level, body mass, and habitat. We reviewed findings from 127 publications and classified the resting strategies of terrestrial and aquatic mammalian species into three categories: social (e.g., resting in groups), temporal (e.g., resting during the day), or spatial (e.g., resting in burrows). Temporal strategies were most common (54% of cases), but the prevalence of strategies varied with body mass and among trophic levels. Specifically, lower trophic levels and smaller species such as rodents and lagomorphs used more spatial and social resting strategies, whereas top predators and larger species used mostly temporal resting strategies. Resting strategies also varied among habitat types (e.g., rainforest vs. grassland), but this was primarily because closely related species shared both habitats and resting strategies. Human presence also affected resting strategies at all trophic levels but most strongly influenced top predators through shifts in rest timing. Human-induced behavioral changes in rest patterns cascade to modify behaviors across multiple trophic levels. These findings advance our fundamental understanding of natural history and ecology in wild animals and provide a roadmap for future comparative studies.

Recent efforts to evaluate the heritability of the brain's functional connectome have predominantly focused on static connectivity. However, evaluating connectivity changes across time can provide valuable insight about the inherent dynamic nature of brain function. Here, the heritability of Human Connectome Project resting-state fMRI data was examined to determine whether there is a genetic basis for dynamic fluctuations in functional connectivity. The dynamic connectivity variance, in addition to the dynamic mean and standard static connectivity, was evaluated. Heritability was estimated using Accelerated Permutation Inference for the ACE (APACE), which models the additive genetic (h2), common environmental (c2), and unique environmental (e2) variance. Heritability was moderate (mean h2: dynamic mean = 0.35, dynamic variance = 0.45, and static = 0.37) and tended to be greater for dynamic variance compared to either dynamic mean or static connectivity. Further, heritability of dynamic variance was reliable across both sessions for several network connections, particularly between higher-order cognitive and visual networks. For both dynamic mean and static connectivity, similar patterns of heritability were found across networks. The findings support the notion that dynamic connectivity is genetically influenced. The flexibility of network connections, not just their strength, is a heritable endophenotype that may predispose trait behavior.

While functional neuroimaging studies typically focus on a particular paradigm to investigate network connectivity, the human brain appears to possess an intrinsic "trait" architecture that is independent of any given paradigm. We have previously proposed the use of "cross-paradigm connectivity (CPC)" to quantify shared connectivity patterns across multiple paradigms and have demonstrated the utility of such measures in clinical studies. Here, using generalizability theory and connectome fingerprinting, we examined the reliability, stability, and individual identifiability of CPC in a group of highly-sampled healthy traveling subjects who received fMRI scans with a battery of five paradigms across multiple sites and days. Compared with single-paradigm connectivity matrices, the CPC matrices showed higher reliability in connectivity diversity, lower reliability in connectivity strength, higher stability, and higher individual identification accuracy. All of these assessments increased as a function of number of paradigms included in the CPC analysis. In comparisons involving different paradigm combinations and different brain atlases, we observed significantly higher reliability, stability, and identifiability for CPC matrices constructed from task-only data (versus those from both task and rest data), and higher identifiability but lower stability for CPC matrices constructed from the Power atlas (versus those from the AAL atlas). Moreover, we showed that multi-paradigm CPC matrices likely reflect the brain's "trait" structure that cannot be fully achieved from single-paradigm data, even with multiple runs. The present results provide evidence for the feasibility and utility of CPC in the study of functional "trait" networks and offer some methodological implications for future CPC studies.

The body naturally and continuously secretes sweat for thermoregulation during sedentary and routine activities at rates that can reflect underlying health conditions, including nerve damage, autonomic and metabolic disorders, and chronic stress. However, low secretion rates and evaporation pose challenges for collecting resting thermoregulatory sweat for non-invasive analysis of body physiology. Here we present wearable patches for continuous sweat monitoring at rest, using microfluidics to combat evaporation and enable selective monitoring of secretion rate. We integrate hydrophilic fillers for rapid sweat uptake into the sensing channel, reducing required sweat accumulation time towards real-time measurement. Along with sweat rate sensors, we integrate electrochemical sensors for pH, Cl-, and levodopa monitoring. We demonstrate patch functionality for dynamic sweat analysis related to routine activities, stress events, hypoglycemia-induced sweating, and Parkinson's disease. By enabling sweat analysis compatible with sedentary, routine, and daily activities, these patches enable continuous, autonomous monitoring of body physiology at rest.

Sustained attention and working memory were improved in young adults after they engaged in a recently developed, closed-loop, digital meditation practice. Whether this type of meditation also has a sustained effect on dominant resting-state networks is currently unknown. In this study, we examined the resting brain states before and after a period of breath-focused, digital meditation training versus placebo using an electroencephalography (EEG) microstate approach. We found topographical changes in postmeditation rest, compared with baseline rest, selectively for participants who were actively involved in the meditation training and not in participants who engaged with an active, expectancy-match, placebo control paradigm. Our results suggest a reorganization of brain network connectivity after 6 weeks of intensive meditation training in brain areas, mainly including the right insula, the superior temporal gyrus, the superior parietal lobule, and the superior frontal gyrus bilaterally. These findings provide an opening for the development of a novel noninvasive treatment of neuropathological states by low-cost, breath-focused, digital meditation practice, which can be monitored by the EEG microstate approach.

