When response variability increases neural network robustness to synaptic noise

Degradation-aware neural imputation: Advancing decoding stability in brain machine interfaces

Neural signal degradation poses a significant challenge in maintaining stable performance when decoding motor tasks using multiunit activity (MUA) and local field potential (LFP) signals in the implantable brain machine interface (iBMI) applications. Effective methods for imputing degraded or missing signals are essential to restore neural signal integrity, thereby improving decoding accuracy and system robustness over long-term recordings with fluctuating signal quality. This study introduces a confidence-weighted Bayesian linear regression (CW-BLR) approach to impute neural signals affected by degradation, enhancing the robustness and consistency of decoding. The performance of CW-BLR was compared to traditional methods-mean imputation (Mean-imp) and Gaussian-mixture-model-based expectation-maximization (GMM-EM)-using a kernel-sliced inverse regression (kSIR) decoder to evaluate decoding outcomes. Four Wistar rats were trained to perform a forelimb-reaching task while neural activity (MUA and LFPs) was recorded over 27 days. CW-BLR imputed signals degraded during days 8-27. Decoding performance was evaluated using kSIR and compared with Mean-imp and GMM-EM. CW-BLR demonstrated superior performance by effectively preserving both temporal and spatial dependencies within the neural signals. CW-BLR-imputed data significantly improved decoding accuracy over traditional imputation methods, with the kSIR decoder showing consistently higher performance, particularly in maintaining signal quality from the degraded period. CW-BLR offers a robust and effective imputation framework for iBMI applications, addressing signal degradation challenges and maintaining accurate decoding over prolonged recordings. By utilizing confidence-based quality metrics, CW-BLR surpasses traditional methods, providing stable neural decoding across fluctuating signal quality scenarios.

Extreme signal amplitude events in neuromagnetic oscillations reveal brain aging processing across adulthood

Introduction

Neurophysiological activity, as noninvasively captured by electro- and magnetoencephalography (EEG and MEG), demonstrates complex temporal fluctuations approximated by typical variations around the mean values and rare events with large amplitude. The statistical properties of these extreme and rare events in neurodynamics may reflect the limits or capacity of the brain as a complex system in information processing. However, the exact role of these extreme neurodynamic events in ageing, and their spectral and spatial patterns remain elusive. Our study hypothesized that ageing would be associated with frequency specific alterations in the brain's tendency to synchronize large ensembles of neurons and to produce extreme events.

Methods

To identify spatio-spectral patterns of these age-related changes in extreme neurodynamics, we examined resting-state MEG recordings from a large cohort of adults (n = 645), aged 18 to 89. We characterized extreme neurodynamics by computing sample skewness and kurtosis, and used Partial Least Squares to test for differences across age groups.

Results

Our findings revealed that each canonical frequency, from theta to lower gamma, displayed unique spatial patterns of either age-related increases, decreases, or both in the brain's tendency to produce extreme neuromagnetic events.

Discussion

Our study introduces a novel neuroimaging framework for understanding ageing through the extreme and rare events of the neurophysiological activity, offering more sensitivity than typical comparative approaches.

THE PROMISE OF INVESTIGATING NEURAL VARIABILITY IN PSYCHIATRIC DISORDERS

The synergy of psychiatry and neuroscience has recently sought to identify biomarkers that can diagnose mental health disorders, predict their progression, and forecast treatment efficacy. However, biomarkers have achieved limited success to date, potentially due to a narrow focus on specific aspects of brain signals. This highlights a critical need for methodologies that can fully exploit the potential of neuroscience to transform psychiatric practice. In recent years, there is emerging evidence of the ubiquity and importance of moment-to-moment neural variability for brain function. Single-neuron recordings and computational models have demonstrated the significance of variability even at the microscopic level. Concurrently, studies involving healthy humans using neuroimaging recording techniques have strongly indicated that neural variability, once dismissed as undesirable noise, is an important substrate for cognition. Given the cognitive disruption in several psychiatric disorders, neural variability is a promising biomarker in this context and careful consideration of design choices is necessary to advance the field. This review provides an overview of the significance and substrates of neural variability across different recording modalities and spatial scales. We also review the existing evidence supporting its relevance in the study of psychiatric disorders. Finally, we advocate for future research to investigate neural variability within disorder-relevant, task-based paradigms and longitudinal designs. Supported by computational models of brain activity, this framework holds the potential for advancing precision psychiatry in a powerful and experimentally feasible manner.

Localized electrocortical activity as a function of single-leg squat phases and its relationship to knee frontal plane stability

This study investigated differences in electroencephalography (EEG) activity within motor-related brain areas during three phases of a single-leg squat (SLS)-i.e., descending, holding, and ascending phases. Specifically, utilizing advanced magnetic resonance imaging guided EEG source localization techniques and markerless motion capture technology, we explored the interplay between concurrently recorded lower-extremity biomechanics and brain activity. Among the phases of a nondominant leg SLS, differences in contralateral brain activity (right hemisphere) were found in the activity of the precentral gyrus, the postcentral gyrus, and the sensory motor area. Alternatively, during the dominant SLS leg, differences among the three SLS phases in contralateral brain activity were fewer. Hemispheric dependent brain activity also significantly correlated with participants' knee valgus angle range of motion (right hemisphere) and peak knee valgus angles (left hemisphere). In addition to the novel brain and biomechanical findings, this study sheds light on the technical feasibility of recording EEG during complex multi-joint movements and its potential applications in understanding sensorimotor behavior.

Systematic Review and Meta-Analysis: Do White Noise or Pink Noise Help With Task Performance in Youth With Attention-Deficit/Hyperactivity Disorder or With Elevated Attention Problems?

OBJECTIVE

Public interest in the potential benefits of white, pink, and brown noise for attention-deficit/hyperactivity disorder (ADHD) has recently mushroomed. White noise contains all frequencies of noise and sounds like static; pink or brown noise has more power in the lower frequencies and may sound, respectively, like rain or a waterfall. This meta-analysis evaluated effects on laboratory tasks in individuals with ADHD or elevated ADHD symptoms.

