Poorer white matter microstructure predicts slower and more variable reaction time performance: evidence for the neural noise hypothesis in a large lifespan cohort

Most prior research in the neural and behavioral sciences has been focused on characterizing averages in cognition, brain characteristics, or behavior, and attempting to predict differences in these averages among individuals. However, this overwhelming focus on mean levels may leave us with an incomplete picture of what drives individual differences in behavioral phenotypes by ignoring the variability of behavior around an individual’s mean. In particular, better white matter (WM) structural microstructure has been hypothesized to support consistent behavioral performance by decreasing gaussian noise in signal transfer. In contrast, lower indices of white matter microstructure have been associated with greater within-subject variance in the ability to deploy performance-related resources, especially in clinical samples. We tested this ‘neural noise’ hypothesis in a large adult lifespan cohort (Cam-CAN) with over 2500 individuals in a (2681 behavioral sessions with 708 scans in adults aged 18–102) using measures of WM tract microstructure to predict mean levels and variability in reaction time performance on a simple behavioral task using a dynamic structural equation model (DSEM). We found broad support for neural noise hypothesis, such that lower WM microstructure predicted individual differences in separable components of behavioral performance estimated using DSEM, including slower mean responses and increased variability. These effects were robust when including age in the model, suggesting consistent effects of WM microstructure across the adult lifespan above and beyond concurrent effects of ageing. Crucially, these results demonstrate the utility of DSEM for modeling and predicting behavioral variability directly, and the promise of studying variability for understanding cognitive processes.

Causal contributions of neural noise to left frontal networks subtending the modulation of conscious visual perception in the human brain

For several decades, the field of human neurophysiology has focused on the role played by cortical oscillations in enabling brain function underpinning behaviors. In parallel, a less visible but robust body of work on the stochastic resonance phenomenon has also theorized contributions of neural noise - hence more heterogeneous, complex and less predictable activity - in brain coding. The latter notion has received indirect causal support via improvements of visual function during non-regular or random brain stimulation patterns. Nonetheless, direct evidence demonstrating an impact of brain stimulation on direct measures of neural noise is still lacking. Here we evaluated the impact of three non frequency-specific TMS bursts, compared to a control pure high-beta TMS rhythm, delivered to the left FEF during a visual detection task, on the heterogeneity, predictability and complexity of ongoing brain activity recorded with scalp EEG. Our data showed surprisingly that the three non frequency-specific TMS patterns did not prevent a build-up of local high-beta activity. Nonetheless, they increased power across broader or in multiple frequency bands compared to control purely rhythmic high-beta bursts tested along. Importantly, non frequency-specific patterns enhanced signal entropy over multiple time-scales, suggesting higher complexity and an overall induction of higher levels of cortical noise than rhythmic TMS bursts. Our outcomes provide indirect evidence on a potential modulatory role played by sources of stochastic noise on brain oscillations and synchronization. Additionally, they pave the way towards the development of novel neurostimulation approaches to manipulate cortical sources of noise and further investigate their causal role in neural coding.

Altered EEG Variability on Different Time Scales in Participants with Autism Spectrum Disorder

Background: One of the great challenges in psychiatry is finding reliable biomarkers that may allow for more accurate diagnosis and treatment of patients. In this context the topic of neural variability received scientific attention in recent years. Altered neural variability was found in different cohorts of patients with autism spectrum disorder (ASD) using both functional magnetic resonance imaging (fMRI) and electroencephalography (EEG). These findings lead to the neural noise hypothesis of ASD. The number of studies focusing on neural variability is, however, yet small and the reported effects are controversial and poorly understood. Methods: In the present study we compared different temporal and structural aspects of variability in visually evoked EEG activity in a cohort of 16 adult participants with Asperger Syndrome (AS) and 19 matched neurotypical (NT) controls. Participants performed a visual oddball task using fine and coarse checkerboard stimuli. Results: We investigated various measures of neural variability and found effects on multiple time scales. (1) As opposed to some of 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-minute experiment and (3) seems to be based on smaller variability of neural background activity in patients compared to NTs. Limitations: The present study is exploratory in nature with a hypothesis generating character. Further studies with a new and larger set of participants are thus mandatory to verify or falsify our findings. Conclusion: 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 trade like effects. The inconsistency of our findings with previous reports from the literature, on the other hand, rather points towards state-like effects, specific to the current stimulus material and/or experimental paradigm.