Episodic memory depends on interactions between the hippocampus and interconnected neocortical regions. Here, using data-driven analyses of resting-state functional magnetic resonance imaging (fMRI) data, we identified the networks that interact with the hippocampus-the default mode network (DMN) and a "medial temporal network" (MTN) that included regions in the medial temporal lobe (MTL) and precuneus. We observed that the MTN plays a critical role in connecting the visual network to the DMN and hippocampus. The DMN could be further divided into 3 subnetworks: a "posterior medial" (PM) subnetwork comprised of posterior cingulate and lateral parietal cortices; an "anterior temporal" (AT) subnetwork comprised of regions in the temporopolar and dorsomedial prefrontal cortex; and a "medial prefrontal" (MP) subnetwork comprised of regions primarily in the medial prefrontal cortex (mPFC). These networks vary in their functional connectivity (FC) along the hippocampal long axis and represent different kinds of information during memory-guided decision-making. Finally, a Neurosynth meta-analysis of fMRI studies suggests new hypotheses regarding the functions of the MTN and DMN subnetworks, providing a framework to guide future research on the neural architecture of episodic memory.

Recent studies have shown higher resting-state vagally-mediated heart rate variability (vmHRV) to be related to greater memory retrieval. Research has not yet linked resting vmHRV with memory encoding and retrieval, as both are thought to play an important role in correctly distinguishing between true and false memories. The current study investigated this possible link in n = 71 undergraduate students. VmHRV was assessed during a 5-minute resting baseline period. Participants then completed the Deese-Roediger-McDermott (DRM) task, where they first viewed 6 word lists (12 words per list), and were later asked to identify previously shown words (true memories) and reject non-presented words. Results showed that participants with lower resting vmHRV were less able to discriminate true from false items. These data extend previous work on resting vmHRV and memory suggesting that resting vmHRV represents a psychophysiological pathway involved in both the proper encoding and retrieval of memories.

Difficulties with higher-order cognitive functions in youth are a potentially important vulnerability factor for the emergence of problematic behaviors and a range of psychopathologies. This study examined 2013 9-10 year olds in the first data release from the Adolescent Brain Cognitive Development 21-site consortium study in order to identify resting state functional connectivity patterns that predict individual-differences in three domains of higher-order cognitive functions: General Ability, Speed/Flexibility, and Learning/Memory. For General Ability scores in particular, we observed consistent cross-site generalizability, with statistically significant predictions in 14 out of 15 held-out sites. These results survived several tests for robustness including replication in split-half analysis and in a low head motion subsample. We additionally found that connectivity patterns involving task control networks and default mode network were prominently implicated in predicting differences in General Ability across participants. These findings demonstrate that resting state connectivity can be leveraged to produce generalizable markers of neurocognitive functioning. Additionally, they highlight the importance of task control-default mode network interconnections as a major locus of individual differences in cognitive functioning in early adolescence.

Early-life adversity (ELA) is associated with lifelong memory deficits, yet the responsible mechanisms remain unclear. We impose ELA by rearing rat pups in simulated poverty, assess hippocampal memory, and probe changes in gene expression, their transcriptional regulation, and the consequent changes in hippocampal neuronal structure. ELA rats have poor hippocampal memory and stunted hippocampal pyramidal neurons associated with ~140 differentially expressed genes. Upstream regulators of the altered genes include glucocorticoid receptor and, unexpectedly, the transcription factor neuron-restrictive silencer factor (NRSF/REST). NRSF contributes critically to the memory deficits because blocking its function transiently following ELA rescues spatial memory and restores the dendritic arborization of hippocampal pyramidal neurons in ELA rats. Blocking NRSF function in vitro augments dendritic complexity of developing hippocampal neurons, suggesting that NRSF represses genes involved in neuronal maturation. These findings establish important, surprising contributions of NRSF to ELA-induced transcriptional programming that disrupts hippocampal maturation and memory function.

ObjectiveDisruption of rest-activity rhythms is cross-sectionally associated with metabolic disorders, including type 2 diabetes, yet it remains unclear whether it predicts impaired glucose metabolism and homeostasis. The aim of this study is to examine the cross-sectional and prospective associations between rest-activity rhythm characteristics and glycemic measures in a cohort of older men.Research design and methodsBaseline rest-activity rhythms were derived from actigraphy with use of extended cosine model analysis. With subjects fasting, glucose, insulin, and HOMA of insulin resistance (HOMA-IR) were measured from blood at baseline and after ∼3.5 years. Type 2 diabetes was defined based on self-report, medication use, and fasting glucose.ResultsIn the cross-sectional analysis (n = 2,450), lower 24-h amplitude-to-mesor ratio (i.e., mean activity-adjusted rhythm amplitude) and reduced overall rhythmicity were associated with higher fasting insulin and HOMA-IR (all P trend < 0.0001), indicating increased insulin resistance. The odds of baseline type 2 diabetes were significantly higher among those in the lowest quartile of amplitude (Q1) (odds ratio [OR]Q1 vs. Q4 1.63 [95% CI 1.14, 2.30]) and late acrophase group (ORlate vs. normal 1.46 [95% CI 1.04, 2.04]). In the prospective analysis (n = 861), multiple rest-activity characteristics predicted a two- to threefold increase in type 2 diabetes risk, including a lower amplitude (ORQ1 vs. Q4 3.81 [95% CI 1.45, 10.00]) and amplitude-to-mesor ratio (OR 2.79 [95% CI 1.10, 7.07]), reduced overall rhythmicity (OR 3.49 [95% CI 1.34, 9.10]), and a late acrophase (OR 2.44 [1.09, 5.47]).ConclusionsRest-activity rhythm characteristics are associated with impaired glycemic metabolism and homeostasis and higher risk of incident type 2 diabetes.