METHOD

Eligible studies reported on participants with diagnosis of ADHD or elevated symptoms of ADHD who were assessed in a randomized trial using laboratory tasks intended to measure aspects of attention or academic work involving attention or executive function while exposed to white, pink, and brown noise and compared with a low/no noise condition. Two authors independently reviewed and screened studies for eligibility. A random-effects meta-analysis was conducted with preplanned moderator analyses of age, diagnostic status, and task type. Publication bias was evaluated. The GRADE tool was used to assess certainty of the evidence. Sensitivity analyses were conducted to evaluate robustness.

RESULTS

Studies of children and college-age young adults with ADHD or ADHD symptoms (k = 13, N = 335) yielded a small but statistically significant benefit of white and pink noise on task performance (g = 0.249, 95% CI [0.135, 0.363], p < .0001). No studies of brown noise were identified. Heterogeneity was minimal, and moderators were nonsignificant; results survived sensitivity tests, and no publication bias was identified. In non-ADHD comparison groups (k = 11, N = 335), white and pink noise had a negative effect (g = -0.212, 95% CI [-0.355, -0.069], p = .0036).

CONCLUSION

White and pink noise provide a small benefit on laboratory attention tasks for individuals with ADHD or high ADHD symptoms, but not for non-ADHD individuals. This article addresses theoretical implications, cautions, risks, and limitations.

PLAIN LANGUAGE SUMMARY

Public interest in the potential benefits of white, pink, and brown noise exposure for enhancing task performance for individuals with attention-deficit/hyperactivity disorder (ADHD) has increased substantially. This systematic review and meta-analysis included 13 studies with 335 participants and found that white/pink noise improved cognitive performance for children and young adults with ADHD or significant ADHD symptoms. In contrast, white/pink noise impaired cognitive performance for individuals without ADHD. Positive effects of noise were small, but these results point to a possible low-cost, low-risk intervention that may benefit youth with ADHD. Additional studies are needed to confirm effects and identify safe and appropriate decibel levels. The potential detrimental effects for individuals without ADHD also requires further study.

STUDY PREREGISTRATION INFORMATION

White Noise for ADHD: A Systematic Review And Meta-analysis; https://www.crd.york.ac.uk/prospero; CRD42023393992.

Age-Dependent Spatial Patterns of Brain Noise in fMRI Series

Functional Magnetic Resonance Imaging (fMRI) serves as a unique non-invasive tool for investigating brain function by analyzing blood oxygenation level-dependent (BOLD) series. These signals result from the complex interplay between deterministic and stochastic components underpinning biological brain activity. In this context, the quantification of the stochastic component, here defined as brain noise, is challenging without making assumptions on the deterministic dynamics. Leveraging on Approximate Entropy, in this study we present a methodological framework aimed to estimate intrinsic stochastic brain dynamics through fMRI data analysis without making assumption on the deterministic model. We estimated brain noise from fMRI series of 200 participants from the publicly available Cam-CAN dataset, aiming to quantify the amount of stochastic dynamics in different brain regions. Moreover, we hypothesize that a functional relationship exists between intrinsic brain noise and subject's age. Results indicate that a significant part - approximately 18% to 60% - of the fMRI signal power can be attributed to the intrinsic stochastic dynamics within the brain, and a linear augmentation is reported in association with the maturation process. These findings underscore the physiological importance of characterizing neural noise and its unique distributions across various brain regions.

Stochastic brain dynamics exhibits differential regional distribution and maturation-related changes

Functional magnetic resonance imaging (fMRI) is a powerful non-invasive method for studying brain function by analyzing blood oxygenation level-dependent (BOLD) signals. These signals arise from intricate interplays of deterministic and stochastic biological elements. Quantifying the stochastic part is challenging due to its reliance on assumptions about the deterministic segment. We present a methodological framework to estimate intrinsic stochastic brain dynamics in fMRI data without assuming deterministic dynamics. Our approach utilizes Approximate Entropy and its behavior in noisy series to identify and characterize dynamical noise in unobservable fMRI dynamics. Applied to extensive fMRI datasets (645 Cam-CAN, 1086 Human Connectome Project subjects), we explore lifelong maturation of intrinsic brain noise. Findings indicate 10% to 60% of fMRI signal power is due to intrinsic stochastic brain elements, varying by age. These components demonstrate a physiological role of neural noise which shows a distinct distributions across brain regions and increase linearly during maturation.

Decreased long-range temporal correlations in the resting-state functional magnetic resonance imaging blood-oxygen-level-dependent signal reflect motor sequence learning up to 2 weeks following training

Decreased long-range temporal correlations (LRTC) in brain signals can be used to measure cognitive effort during task execution. Here, we examined how learning a motor sequence affects long-range temporal memory within resting-state functional magnetic resonance imaging signal. Using the Hurst exponent (HE), we estimated voxel-wise LRTC and assessed changes over 5 consecutive days of training, followed by a retention scan 12 days later. The experimental group learned a complex visuomotor sequence while a complementary control group performed tightly matched movements. An interaction analysis revealed that HE decreases were specific to the complex sequence and occurred in well-known motor sequence learning associated regions including left supplementary motor area, left premotor cortex, left M1, left pars opercularis, bilateral thalamus, and right striatum. Five regions exhibited moderate to strong negative correlations with overall behavioral performance improvements. Following learning, HE values returned to pretraining levels in some regions, whereas in others, they remained decreased even 2 weeks after training. Our study presents new evidence of HE's possible relevance for functional plasticity during the resting-state and suggests that a cortical subset of sequence-specific regions may continue to represent a functional signature of learning reflected in decreased long-range temporal dependence after a period of inactivity.