The unbiased estimation of the fraction of variance explained by a model

The correlation coefficient squared, r2, is commonly used to validate quantitative models on neural data, yet it is biased by trial-to-trial variability: as trial-to-trial variability increases, measured correlation to a model’s predictions decreases. As a result, models that perfectly explain neural tuning can appear to perform poorly. Many solutions to this problem have been proposed, but no consensus has been reached on which is the least biased estimator. Some currently used methods substantially overestimate model fit, and the utility of even the best performing methods is limited by the lack of confidence intervals and asymptotic analysis. We provide a new estimator, r ^ ER 2, that outperforms all prior estimators in our testing, and we provide confidence intervals and asymptotic guarantees. We apply our estimator to a variety of neural data to validate its utility. We find that neural noise is often so great that confidence intervals of the estimator cover the entire possible range of values ([0, 1]), preventing meaningful evaluation of the quality of a model’s predictions. This leads us to propose the use of the signal-to-noise ratio (SNR) as a quality metric for making quantitative comparisons across neural recordings. Analyzing a variety of neural data sets, we find that up to ∼ 40% of some state-of-the-art neural recordings do not pass even a liberal SNR criterion. Moving toward more reliable estimates of correlation, and quantitatively comparing quality across recording modalities and data sets, will be critical to accelerating progress in modeling biological phenomena.

Between Action and Emotional Survival During the COVID-19 era: Sensorimotor Pathways as Control Systems of Transdiagnostic Anxiety-Related Intolerance to Uncertainty

Objectives: The COVID-19 pandemic and aligned social and physical distancing regulations increase the sense of uncertainty, intensifying the risk for psychopathology globally. Anxiety disorders are associated with intolerance to uncertainty. In this review we describe brain circuits and sensorimotor pathways involved in human reactions to uncertainty. We present the healthy mode of coping with uncertainty and discuss deviations from this mode.Methods: Literature search of PubMed and Google Scholar.Results: As manifestation of anxiety disorders includes peripheral reactions and negative cognitions, we suggest an integrative model of threat cognitions modulated by sensorimotor regions: “The Sensorimotor-Cognitive-Integration-Circuit.” The model emphasizes autonomic nervous system coupling with the cortex, addressing peripheral anxious reactions to uncertainty, pathways connecting cortical regions and cost-reward evaluation circuits to sensorimotor regions, filtered by the amygdala and basal ganglia. Of special interest are the ascending and descending tracts for sensory-motor crosstalk in healthy and pathological conditions. We include arguments regarding uncertainty in anxiety reactions to the pandemic and derive from our model treatment suggestions which are supported by scientific evidence. Our model is based on systematic control theories and emphasizes the role of goal conflict regulation in health and pathology. We also address anxiety reactions as a spectrum ranging from healthy to pathological coping with uncertainty, and present this spectrum as a transdiagnostic entity in accordance with recent claims and models.Conclusions: The human need for controllability and predictability suggests that anxiety disorders reactive to the pandemic's uncertainties reflect pathological disorganization of top-down bottom-up signaling and neural noise resulting from non-pathological human needs for coherence in life.

Noise-Induced Hearing Loss in Gerbil: Round Window Assays of Synapse Loss

Previous work in animals with recovered hearing thresholds but permanent inner hair cell synapse loss after noise have suggested initial vulnerability of low spontaneous rate (SR) auditory nerve fibers (ANF). As these fibers have properties of response that facilitate robust sound coding in continuous noise backgrounds, their targeted loss would have important implications for function. To address the issue of relative ANF vulnerabilities after noise, we assessed cochlear physiologic and histologic consequences of temporary threshold shift-producing sound over-exposure in the gerbil, a species with well-characterized distributions of auditory neurons by SR category. The noise exposure targeted a cochlear region with distributed innervation (low-, medium- and high-SR neurons). It produced moderate elevations in outer hair cell-based distortion-product otoacoustic emission and whole nerve compound action potential thresholds in this region, with accompanying reductions in suprathreshold response amplitudes, quantified at 24 h. These parameters of response recovered well with post-exposure time. Chronic synapse loss was maximum in the frequency region initially targeted by the noise. Cochlear round window recorded mass potentials (spontaneous neural noise and sound-driven peri-stimulus time responses, PSTR) reflected parameters of the loss not detected by the conventional assays. Spontaneous activity was acutely reduced. Steady-state (PSTR plateau) activity was correlated with synapse loss in frequency regions with high concentrations of low-SR neurons, whereas the PSTR onset peak and spontaneous round window noise, both dominated by high-SR fiber activity, were relatively unaltered across frequency in chronic ears. Together, results suggest that acute targets of noise were of mixed SR subtypes, but chronic targets were predominantly low-SR neurons. PSTRs captured key properties of the auditory nerve response and vulnerability to injury that should yield important diagnostic information in hearing loss etiologies producing cochlear synaptic and neural loss.