BackgroundNegative emotional states contribute to cigarette smoking, and difficulties in regulating these states can hinder smoking cessation. Understanding the neural bases of these difficulties in smokers may facilitate development of novel therapies for Tobacco Use Disorder.MethodsThirty-seven participants (18 smokers, 19 nonsmokers; 16-21 years old) completed the Difficulties in Emotion Regulation Scale (DERS), which is comprised of 6 subscales (lack of emotional clarity, lack of emotional awareness, limited access to emotion regulation strategies, nonacceptance of emotional responses, difficulties engaging in goal-directed behaviors, and impulse control difficulties) that combine to provide a total score. Participants also underwent functional magnetic resonance imaging to determine resting-state functional connectivity of the amygdala. Separate ANOVAs were used to determine group differences in self-reports on the DERS. Voxel-wise linear mixed models were performed to determine whether group influenced relationships between whole-brain functional connectivity of the amygdala and scores on the DERS.ResultsCompared with nonsmokers, smokers reported greater difficulties in emotion regulation, denoted by higher total scores on the DERS. Group differences were observed on a subscale of lack of emotional clarity, but no other subscale differences on the DERS were observed. Nonsmokers exhibited a greater negative correlation than smokers between lack of emotional clarity scores and connectivity of the amygdala with the left inferior frontal gyrus. Finally, this amygdala-to-left inferior frontal gyrus connectivity was weaker in smokers than in nonsmokers.ConclusionsThese findings suggest that difficulties in emotion regulation in smokers are at least partially due to lack of emotional clarity. Given the role of the inferior frontal gyrus in understanding emotional states, strengthening connectivity between the amygdala and the inferior frontal gyrus may improve emotional clarity to help smokers regulate their negative emotions, thereby improving their ability to quit smoking.

When classifying major versus minor tone-scrambles (random sequences of pure tones), most listeners (70%) perform at chance while the remaining listeners perform nearly perfectly. The current study investigated whether inserting rests and cyclic sequences into the stimuli could heighten sensitivity in such tasks. In separate blocks, listeners classified tone-scramble variants as major versus minor ("3" task) or fourth versus tritone ("4" task). In three "Fast" variants, tones were played at 65 ms/tone as a continuous, random stream ("FR"), or with a rest after every fourth tone ("FRwR"), or as a repeating sequence of four tones with a rest after every fourth tone ("FCwR"). In the "Slow" variant, tones were played at 325 ms/tone in random order. In both the 3 and 4 tasks, performance was ordered from best to worst as follows: FRwR > FR > FCwR > Slow. Post hoc analysis revealed that performance was suppressed in the Slow and FCwR task-variants due to a powerful bias inclining listeners to respond "major" or "fourth" ("minor" or "tritone") if the 4-note sequence defining the stimulus ended on a high (low) note. Overall, the results indicate that inserting regular rests into random tone sequences heightens sensitivity to musical mode.

Although both resting and task-induced functional connectivity (FC) have been used to characterize the human brain and cognitive abilities, the potential of task-induced FCs in individualized prediction for out-of-scanner cognitive traits remains largely unexplored. A recent study Greene et al. (2018) predicted the fluid intelligence scores using FCs derived from rest and multiple task conditions, suggesting that task-induced brain state manipulation improved prediction of individual traits. Here, using a large dataset incorporating fMRI data from rest and 7 distinct task conditions, we replicated the original study by employing a different machine learning approach, and applying the method to predict two reading comprehension-related cognitive measures. Consistent with their findings, we found that task-based machine learning models often outperformed rest-based models. We also observed that combining multi-task fMRI improved prediction performance, yet, integrating the more fMRI conditions can not necessarily ensure better predictions. Compared with rest, the predictive FCs derived from language and working memory tasks were highlighted with more predictive power in predominantly default mode and frontoparietal networks. Moreover, prediction models demonstrated high stability to be generalizable across distinct cognitive states. Together, this replication study highlights the benefit of using task-based FCs to reveal brain-behavior relationships, which may confer more predictive power and promote the detection of individual differences of connectivity patterns underlying relevant cognitive traits, providing strong evidence for the validity and robustness of the original findings.

BACKGROUND: Heart rate is routinely measured as part of the clinical examination but is rarely acted upon unless it is well outside a population-based normal range. With wearable sensor technologies, heart rate can now be continuously measured, making it possible to accurately identify an individuals normal heart rate and potentially important variations in it over time. Our objective is to describe inter- and intra-individual variability in resting heart rate (RHR) collected over the course of two years using a wearable device, studying the variations of resting heart rate as a function of time of year, as well as individuals characteristics like age, sex, average sleep duration, and body mass index (BMI). METHODS AND FINDINGS: Our retrospective, longitudinal cohort study includes 92,457 de-identified individuals from the United States (all 50 states), who consistently-over at least 35 weeks in the period from March 2016 to February 2018, for at least 2 days per week, and at least 20 hours per day-wore a heart rate wrist-worn tracker. In this study, we report daily RHR and its association with age, BMI, sex, and sleep duration, and its variation over time. Individual daily RHR was available for a median of 320 days, providing nearly 33 million daily RHR values. We also explored the range in daily RHR variability between individuals, and the long- and short-term changes in the trajectory of an individuals daily RHR. Mean daily RHR was 65 beats per minute (bpm), with a range of 40 to 109 bpm among all individuals. The mean RHR differed significantly by age, sex, BMI, and average sleep duration. Time of year variations were also noted, with a minimum in July and maximum in January. For most subjects, RHR remained relatively stable over the short term, but 20% experienced at least 1 week in which their RHR fluctuated by 10 bpm or more. CONCLUSIONS: Individuals have a daily RHR that is normal for them but can differ from another individuals normal by as much as 70 bpm. Within individuals, RHR was much more consistent over time, with a small but significant seasonal trend, and detectable discrete and infrequent episodes outside their norms.