Neural variability in three major psychiatric disorders

Across the major psychiatric disorders (MPDs), a shared disruption in brain physiology is suspected. Here we investigate the neural variability at rest, a well-established behavior-relevant marker of brain function, and probe its basis in gene expression and neurotransmitter receptor profiles across the MPDs. We recruited 219 healthy controls and 279 patients with schizophrenia, major depressive disorder, or bipolar disorders (manic or depressive state). The standard deviation of blood oxygenation level-dependent signal (SDBOLD) obtained from resting-state fMRI was used to characterize neural variability. Transdiagnostic disruptions in SDBOLD patterns and their relationships with clinical symptoms and cognitive functions were tested by partial least-squares correlation. Moving beyond the clinical sample, spatial correlations between the observed patterns of SDBOLD disruption and postmortem gene expressions, Neurosynth meta-analytic cognitive functions, and neurotransmitter receptor profiles were estimated. Two transdiagnostic patterns of disrupted SDBOLD were discovered. Pattern 1 is exhibited in all diagnostic groups and is most pronounced in schizophrenia, characterized by higher SDBOLD in the language/auditory networks but lower SDBOLD in the default mode/sensorimotor networks. In comparison, pattern 2 is only exhibited in unipolar and bipolar depression, characterized by higher SDBOLD in the default mode/salience networks but lower SDBOLD in the sensorimotor network. The expression of pattern 1 related to the severity of clinical symptoms and cognitive deficits across MPDs. The two disrupted patterns had distinct spatial correlations with gene expressions (e.g., neuronal projections/cellular processes), meta-analytic cognitive functions (e.g., language/memory), and neurotransmitter receptor expression profiles (e.g., D2/serotonin/opioid receptors). In conclusion, neural variability is a potential transdiagnostic biomarker of MPDs with a substantial amount of its spatial distribution explained by gene expressions and neurotransmitter receptor profiles. The pathophysiology of MPDs can be traced through the measures of neural variability at rest, with varying clinical-cognitive profiles arising from differential spatial patterns of aberrant variability.

Exploiting noise as a resource for computation and learning in spiking neural networks

Networks of spiking neurons underpin the extraordinary information-processing capabilities of the brain and have become pillar models in neuromorphic artificial intelligence. Despite extensive research on spiking neural networks (SNNs), most studies are established on deterministic models, overlooking the inherent non-deterministic, noisy nature of neural computations. This study introduces the noisy SNN (NSNN) and the noise-driven learning (NDL) rule by incorporating noisy neuronal dynamics to exploit the computational advantages of noisy neural processing. The NSNN provides a theoretical framework that yields scalable, flexible, and reliable computation and learning. We demonstrate that this framework leads to spiking neural models with competitive performance, improved robustness against challenging perturbations compared with deterministic SNNs, and better reproducing probabilistic computation in neural coding. Generally, this study offers a powerful and easy-to-use tool for machine learning, neuromorphic intelligence practitioners, and computational neuroscience researchers.

Resting-state brain signal complexity discriminates young healthy APOE e4 carriers from non-e4 carriers

It is well established that the e4 allele of the APOE gene is associated with impaired brain functionality and cognitive decline in humans at elder age. However, it is controversial whether and how the APOE e4 allele is associated with superior brain function among young healthy individuals, thus indicates a case of antagonistic pleiotropy of APOE e4 allele. Signal complexity is a critical aspect of brain activity that has been associated with brain function. In this study, the multiscale entropy (MSE) of resting-state EEG signals among a sample of young healthy adults (N = 260) as an indicator of brain signal complexity was investigated. It was of interest whether MSE differs across APOE genotype groups while age and education level were controlled for and whether the APOE genotype effect on MSE interacts with MSE time scale, as well as EEG recording condition. Results of linear mixed models indicate overall larger MSE in APOE e4 carriers. This genotype-dependent difference is larger at high as compared with low time scales. The interaction effect between APOE genotype and recording condition indicates increased between-state MSE change in young healthy APOE e4 carriers as compared with non-carriers. Because higher complexity is commonly taken to be associated with better cognitive functioning, the present results complement previous findings and therefore point to a pleiotropic spectrum of the APOE gene polymorphism.

Altered EEG variability on different time scales in participants with autism spectrum disorder: an exploratory study

One of the great challenges in psychiatry is finding reliable biomarkers that may allow for more accurate diagnosis and treatment of patients. Neural variability received increasing attention in recent years as a potential biomarker. In the present explorative study we investigated temporal variability in visually evoked EEG activity in a cohort of 16 adult participants with Asperger Syndrome (AS) and 19 neurotypical (NT) controls. Participants performed a visual oddball task using fine and coarse checkerboard stimuli. We investigated various measures of neural variability and found effects on multiple time scales. (1) As opposed to the previous studies, we found reduced inter-trial variability in the AS group compared to NT. (2) This effect builds up over the entire course of a 5-min experiment and (3) seems to be based on smaller variability of neural background activity in AS compared to NTs. The here reported variability effects come with considerably large effect sizes, making them promising candidates for potentially reliable biomarkers in psychiatric diagnostics. The observed pattern of universality across different time scales and stimulation conditions indicates trait-like effects. Further research with a new and larger set of participants are thus needed to verify or falsify our findings.

The modulatory effect of adaptive task-switching training on resting-state neural network dynamics in younger and older adults

With increasing life expectancy and active aging, it becomes crucial to investigate methods which could compensate for generally detected cognitive aging processes. A promising candidate is adaptive cognitive training, during which task difficulty is adjusted to the participants' performance level to enhance the training and potential transfer effects. Measuring intrinsic brain activity is suitable for detecting possible distributed training-effects since resting-state dynamics are linked to the brain's functional flexibility and the effectiveness of different cognitive processes. Therefore, we investigated if adaptive task-switching training could modulate resting-state neural dynamics in younger (18-25 years) and older (60-75 years) adults (79 people altogether). We examined spectral power density on resting-state EEG data for measuring oscillatory activity, and multiscale entropy for detecting intrinsic neural complexity. Decreased coarse timescale entropy and lower frequency band power as well as increased fine timescale entropy and higher frequency band power revealed a shift from more global to local information processing with aging before training. However, cognitive training modulated these age-group differences, as coarse timescale entropy and lower frequency band power increased from pre- to post-training in the old-training group. Overall, our results suggest that cognitive training can modulate neural dynamics even when measured outside of the trained task.

Lost Dynamics and the Dynamics of Loss: Longitudinal Compression of Brain Signal Variability is Coupled with Declines in Functional Integration and Cognitive Performance

Reduced moment-to-moment blood oxygen level-dependent (BOLD) signal variability has been consistently linked to advanced age and poorer cognitive performance, showing potential as a functional marker of brain aging. To date, however, this promise has rested exclusively on cross-sectional comparisons. In a sample of 74 healthy adults, we provide the first longitudinal evidence linking individual differences in BOLD variability, age, and performance across multiple cognitive domains over an average period of 2.5 years. As expected, those expressing greater loss of BOLD variability also exhibited greater decline in cognition. The fronto-striato-thalamic system emerged as a core neural substrate for these change-change associations. Preservation of signal variability within regions of the fronto-striato-thalamic system also cohered with preservation of functional integration across regions of this system, suggesting that longitudinal maintenance of "local" dynamics may require across-region communication. We therefore propose this neural system as a primary target in future longitudinal studies on the neural substrates of cognitive aging. Given that longitudinal change-change associations between brain and cognition are notoriously difficult to detect, the presence of such an association within a relatively short follow-up period bolsters the promise of brain signal variability as a viable, experimentally sensitive probe for studying individual differences in human cognitive aging.

Absolute and relative variability changes of the resting state brain rhythms from childhood and adolescence to young adulthood

The present report aimed to analyze the possible relationship of spontaneous EEG power variability across epochs in individual subjects (absolute and relative) with age. For this purpose, the resting state EEG of a sample of 258 healthy subjects (6-29 years old) in open and closed eyes experimental conditions were recorded. The power spectral density (PSD) was calculated from 0.5-45 Hz. Three electrodes with the highest PSD in each band were selected, and linear and inverse regression of the mean, standard deviation (SD), and coefficient of variation CV of the PSD vs age were computed. The results showed that the EEG absolute variability (SD) decreases with age, and in contrast, the relative variability (CV) increased, except for high frequencies in which it remains stable during maturation. We conclude that the variability in the EEG PSD when is not influenced by the mean PSD tends to increase from childhood and adolescence to young adulthood. Present results complement the extensive literature on changes of EEG power in different brain rhythms with the changes in EEG power variability during maturation.

The role of attention in the relationship between early life stress and depression

Early life stress (ELS) can be very harmful to an individual's wellbeing and brain development. It is well established that childhood maltreatment is a significant risk factor for depression. ELS is positively correlated with depressive symptoms both in major depression disorder patients and healthy individuals, but the cognitive and neural mechanisms underlying this association are still unclear. In the present study, we calculate the within/between-network connectivity in 528 college students, and Pearson correlation was performed to investigate the relationship between network measures and ELS. Additionally, the same method was applied to verify these results in another sample. Finally, mediation analysis was performed to explore the cognitive and neural mechanisms regarding the association between ELS and depression. Correlation analysis indicated that ELS was positively correlated with the within-network connectivity of the ventral attention network (VAN), the dorsal attention network (DAN), the salience network (SN), the somatosensory network (SMN) and the between-network connectivity of ventral attention network-dorsal attention network (VAN-DAN), ventral attention network- somatosensory network (VAN-SMN), and ventral attention network-visual network (VAN-VN). Validation results indicated that ELS is associated with the within-network connectivity of VAN and DAN. Mediation analysis revealed that attention bias and the within-network connectivity of VAN could mediated the relationship between ELS and depression. Both behavioral and neural evidence emphasize ELS's influence on individual's emotion attention. Furthermore, the present study also provides two possible mediation models to explain the potential mechanisms behind the relationship between ELS and depression.

Electrocortical dynamics differentiate athletes exhibiting low- and high- ACL injury risk biomechanics

Anterior cruciate ligament (ACL) injuries are physically and emotionally debilitating for athletes,while motor and biomechanical deficits that contribute to ACL injury have been identified, limited knowledge about the relationship between the central nervous system (CNS) and biomechanical patterns of motion has impeded approaches to optimize ACL injury risk reduction strategies. In the current study it was hypothesized that high-risk athletes would exhibit altered temporal dynamics in their resting state electrocortical activity when compared to low-risk athletes. Thirty-eight female athletes performed a drop vertical jump (DVJ) to assess their biomechanical risk factors related to an ACL injury. The athletes' electrocortical activity was also recorded during resting state in the same visit as the DVJ assessment. Athletes were divided into low- and high-risk groups based on their performance of the DVJ. Recurrence quantification analysis was used to quantify the temporal dynamics of two frequency bands previously shown to relate to sensorimotor and attentional control. Results revealed that high-risk participants showed more deterministic electrocortical behavior than the low-risk group in the frontal theta and central/parietal alpha-2 frequency bands. The more deterministic resting state electrocortical dynamics for the high-risk group may reflect maladaptive neural behavior-excessively stable deterministic patterning that makes transitioning among functional task-specific networks more difficult-related to attentional control and sensorimotor processing neural regions.

Regional increases in brain signal variability are associated with pain intensity reductions following repeated eccentric exercise bouts

BACKGROUND

Traditional pain interventions limit fluctuations in pain sensation, which may paradoxically impair endogenous pain modulatory systems (EPMS). However, controlled exposures to clinically relevant pain (e.g. delayed onset muscle soreness [DOMS]) may build capacity in the EPMS. Emerging evidence suggests that regional signal variability (RSV) may be an important indicator of efficiency and modulatory capacity within brain regions. This study sought to determine the role of RSV in both susceptibility to and trainability of pain response following repeated DOMS inductions.