Electroconvulsive therapy (ECT) is a highly effective neuromodulatory intervention for treatment-resistant major depressive disorder (MDD). Presently, however, understanding of its neurophysiological effects remains incomplete. In the present study, we utilised resting-state electroencephalography (RS-EEG) to explore changes in functional connectivity, network topology, and spectral power elicited by an acute open-label course of ECT in a cohort of 23 patients with treatment-resistant MDD. RS-EEG was recorded prior to commencement of ECT and again within 48 h following each patient's final treatment session. Our results show that ECT was able to enhance connectivity within lower (delta and theta) frequency bands across subnetworks largely confined to fronto-central channels, while, conversely, more widespread subnetworks of reduced connectivity emerged within faster (alpha and beta) bands following treatment. Graph-based topological analyses revealed changes in measures of functional segregation (clustering coefficient), integration (characteristic path length), and small-world architecture following ECT. Finally, post-treatment enhancement of delta and theta spectral power was observed, which showed a positive association with the number of ECT sessions received. Overall, our findings indicate that RS-EEG can provide a sensitive measure of dynamic neural activity following ECT and highlight network-based analyses as a promising avenue for furthering mechanistic understanding of the effects of convulsive therapies.

The brain is a complex, multiscale dynamical system composed of many interacting regions. Knowledge of the spatiotemporal organization of these interactions is critical for establishing a solid understanding of the brain's functional architecture and the relationship between neural dynamics and cognition in health and disease. The possibility of studying these dynamics through careful analysis of neuroimaging data has catalyzed substantial interest in methods that estimate time-resolved fluctuations in functional connectivity (often referred to as "dynamic" or time-varying functional connectivity; TVFC). At the same time, debates have emerged regarding the application of TVFC analyses to resting fMRI data, and about the statistical validity, physiological origins, and cognitive and behavioral relevance of resting TVFC. These and other unresolved issues complicate interpretation of resting TVFC findings and limit the insights that can be gained from this promising new research area. This article brings together scientists with a variety of perspectives on resting TVFC to review the current literature in light of these issues. We introduce core concepts, define key terms, summarize controversies and open questions, and present a forward-looking perspective on how resting TVFC analyses can be rigorously and productively applied to investigate a wide range of questions in cognitive and systems neuroscience.

There has been a recent call for longitudinal cohort studies to track the physiological recovery of sport-related concussion (SRC) and its relationship with clinical recovery. Resting-state functional magnetic resonance imaging (rs-fMRI) has shown potential for detecting subtle changes in brain function after SRC. We investigated the effects of SRC on rs-fMRI metrics assessing local connectivity (regional homogeneity; REHO), global connectivity (average nodal strength), and the relative amplitude of slow oscillations of rs-fMRI (fractional amplitude of low-frequency fluctuations; fALFF). Athletes diagnosed with SRC (n = 92) completed visits with neuroimaging at 24-48 h post-injury (24 h), after clearance to begin the return-to-play (RTP) progression (asymptomatic), and 7 days following unrestricted RTP (post-RTP). Non-injured athletes (n = 82) completed visits yoked to the schedule of matched injured athletes and served as controls. Concussed athletes had elevated symptoms, worse neurocognitive performance, greater balance deficits, and elevated psychological symptoms at the 24-h visit relative to controls. These deficits were largely recovered by the asymptomatic visit. Concussed athletes still reported elevated psychological symptoms at the asymptomatic visit relative to controls. Concussed athletes also had elevated REHO in the right middle and superior frontal gyri at the 24-h visit that returned to normal levels by the asymptomatic visit. Additionally, REHO in these regions at 24 h predicted psychological symptoms at the asymptomatic visit in concussed athletes. Current results suggest that SRC is associated with an acute alteration in local connectivity that follows a similar time course as clinical recovery. Our results do not indicate strong evidence that concussion-related alterations in rs-fMRI persist beyond clinical recovery.

BackgroundUse of functional MRI (fMRI) in pre-surgical planning is a non-invasive method for pre-operative functional mapping for patients with brain tumors, especially tumors located near eloquent cortex. Currently, this practice predominantly involves task-based fMRI (T-fMRI). Resting state fMRI (RS-fMRI) offers an alternative with several methodological advantages. Here, we compare group-level analyses of RS-fMRI vs. T-fMRI as methods for language localization.PurposeTo contrast RS-fMRI vs. T-fMRI as techniques for localization of language function.MethodsWe analyzed data obtained in 35 patients who had both T-fMRI and RS-fMRI scans during the course of pre-surgical evaluation. The RS-fMRI data were analyzed using a previously trained resting-state network classifier. The T-fMRI data were analyzed using conventional techniques. Group-level results obtained by both methods were evaluated in terms of two outcome measures: (1) inter-subject variability of response magnitude and (2) sensitivity/specificity analysis of response topography, taking as ground truth previously reported maps of the language system based on intraoperative cortical mapping as well as meta-analytic maps of language task fMRI responses.ResultsBoth fMRI methods localized major components of the language system (areas of Broca and Wernicke) although not with equal inter-subject consistency. Word-stem completion T-fMRI strongly activated Broca's area but also several task-general areas not specific to language. RS-fMRI provided a more specific representation of the language system.ConclusionWe demonstrate several advantages of classifier-based mapping of language representation in the brain. Language T-fMRI activated task-general (i.e., not language-specific) functional systems in addition to areas of Broca and Wernicke. In contrast, classifier-based analysis of RS-fMRI data generated maps confined to language-specific regions of the brain.