METHODS

Baseline and follow-up resting-state fMRI was performed on 12 healthy volunteers ~40 days apart. Between scanning visits, participants received four weekly DOMS inductions in alternating elbow flexors and were supplied seven days of post-induction pain ratings. Voxel-wise standard deviation of signal intensity was calculated to measure RSV. Associations among DOMS-related pain and RSV were assessed with regression. Relationships among baseline and change measurements were probed (i.e. susceptibility to DOMS; trainability following multiple inductions).

RESULTS

Significant association between baseline RSV in left middle frontal gyrus (MFG) and right cerebellum and reductions in DOMS-related pain unpleasantness were detected. Furthermore, increases in RSV were associated with reduced DOMS pain intensity (left lingual gyrus, right MTG, left MTG, left precuneus) and unpleasantness (left MTG, right SFG).

DISCUSSION

Findings suggest that RSV may be an indicator of EPMS resilience and responsivity to training, as well as an indicator that is responsive to training. Involved regions underlie cognitive, affective and representation processes. Results further clarify the potential role of RSV as an indicator of pain modulation and resilience.

SIGNIFICANCE

Regional signal variability may be an important indicator of endogenous pain modulatory system responsivity to training following repeated bouts of clinically relevant pain and may in fact be responsive to training itself.

Opposite effects of dopamine and serotonin on resting-state networks: review and implications for psychiatric disorders

Alterations in brain intrinsic activity-as organized in resting-state networks (RSNs) such as sensorimotor network (SMN), salience network (SN), and default-mode network (DMN)-and in neurotransmitters signaling-such as dopamine (DA) and serotonin (5-HT)-have been independently detected in psychiatric disorders like bipolar disorder and schizophrenia. Thus, the aim of this work was to investigate the relationship between such neurotransmitters and RSNs in healthy, by reviewing the relevant work on this topic and performing complementary analyses, in order to better understand their physiological link, as well as their alterations in psychiatric disorders. According to the reviewed data, neurotransmitters nuclei diffusively project to subcortical and cortical regions of RSNs. In particular, the dopaminergic substantia nigra (SNc)-related nigrostriatal pathway is structurally and functionally connected with core regions of the SMN, whereas the ventral tegmental area (VTA)-related mesocorticolimbic pathway with core regions of the SN. The serotonergic raphe nuclei (RNi) connections involve regions of the SMN and DMN. Coherently, changes in neurotransmitters activity impact the functional configuration and level of activity of RSNs, as measured by functional connectivity (FC) and amplitude of low-frequency fluctuations/temporal variability of BOLD signal. Specifically, DA signaling is associated with increase in FC and activity in the SMN (hypothetically via the SNc-related nigrostriatal pathway) and SN (hypothetically via the VTA-related mesocorticolimbic pathway), as well as concurrent decrease in FC and activity in the DMN. By contrast, 5-HT signaling (via the RNi-related pathways) is associated with decrease in SMN activity along with increase in DMN activity. Complementally, our empirical data showed a positive correlation between SNc-related FC and SMN activity, whereas a negative correlation between RNi-related FC and SMN activity (along with tilting of networks balance toward the DMN). According to these data, we hypothesize that the activity of neurotransmitter-related neurons synchronize the low-frequency oscillations within different RSNs regions, thus affecting the baseline level of RSNs activity and their balancing. In our model, DA signaling favors the predominance of SMN-SN activity, whereas 5-HT signaling favors the predominance of DMN activity, manifesting in distinct behavioral patterns. In turn, alterations in neurotransmitters signaling (or its disconnection) may favor a correspondent functional reorganization of RSNs, manifesting in distinct psychopathological states. The here suggested model carries important implications for psychiatric disorders, providing novel and well testable hypotheses especially on bipolar disorder and schizophrenia.

Altered Brain Entropy as a predictor of antidepressant response in major depressive disorder

OBJECTIVE

To explore the alterations and value of brain entropy (BEN) in major depressive disorder (MDD).

METHODS

85 MDD patients and 45 matched normal controls were recruited. MDD was diagnosed based on Diagnostic and Statistical Manual of Mental Disorders, 4th ed (DSM-IV). Symptoms of depression were assessed using the Hamilton depression scale-24 (HAMD-24) at baseline and follow-up (after 8-week treatment). All subjects underwent functional magnetic resonance imaging (fMRI) scans at baseline, and 30 MDD patients completed scans at 8th week. Whole brain BEN maps at each session was calculated using the BEN mapping toolbox.

RESULTS

The 8-week antidepressant treatment improved symptoms for all MDD patients. As compared to normal controls, MDD showed reduced BEN in medial orbitofrontal cortex (MOFC)/subgenual cingulate cortex (sgACC), but increased BEN in motor cortex (MC). In MDD patients, higher baseline BEN in MOFC/sgACC and lower baseline BEN in temporal cortex (TC) were associated with higher baseline HAMD scores; higher baseline BEN in MOFC/sgACC and hippocampus were associated with greater reduction of HAMD scores after 8-week medication; greater reduction of HAMD scores after 8-week medication was correlated with greater BEN reduction in MOFC/sgACC but were correlated with less BEN reduction in MC, TC, fusiform gyrus (FG) and visual cortex (VC).

CONCLUSION

The results highlighted MOFC/sgACC BEN as a potential marker for the prediction of MDD diagnosis and treatment effect. MDD might have increased MOFC/sgACC BEN but reduced BEN in visual and sensory-motor circuits corresponding to the imbalanced emotional and sensory-motor information processing. Reversing this unbalanced BEN would improve disease conditions in MDD.

Cerebral blood flow and heart rate variability predict fatigue severity in patients with chronic fatigue syndrome

Prolonged, disabling fatigue is the hallmark of chronic fatigue syndrome (CFS). Previous neuroimaging studies have provided evidence for nervous system involvement in CFS etiology, including perturbations in brain structure/function. In this arterial spin labeling (ASL) MRI study, we examined variability in cerebral blood flow (CBFV) and heart rate (HRV) in 28 women: 14 with CFS and 14 healthy controls. We hypothesized that CBFV would be reduced in individuals with CFS compared to healthy controls, and that increased CBFV and HRV would be associated with lower levels of fatigue in affected individuals. Our results provided support for these hypotheses. Although no group differences in CBFV or HRV were detected, greater CBFV and more HRV power were both associated with lower fatigue symptom severity in individuals with CFS. Exploratory statistical analyses suggested that protective effects of high CBFV were greatest in individuals with low HRV. We also found novel evidence of bidirectional association between the very high frequency (VHF) band of HRV and CBFV. Taken together, the results of this study suggest that CBFV and HRV are potentially important measures of adaptive capacity in chronic illnesses like CFS. Future studies should address these measures as potential therapeutic targets to improve outcomes and reduce symptom severity in individuals with CFS.

Generalization of learned responses in the mormyrid electrosensory lobe

Appropriate generalization of learned responses to new situations is vital for adaptive behavior. We provide a circuit-level account of generalization in the electrosensory lobe (ELL) of weakly electric mormyrid fish. Much is already known in this system about a form of learning in which motor corollary discharge signals cancel responses to the uninformative input evoked by the fish's own electric pulses. However, for this cancellation to be useful under natural circumstances, it must generalize accurately across behavioral regimes, specifically different electric pulse rates. We show that such generalization indeed occurs in ELL neurons, and develop a circuit-level model explaining how this may be achieved. The mechanism involves regularized synaptic plasticity and an approximate matching of the temporal dynamics of motor corollary discharge and electrosensory inputs. Recordings of motor corollary discharge signals in mossy fibers and granule cells provide direct evidence for such matching.

Opposing patterns of neuronal variability in the sensorimotor network mediate cyclothymic and depressive temperaments

Affective temperaments have been described since the early 20th century and may play a central role in psychiatric illnesses, such as bipolar disorder (BD). However, the neuronal basis of temperament is still unclear. We investigated the relationship of temperament with neuronal variability in the resting state signal-measured by fractional standard deviation (fSD) of Blood-Oxygen-Level Dependent signal-of the different large-scale networks, that is, sensorimotor network (SMN), along with default-mode, salience and central executive networks, in standard frequency band (SFB) and its sub-frequencies slow4 and slow5, in a large sample of healthy subject (HC, n = 109), as well as in the various temperamental subgroups (i.e., cyclothymic, hyperthymic, depressive, and irritable). A replication study on an independent dataset of 121 HC was then performed. SMN fSD positively correlated with cyclothymic z-score and was significantly increased in the cyclothymic temperament compared to the depressive temperament subgroups, in both SFB and slow4. We replicated our findings in the independent dataset. A relationship between cyclothymic temperament and neuronal variability, an index of intrinsic neuronal activity, in the SMN was found. Cyclothymic and depressive temperaments were associated with opposite changes in the SMN variability, resembling changes previously described in manic and depressive phases of BD. These findings shed a novel light on the neural basis of affective temperament and also carry important implications for the understanding of a potential dimensional continuum between affective temperaments and BD, on both psychological and neuronal levels.

Dynamics of spontaneous alpha activity correlate with language ability in young children

Early childhood is a period of tremendous growth in both language ability and brain maturation. To understand the dynamic interplay between neural activity and spoken language development, we used resting-state EEG recordings to explore the relation between alpha oscillations (7-10 Hz) and oral language ability in 4- to 6-year-old children with typical development (N = 41). Three properties of alpha oscillations were investigated: a) alpha power using spectral analysis, b) flexibility of the alpha frequency quantified via the oscillation's moment-to-moment fluctuations, and c) scaling behavior of the alpha oscillator investigated via the long-range temporal correlation in the alpha-amplitude time course. All three properties of the alpha oscillator correlated with children's oral language abilities. Higher language scores were correlated with lower alpha power, greater flexibility of the alpha frequency, and longer temporal correlations in the alpha-amplitude time course. Our findings demonstrate a cognitive role of several properties of the alpha oscillator that has largely been overlooked in the literature.

Differential Effects of Simulated Cortical Network Lesions on Synchrony and EEG Complexity

Brain function has been proposed to arise as a result of the coordinated activity between distributed brain areas. An important issue in the study of brain activity is the characterization of the synchrony among these areas and the resulting complexity of the system. However, the variety of ways to define and, hence, measure brain synchrony and complexity has sometimes led to inconsistent results. Here, we study the relationship between synchrony and commonly used complexity estimators of electroencephalogram (EEG) activity and we explore how simulated lesions in anatomically based cortical networks would affect key functional measures of activity. We explored this question using different types of neural network lesions while the brain dynamics was modeled with a time-delayed set of 66 Kuramoto oscillators. Each oscillator modeled a region of the cortex (node), and the connectivity and spatial location between different areas informed the creation of a network structure (edges). Each type of lesion consisted on successive lesions of nodes or edges during the simulation of the neural dynamics. For each type of lesion, we measured the synchrony among oscillators and three complexity estimators (Higuchi's Fractal Dimension, Sample Entropy and Lempel-Ziv Complexity) of the simulated EEGs. We found a general negative correlation between EEG complexity metrics and synchrony but Sample Entropy and Lempel-Ziv showed a positive correlation with synchrony when the edges of the network were deleted. This suggests an intricate relationship between synchrony of the system and its estimated complexity. Hence, complexity seems to depend on the multiple states of interaction between the oscillators of the system. Our results can contribute to the interpretation of the functional meaning of EEG complexity.