BackgroundUnderstanding the interactions between increased insecticide resistance and resting behaviour patterns of malaria mosquitoes is important for planning of adequate vector control. This study was designed to investigate the resting behavior, host preference and rates of Plasmodium falciparum infection in relation to insecticide resistance of malaria vectors in different ecologies of western Kenya.MethodsAnopheles mosquito collections were carried out during the dry and rainy seasons in Kisian (lowland site) and Bungoma (highland site), both in western Kenya using pyrethrum spray catches (PSC), mechanical aspiration (Prokopack) for indoor collections, clay pots, pit shelter and Prokopack for outdoor collections. WHO tube bioassay was used to determine levels of phenotypic resistance of indoor and outdoor collected mosquitoes to deltamethrin. PCR-based molecular diagnostics were used for mosquito speciation, genotype for knockdown resistance mutations (1014S and 1014F) and to determine specific host blood meal origins. Enzyme-linked Immunosorbent Assay (ELISA) was used to determine mosquito sporozoite infections.ResultsAnopheles gambiae s.l. was the most predominant species (75%, n = 2706) followed by An. funestus s.l. (25%, n = 860). An. gambiae s.s hereafter (An. gambiae) accounted for 91% (95% CI: 89-93) and An. arabiensis 8% (95% CI: 6-9) in Bungoma, while in Kisian, An. arabiensis composition was 60% (95% CI: 55-66) and An. gambiae 39% (95% CI: 34-44). The resting densities of An. gambiae s.l and An. funestus were higher indoors than outdoor in both sites (An. gambiae s.l; F1, 655 = 41.928, p < 0.0001, An. funestus; F1, 655 = 36.555, p < 0.0001). The mortality rate for indoor and outdoor resting An. gambiae s.l F1 progeny was 37% (95% CI: 34-39) vs 67% (95% CI: 62-69) respectively in Bungoma. In Kisian, the mortality rate was 67% (95% CI: 61-73) vs 76% (95% CI: 71-80) respectively. The mortality rate for F1 progeny of An. funestus resting indoors in Bungoma was 32% (95% CI: 28-35). The 1014S mutation was only detected in indoor resitng An. arabiensis. Similarly, the 1014F mutation was present only in indoor resting An. gambiae. The sporozoite rates were highest in An. funestus followed by An. gambiae, and An. arabiensis resting indoors at 11% (34/311), 8% (47/618) and 4% (1/27) respectively in Bungoma. Overall, in Bungoma, the sporozoite rate for indoor resting mosquitoes was 9% (82/956) and 4% (8/190) for outdoors. In Kisian, the sporozoite rate was 1% (1/112) for indoor resting An. gambiae. None of the outdoor collected mosquitoes in Kisian tested positive for sporozoite infections (n = 73).ConclusionThe study reports high indoor resting densities of An. gambiae and An. funestus, insecticide resistance, and persistence of malaria transmission indoors regardless of the use of long-lasting insecticidal nets (LLINs). These findings underline the difficulties of controlling malaria vectors resting and biting indoors using the current interventions. Supplemental vector control tools and implementation of sustainable insecticide resistance management strategies are needed in western Kenya.

Cytoscape is one of the most successful network biology analysis and visualization tools, but because of its interactive nature, its role in creating reproducible, scalable, and novel workflows has been limited. We describe Cytoscape Automation (CA), which marries Cytoscape to highly productive workflow systems, for example, Python/R in Jupyter/RStudio. We expose over 270 Cytoscape core functions and 34 Cytoscape apps as REST-callable functions with standardized JSON interfaces backed by Swagger documentation. Independent projects to create and publish Python/R native CA interface libraries have reached an advanced stage, and a number of automation workflows are already published.

Individuals with schizophrenia and bipolar disorder show alterations in functional neural connectivity during rest. However, resting-state network (RSN) disruptions have not been systematically compared between the two disorders. Further, the impact of RSN disruptions on social cognition, a key determinant of functional outcome, has not been studied. Forty-eight individuals with schizophrenia, 46 with bipolar disorder, and 48 healthy controls completed resting-state functional magnetic resonance imaging. An atlas-based approach was used to examine functional connectivity within nine RSNs across the cortex. RSN connectivity was assessed via nonparametric permutation testing, and associations with performance on emotion perception, mentalizing, and emotion management tasks were examined. Group differences were observed in the medial and lateral visual networks and the sensorimotor network. Individuals with schizophrenia demonstrated reduced connectivity relative to healthy controls in all three networks. Individuals with bipolar disorder demonstrated reduced connectivity relative to controls in the medial visual network and connectivity within this network was significantly positively correlated with emotion management. In healthy controls, connectivity within the medial and lateral visual networks positively correlated with mentalizing. No significant correlations were found for either visual network in schizophrenia. Results highlight the role of altered early visual processing in social cognitive deficits in both schizophrenia and bipolar disorder. However, individuals with bipolar disorder appear to compensate for disrupted visual network connectivity on social cognitive tasks, whereas those with schizophrenia do not. The current study adds clarity on the neurophysiology underlying social cognitive deficits that result in impaired functioning in serious mental illness.