Assessing the mean strength and variations of the time-to-time fluctuations of resting-state brain activity

The time-to-time fluctuations (TTFs) of resting-state brain activity as captured by resting-state fMRI (rsfMRI) have been repeatedly shown to be informative of functional brain structures and disease-related alterations. TTFs can be characterized by the mean and the range of successive difference. The former can be measured with the mean squared successive difference (MSSD), which is mathematically similar to standard deviation; the latter can be calculated by the variability of the successive difference (VSD). The purpose of this study was to evaluate both the resting state-MSSD and VSD of rsfMRI regarding their test-retest stability, sensitivity to brain state change, as well as their biological meanings. We hypothesized that MSSD and VSD are reliable in resting brain; both measures are sensitive to brain state changes such as eyes-open compared to eyes-closed condition; both are predictive of age. These hypotheses were tested with three rsfMRI datasets and proven true, suggesting both MSSD and VSD as reliable and useful tools for resting-state studies.

Contrasting variability patterns in the default mode and sensorimotor networks balance in bipolar depression and mania

Depressive and manic phases in bipolar disorder show opposite constellations of affective, cognitive, and psychomotor symptoms. At a neural level, these may be related to topographical disbalance between large-scale networks, such as the default mode network (DMN) and sensorimotor network (SMN). We investigated topographical patterns of variability in the resting-state signal-measured by fractional SD (fSD) of the BOLD signal-of the DMN and SMN (and other networks) in two frequency bands (Slow5 and Slow4) with their ratio and clinical correlations in depressed (n = 20), manic (n = 20), euthymic (n = 20) patients, and healthy controls (n = 40). After controlling for global signal changes, the topographical balance between the DMN and SMN, specifically in the lowest frequency band, as calculated by the Slow5 fSD DMN/SMN ratio, was significantly increased in depression, whereas the same ratio was significantly decreased in mania. Additionally, Slow5 variability was increased in the DMN and decreased in the SMN in depressed patients, whereas the opposite topographical pattern was observed in mania. Finally, the Slow5 fSD DMN/SMN ratio correlated positively with clinical scores of depressive symptoms and negatively with those of mania. Results were replicated in a smaller independent bipolar disorder sample. We demonstrated topographical abnormalities in frequency-specific resting-state variability in the balance between DMN and SMN with opposing patterns in depression and mania. The Slow5 DMN/SMN ratio was tilted toward the DMN in depression but was shifted toward the SMN in mania. The Slow5 fSD DMN/SMN pattern could constitute a state-biomarker in diagnosis and therapy.

Annual research review: Reaction time variability in ADHD and autism spectrum disorders: measurement and mechanisms of a proposed trans-diagnostic phenotype

BACKGROUND

Intraindividual variability in reaction time (RT) has received extensive discussion as an indicator of cognitive performance, a putative intermediate phenotype of many clinical disorders, and a possible trans-diagnostic phenotype that may elucidate shared risk factors for mechanisms of psychiatric illnesses.

SCOPE AND METHODOLOGY

Using the examples of attention deficit hyperactivity disorder (ADHD) and autism spectrum disorders (ASD), we discuss RT variability. We first present a new meta-analysis of RT variability in ASD with and without comorbid ADHD. We then discuss potential mechanisms that may account for RT variability and statistical models that disentangle the cognitive processes affecting RTs. We then report a second meta-analysis comparing ADHD and non-ADHD children on diffusion model parameters. We consider how findings inform the search for neural correlates of RT variability.

FINDINGS

Results suggest that RT variability is increased in ASD only when children with comorbid ADHD are included in the sample. Furthermore, RT variability in ADHD is explained by moderate to large increases (d = 0.63-0.99) in the ex-Gaussian parameter τ and the diffusion parameter drift rate, as well as by smaller differences (d = 0.32) in the diffusion parameter of nondecision time. The former may suggest problems in state regulation or arousal and difficulty detecting signal from noise, whereas the latter may reflect contributions from deficits in motor organization or output. The neuroimaging literature converges with this multicomponent interpretation and also highlights the role of top-down control circuits.

CONCLUSION

We underscore the importance of considering the interactions between top-down control, state regulation (e.g., arousal), and motor preparation when interpreting RT variability and conclude that decomposition of the RT signal provides superior interpretive power and suggests mechanisms convergent with those implicated using other cognitive paradigms. We conclude with specific recommendations for the field for next steps in the study of RT variability in neurodevelopmental disorders.

Moment-to-moment brain signal variability: a next frontier in human brain mapping?

Neuroscientists have long observed that brain activity is naturally variable from moment-to-moment, but neuroimaging research has largely ignored the potential importance of this phenomenon. An emerging research focus on within-person brain signal variability is providing novel insights, and offering highly predictive, complementary, and even orthogonal views of brain function in relation to human lifespan development, cognitive performance, and various clinical conditions. As a result, brain signal variability is evolving as a bona fide signal of interest, and should no longer be dismissed as meaningless noise when mapping the human brain.

Convergence analyses on on-line weight noise injection-based training algorithms for MLPs

Injecting weight noise during training is a simple technique that has been proposed for almost two decades. However, little is known about its convergence behavior. This paper studies the convergence of two weight noise injection-based training algorithms, multiplicative weight noise injection with weight decay and additive weight noise injection with weight decay. We consider that they are applied to multilayer perceptrons either with linear or sigmoid output nodes. Let w(t) be the weight vector, let V(w) be the corresponding objective function of the training algorithm, let α >; 0 be the weight decay constant, and let μ(t) be the step size. We show that if μ(t)→ 0, then with probability one E[||w(t)||2(2)] is bound and lim(t) → ∞ ||w(t)||2 exists. Based on these two properties, we show that if μ(t)→ 0, Σtμ(t)=∞, and Σtμ(t)(2) <; ∞, then with probability one these algorithms converge. Moreover, w(t) converges with probability one to a point where ∇wV(w)=0.

The cognitive neuroscience of ageing

The availability of neuroimaging technology has spurred a marked increase in the human cognitive neuroscience literature, including the study of cognitive ageing. Although there is a growing consensus that the ageing brain retains considerable plasticity of function, currently measured primarily by means of functional MRI, it is less clear how age differences in brain activity relate to cognitive performance. The field is also hampered by the complexity of the ageing process itself and the large number of factors that are influenced by age. In this Review, current trends and unresolved issues in the cognitive neuroscience of ageing are discussed.

The modulation of BOLD variability between cognitive states varies by age and processing speed

Increasing evidence suggests that brain variability plays a number of important functional roles for neural systems. However, the relationship between brain variability and changing cognitive demands remains understudied. In the current study, we demonstrate experimental condition-based modulation in brain variability using functional magnetic resonance imaging. Within a sample of healthy younger and older adults, we found that blood oxygen level-dependent signal variability was an effective discriminator between fixation and external cognitive demand. Across a number of regions, brain variability increased broadly on task compared with fixation, particularly in younger and faster performing adults. Conversely, older and slower performing adults exhibited fewer changes in brain variability within and across experimental conditions and brain regions, indicating a reduction in variability-based neural specificity. Increases in brain variability on task may represent a more complex neural system capable of greater dynamic range between brain states, as well as an enhanced ability to efficiently process varying and unexpected external stimuli. The current results help establish the developmental and performance correlates of state-to-state brain variability-based transitions and offer a new line of inquiry in the study of rest versus task modes in the human brain.

On the noise-enhancing ability of stochastic Hodgkin-Huxley neuron systems

Recently noise has been shown to be useful in enhancing neuron sensitivity by stochastic resonance. In this study, in order to measure the noise-enhancing factor (NEF), a nonlinear stochastic model is introduced for the Hodgkin-Huxley (HH) neuron system with synaptic noise input stimulation and channel noises in the sodium and potassium channels. The enhancing factor of the HH neuron system is measured from the point of view of the noise-exploiting level of nonlinear stochastic H(infinity) signal processing. Since the nonlinear stochastic-enhancing measure problem of HH neuron systems requires a solution for the difficulty presented by the Hamilton Jacobi inequality (HJI), a fuzzy interpolation of locally linearized systems is employed to simplify the nonlinear noise-enhancing problems by solving only a set of linear matrix inequalities. The NEF of the HH neuron system is found to be related to the locations of eigenvalues of linearized HH neuron systems and can be estimated through the H(infinity) signal processing method. Based on a stochastic fuzzy linearized HH neuron system, we found that channel noises are enhanced by the active eigenvalues of ionic channels while synaptic noises are attenuated by the passive eigenvalues of synaptic process.

Effects of multiplicative power law neural noise in visual information processing

The human visual system is intrinsically noisy. The benefits of internal noise as part of visual code are controversial. Here the information-theoretic properties of multiplicative (i.e. signal-dependent) neural noise are investigated. A quasi-linear communication channel model is presented. The model shows that multiplicative power law neural noise promotes the minimum information transfer after efficient coding. It is demonstrated that Weber's law and the human contrast sensitivity function arise on the basis of minimum transfer of information and power law neural noise. The implications of minimum information transfer in self-organized neural networks and weakly coupled neurons are discussed.

Objective functions of online weight noise injection training algorithms for MLPs

Injecting weight noise during training has been a simple strategy to improve the fault tolerance of multilayer perceptrons (MLPs) for almost two decades, and several online training algorithms have been proposed in this regard. However, there are some misconceptions about the objective functions being minimized by these algorithms. Some existing results misinterpret that the prediction error of a trained MLP affected by weight noise is equivalent to the objective function of a weight noise injection algorithm. In this brief, we would like to clarify these misconceptions. Two weight noise injection scenarios will be considered: one is based on additive weight noise injection and the other is based on multiplicative weight noise injection. To avoid the misconceptions, we use their mean updating equations to analyze the objective functions. For injecting additive weight noise during training, we show that the true objective function is identical to the prediction error of a faulty MLP whose weights are affected by additive weight noise. It consists of the conventional mean square error and a smoothing regularizer. For injecting multiplicative weight noise during training, we show that the objective function is different from the prediction error of a faulty MLP whose weights are affected by multiplicative weight noise. With our results, some existing misconceptions regarding MLP training with weight noise injection can now be resolved.

On the selection of weight decay parameter for faulty networks

The weight-decay technique is an effective approach to handle overfitting and weight fault. For fault-free networks, without an appropriate value of decay parameter, the trained network is either overfitted or underfitted. However, many existing results on the selection of decay parameter focus on fault-free networks only. It is well known that the weight-decay method can also suppress the effect of weight fault. For the faulty case, using a test set to select the decay parameter is not practice because there are huge number of possible faulty networks for a trained network. This paper develops two mean prediction error (MPE) formulae for predicting the performance of faulty radial basis function (RBF) networks. Two fault models, multiplicative weight noise and open weight fault, are considered. Our MPE formulae involve the training error and trained weights only. Besides, in our method, we do not need to generate a huge number of faulty networks to measure the test error for the fault situation. The MPE formulae allow us to select appropriate values of decay parameter for faulty networks. Our experiments showed that, although there are small differences between the true test errors (from the test set) and the MPE values, the MPE formulae can accurately locate the appropriate value of the decay parameter for minimizing the true test error of faulty networks.

Increased brain signal variability accompanies lower behavioral variability in development

As the brain matures, its responses become optimized. Behavioral measures show this through improved accuracy and decreased trial-to-trial variability. The question remains whether the supporting brain dynamics show a similar decrease in variability. We examined the relation between variability in single trial evoked electrical activity of the brain (measured with EEG) and performance of a face memory task in children (8-15 y) and young adults (20-33 y). Behaviorally, children showed slower, more variable response times (RT), and less accurate recognition than adults. However, brain signal variability increased with age, and showed strong negative correlations with intrasubject RT variability and positive correlations with accuracy. Thus, maturation appears to lead to a brain with greater functional variability, which is indicative of enhanced neural complexity. This variability may reflect a broader repertoire of metastable brain states and more fluid transitions among them that enable optimum responses. Our results suggest that the moment-to-moment variability in brain activity may be a critical index of the cognitive capacity of the brain.