OBJECTIVE:High levels of occupational physical activity (OPA) increase heart rate, blood pressure (BP) and the risk of hypertension. Older workers may be more vulnerable to high levels of OPA due to age-related degeneration of the cardiovascular system and cardiorespiratory fitness. This study investigates the association of relative aerobic workload (RAW) with resting BP and examines if this relation is moderated by age. DESIGN:Cross-sectional epidemiological study. SETTING:Data were collected among employees of 15 Danish companies in the cleaning, manufacturing and transport sectors. PARTICIPANTS:2107 employees were invited for participation, of these 1087 accepted and 562 (42% female and 4% non-Westerns) were included in the analysis based on the criteria of being non-pregnant, no allergy to bandages, sufficient amount of heart rate data corresponding to ≥4 work hours per workday or 75% of average work hours, and no missing outcome and confounder values. PRIMARY AND SECONDARY OUTCOME MEASURES:The primary outcome measure was BP. RESULTS:Heart rate reserve was estimated from ambulatory 24-hour heart rate measures covering 2.5 workdays per participant (SD 1.0 day). Age significantly moderated the association between RAW and BP. Mean intensity and duration of high RAW (≥30% heart rate reserve) showed positive associations with diastolic BP and negative associations with pulse pressure (PP) among participants ≥47 years old. Tendencies towards negative associations between RAW and BP were seen among participants

Objective: To examine resting and postprandial peripheral protease activity in healthy controls and individuals with type 2 diabetes mellitus (T2DM) and pre-T2DM. Methods: Individuals with T2DM or pre-T2DM and healthy controls (mean age 55.8 years) were studied before and for a span of 300 minutes following a single high-calorie McDonald's breakfast. Metalloproteases-2/-9 (MMP-2/-9), elastase, and trypsin activities were assessed in whole blood before and following the meal using a novel high-precision electrophoretic platform. Also assessed were circulating levels of inflammatory biomarkers and insulin receptor density on peripheral blood mononuclear cells (PBMCs) in relationship to protease activity. Results: Premeal MMP-2/-9 and elastase activity levels in T2DM and in pre-T2DM participants were significantly elevated as compared to controls. The T2DM group showed a significant increase in elastase activity 15 minutes after the meal; elastase activity continued to increase to the 30-minute time point (p

When acute stress is experienced immediately after memory encoding (i.e., post-encoding stress) it can significantly impact subsequent memory for that event. For example, recent work has suggested that post-encoding stress occurring in a different context from encoding impairs memory. However, the neural processes underlying these effects are poorly understood. We aimed to expand this understanding by conducting an analysis of resting functional connectivity in the period following post-encoding stress that occurred in a different context than encoding, using seed regions in the medial temporal lobes known for their roles in memory. In the current study of 44 males randomized to stress (n = 23) or control (n = 21) groups, we found that stress increased cortisol, impaired recollection of neutral materials, and altered functional connectivity with medial temporal lobe regions. Although stress did not significantly alter hippocampus-amygdala functional connectivity, relative to participants in the control group, participants in the post-encoding stress group showed lower functional connectivity between the hippocampus and a region with a peak in the superior temporal gyrus. Across participants in both groups, functional connectivity between these regions was related to greater increases in cortisol, and it was also inversely related to recollection of neutral materials. In contrast, the stress group showed greater parahippocampal cortex functional connectivity with a region in the left middle temporal gyrus than the control group. Moreover, greater functional connectivity between the parahippocampal cortex and the observed cluster in the middle temporal gyrus was associated with greater cortisol changes from pre- to post-manipulation, but was not related to differences in memory. The results show that post-encoding stress can alter the resting-state functional connectivity between the medial temporal lobe and neocortex, which may help explain how stress impacts memory.

A mechanistic understanding of the pathology of psychiatric disorders has been hampered by extensive heterogeneity in biology, symptoms, and behavior within diagnostic categories that are defined subjectively. We investigated whether leveraging individual differences in information-processing impairments in patients with post-traumatic stress disorder (PTSD) could reveal phenotypes within the disorder. We found that a subgroup of patients with PTSD from two independent cohorts displayed both aberrant functional connectivity within the ventral attention network (VAN) as revealed by functional magnetic resonance imaging (fMRI) neuroimaging and impaired verbal memory on a word list learning task. This combined phenotype was not associated with differences in symptoms or comorbidities, but nonetheless could be used to predict a poor response to psychotherapy, the best-validated treatment for PTSD. Using concurrent focal noninvasive transcranial magnetic stimulation and electroencephalography, we then identified alterations in neural signal flow in the VAN that were evoked by direct stimulation of that network. These alterations were associated with individual differences in functional fMRI connectivity within the VAN. Our findings define specific neurobiological mechanisms in a subgroup of patients with PTSD that could contribute to the poor response to psychotherapy.

Alzheimer's disease (AD) is the most common type of dementia in the elderly with no effective treatment currently. Recent studies of noninvasive neuroimaging, resting-state functional magnetic resonance imaging (rs-fMRI) with graph theoretical analysis have shown that patients with AD and mild cognitive impairment (MCI) exhibit disrupted topological organization in large-scale brain networks. In previous work, it is a common practice to threshold such networks. However, it is not only difficult to make a principled choice of threshold values, but also worse is the discard of potential important information. To address this issue, we propose a threshold-free feature by integrating a prior persistent homology-based topological feature (the zeroth Betti number) and a newly defined connected component aggregation cost feature to model brain networks over all possible scales. We show that the induced topological feature (Integrated Persistent Feature) follows a monotonically decreasing convergence function and further propose to use its slope as a concise and persistent brain network topological measure. We apply this measure to study rs-fMRI data from the Alzheimer's Disease Neuroimaging Initiative and compare our approach with five other widely used graph measures across five parcellation schemes ranging from 90 to 1,024 region-of-interests. The experimental results demonstrate that the proposed network measure shows more statistical power and stronger robustness in group difference studies in that the absolute values of the proposed measure of AD are lower than MCI and much lower than normal controls, providing empirical evidence for decreased functional integration in AD dementia and MCI.

Neuropsychiatric symptoms (NPS) are common in Alzheimer's disease (AD)-associated neurodegeneration. However, NPS lack a consistent relationship with AD pathology. It is unknown whether any common neural circuits can link these clinically disparate while mechanistically similar features with AD pathology. Here, we explored the neural circuits of NPS in AD-associated neurodegeneration using multivariate pattern analysis (MVPA) of resting-state functional MRI data. Data from 98 subjects (70 amnestic mild cognitive impairment and 28 AD subjects) were obtained. The top 10 regions differentiating symptom presence across NPS were identified, which were mostly the fronto-limbic regions (medial prefrontal cortex, caudate, etc.). These 10 regions' functional connectivity classified symptomatic subjects across individual NPS at 69.46-81.27%, and predicted multiple NPS (indexed by Neuropsychiatric Symptom Questionnaire-Inventory) and AD pathology (indexed by baseline and change of beta-amyloid/pTau ratio) all above 70%. Our findings suggest a fronto-limbic dominated neural circuit that links multiple NPS and AD pathology. With further examination of the structural and pathological changes within the circuit, the circuit may shed light on linking behavioral disturbances with AD-associated neurodegeneration.

BackgroundLarge-scale brain networks such as the default mode network (DMN) are often disrupted in Alzheimer's disease (AD). Numerous studies have examined DMN functional connectivity in those with mild cognitive impairment (MCI), a presumed AD precursor, to discover a biomarker of AD risk. Prior reviews were qualitative or limited in scope or approach.ObjectiveWe aimed to systematically and quantitatively review DMN resting state fMRI studies comparing MCI and healthy comparison (HC) groups.MethodsPubMed was searched for relevant articles. Study characteristics were abstracted and the number of studies showing no group difference or hyper- versus hypo-connnectivity in MCI was tallied. A voxel-wise (ES-SDM) meta-analysis was conducted to identify regional group differences.ResultsQualitatively, our review of 57 MCI versus HC comparisons suggests substantial inconsistency; 9 showed no group difference, 8 showed MCI > HC and 22 showed HC > MCI across the brain, and 18 showed regionally-mixed directions of effect. The meta-analysis of 31 studies revealed areas of significant hypo- and hyper-connectivity in MCI, including hypoconnectivity in the posterior cingulate cortex/precuneus (z = -3.1, p

Localizing neurologic function within the brain remains a significant challenge in clinical neurosurgery. Invasive mapping with direct electrocortical stimulation currently is the clinical gold standard but is impractical in young or cognitively delayed patients who are unable to reliably perform tasks. Resting state functional magnetic resonance imaging non-invasively identifies resting state networks without the need for task performance, hence, is well suited to pediatric patients. We compared sensorimotor network localization by resting state fMRI to cortical stimulation sensory and motor mapping in 16 pediatric patients aged 3.1 to 18.6 years. All had medically refractory epilepsy that required invasive electrographic monitoring and stimulation mapping. The resting state fMRI data were analyzed using a previously trained machine learning classifier that has previously been evaluated in adults. We report comparable functional localization by resting state fMRI compared to stimulation mapping. These results provide strong evidence for the utility of resting state functional imaging in the localization of sensorimotor cortex across a wide range of pediatric patients.

ObjectivesIndependently, physical activity (PA), sedentary behavior (SB), and sleep are related to the development and progression of chronic diseases. Less is known about how rest-activity behaviors cluster within individuals and how rest-activity behavior profiles relate to health. In this study we aimed to investigate if adult women cluster into profiles based on how they accumulate rest-activity behavior (including accelerometer-measured PA, SB, and sleep), and if participant characteristics and health outcomes differ by profile membership.MethodsA convenience sample of 372 women (mean age 55.38 + 10.16) were recruited from four US cities. Participants wore ActiGraph GT3X+ accelerometers on the hip and wrist for a week. Total daily minutes in moderate-to-vigorous PA (MVPA) and percentage of wear-time spent in SB was estimated from the hip device. Total sleep time (hours/minutes) and sleep efficiency (% of in bed time asleep) were estimated from the wrist device. Latent profile analysis (LPA) was performed to identify clusters of participants based on accumulation of the four rest-activity variables. Adjusted ANOVAs were conducted to explore differences in demographic characteristics and health outcomes across profiles.ResultsRest-activity variables clustered to form five behavior profiles: Moderately Active Poor Sleepers (7%), Highly Actives (9%), Inactives (41%), Moderately Actives (28%), and Actives (15%). The Moderately Active Poor Sleepers (profile 1) had the lowest proportion of whites (35% vs 78-91%, p < .001) and college graduates (28% vs 68-90%, p = .004). Health outcomes did not vary significantly across all rest-activity profiles.ConclusionsIn this sample, women clustered within daily rest-activity behavior profiles. Identifying 24-hour behavior profiles can inform intervention population targets and innovative behavioral goals of multiple health behavior interventions.

The human brain is a dynamic system, where communication between spatially distinct areas facilitates complex cognitive functions and behaviors. How information transfers between brain regions and how it gives rise to human cognition, however, are unclear. In this article, using resting-state functional magnetic resonance imaging (fMRI) data from 783 healthy adults in the Human Connectome Project (HCP) dataset, we map the brain's directed information flow architecture through a Granger-Geweke causality prism. We demonstrate that the information flow profiles in the general population primarily involve local exchanges within specialized functional systems, long-distance exchanges from the dorsal brain to the ventral brain, and top-down exchanges from the higher-order systems to the primary systems. Using an information flow map discovered from 550 subjects, the individual directed information flow profiles can significantly predict cognitive flexibility scores in 233 novel individuals. Our results provide evidence for directed information network architecture in the cerebral cortex, and suggest that features of the information flow configuration during rest underpin cognitive ability in humans.

In magnetoencephalography (MEG) research there are a variety of inversion methods to transform sensor data into estimates of brain activity. Each new inversion scheme is generally justified against a specific simulated or task scenario. The choice of this scenario will however have a large impact on how well the scheme performs. We describe a method with minimal selection bias to quantify algorithm performance using human resting state data. These recordings provide a generic, heterogeneous, and plentiful functional substrate against which to test different MEG recording and reconstruction approaches. We used a Hidden Markov model to spatio-temporally partition data into self-similar dynamic states. To test the anatomical precision that could be achieved, we then inverted these data onto libraries of systematically distorted subject-specific cortical meshes and compared the quality of the fit using cross validation and a Free energy metric. This revealed which inversion scheme was able to identify the least distorted (most accurate) anatomical models, and allowed us to quantify an upper bound on the mean anatomical distortion accordingly. We used two resting state datasets, one recorded with head-casts and one without. In the head-cast data, the Empirical Bayesian Beamformer (EBB) algorithm showed the best mean anatomical discrimination (3.7 mm) compared with Minimum Norm/LORETA (6.0 mm) and Multiple Sparse Priors (9.4 mm). This pattern was replicated in the second (conventional dataset) although with a marginally poorer (non-significant) prediction of the missing (cross-validated) data. Our findings suggest that the abundant resting state data now commonly available could be used to refine and validate MEG source reconstruction methods and/or recording paradigms.

New techniques to investigate functional network connectivity in resting state functional magnetic resonance imaging data have recently emerged. One novel approach, called meta-state analysis, goes beyond the mere cross-correlation of time courses of distinct brain areas and explores temporal dynamism in more detail, allowing for connectivity states to overlap in time and capturing global dynamic behavior. Previous studies have shown that patients with chronic schizophrenia exhibit reduced neural dynamism compared to healthy controls, but it is not known whether these alterations extend to earlier phases of the illness. In this study, we analyzed individuals at clinical high-risk (CHR, n = 53) for developing psychosis, patients in an early stage of schizophrenia (ESZ, n = 58), and healthy controls (HC, n = 70). ESZ individuals exhibit reduced neural dynamism across all domains compared to HC. CHR individuals also show reduced neural dynamism but only in 2 out of 4 domains investigated. Overall, meta-state analysis adds information about dynamic fluidity of functional connectivity.

OBJECTIVE:To examine rest-activity circadian rhythm (RAR) and cognitive decline in older men. DESIGN:Longitudinal. SETTING:Osteoporotic Fractures in Men (MrOS) and ancillary Outcomes of Sleep Disorders in Men (MrOS Sleep) studies. PARTICIPANTS:MrOS and MrOS Sleep participants (N=2,754; mean age 76.0 ± 5.3). MEASUREMENTS:The Modified Mini-Mental State examination (3MS) was used to assess cognition at baseline (2003-05) and follow-up examinations (2005-06 and 2007-09). Wrist actigraphy was used to measure 24-hour activity counts at baseline. RAR variables included amplitude (strength of activity rhythm), mesor (mean activity level), pseudo F-statistic (overall circadian rhythm robustness), and acrophase (time of daily peak activity). RESULTS:After an average of 3.4 ± 0.5 years, men with lower amplitudes, mesors, and pseudo F-statistics had greater decline in 3MS performance (amplitude: -0.7 points Q1 vs -0.5 points Q4, p

The hippocampus replays experiences during quiet rest periods, and this replay benefits subsequent memory. A critical open question is how memories are prioritized for this replay. We used functional magnetic resonance imaging (fMRI) pattern analysis to track item-level replay in the hippocampus during an awake rest period after participants studied 15 objects and completed a memory test. Objects that were remembered less well were replayed more during the subsequent rest period, suggesting a prioritization process in which weaker memories-memories most vulnerable to forgetting-are selected for replay. In a second session 12 hours later, more replay of an object during a rest period predicted better subsequent memory for that object. Replay predicted memory improvement across sessions only for participants who slept during that interval. Our results provide evidence that replay in the human hippocampus prioritizes weakly learned information, predicts subsequent memory performance, and relates to memory improvement across a delay with sleep.