Effect of acute alcohol intoxication on scale-free neural activity in the lateral septum in rats

Electrophysiological recordings of brain activity show both oscillatory dynamics that typically are analyzed in the time-frequency domain to describe brain oscillatory phenomena and scale-free arrhythmic activity defined as neural noise. Recent studies consider this arrhythmic fractal dynamics of neural noise as a sensitive biomarker of a number of cognitive processes, activity of neurotransmitter systems, changes that accompany neurodegenerative and psychiatric disorders including alcohol use disorder. We tested the changes in neural noise induced by acute alcohol intoxication in the lateral septum for the entire spectrum (1–200 Hz) of local field potential signal and for frequency specific ranges (delta, theta, beta, gamma and epsilon bands). Five male Wistar rats were implanted with intracranial electrodes and local field potential signal was measured for baseline activity and activity induced by acute ethanol intoxication (2 g/kg). Change in neural noise dynamics was assessed as a change in the slope of linear regression fit of power spectral density curves in double logarithmic scale. In our study alcohol resulted in lower incline of scale-free activity in the lateral septum for high frequency range and for the whole spectrum, which is interpreted generally as increase in neural noise and change in neuronal processing in a more stochastic way initiated by the acute alcohol intoxication. At the same time, we observed decrease in neural noise for low frequency range. The observed changes may be related to the shift of the excitatory-inhibitory balance towards inhibition and changes in neurotransmission mostly in the GABAergic system. Scale-free activity was sensitive in the conditions of acute alcohol intoxication, therefore to understand its role in alcohol use disorder we need more data and studies on the underlying processes. Future studies should include simultaneous recordings and analysis of arrhythmic dynamics with the oscillatory and multiunit spiking activity in the lateral septum. It can reveal the contribution of different-scale processes in changes driven by acute alcohol intoxication and clarify the specific electrophysiological mechanisms.

No Evidence of Reduced Contrast Sensitivity in Migraine-with-Aura for Large, Narrowband, Centrally Presented Noise-Masked Stimuli

Individuals with migraine aura show differences in visual perception compared to control groups. Measures of contrast sensitivity have suggested that people with migraine aura are less able to exclude external visual noise, and that this relates to higher variability in neural processing. The current study compared contrast sensitivity in migraine with aura and control groups for narrow-band grating stimuli at 2 and 8 cycles/degree, masked by Gaussian white noise. We predicted that contrast sensitivity would be lower in the migraine with aura group at high noise levels. Contrast sensitivity was higher for the low spatial frequency stimuli, and decreased with the strength of the masking noise. We did not, however, find any evidence of reduced contrast sensitivity associated with migraine with aura. We propose alternative methods as a more targeted assessment of the role of neural noise and excitability as contributing factors to migraine aura.

Novelty and imitation within the brain: a Darwinian neurodynamic approach to combinatorial problems

Efficient search in vast combinatorial spaces, such as those of possible action sequences, linguistic structures, or causal explanations, is an essential component of intelligence. Is there any computational domain that is flexible enough to provide solutions to such diverse problems and can be robustly implemented over neural substrates? Based on previous accounts, we propose that a Darwinian process, operating over sequential cycles of imperfect copying and selection of neural informational patterns, is a promising candidate. Here we implement imperfect information copying through one reservoir computing unit teaching another. Teacher and learner roles are assigned dynamically based on evaluation of the readout signal. We demonstrate that the emerging Darwinian population of readout activity patterns is capable of maintaining and continually improving upon existing solutions over rugged combinatorial reward landscapes. We also demonstrate the existence of a sharp error threshold, a neural noise level beyond which information accumulated by an evolutionary process cannot be maintained. We introduce a novel analysis method, neural phylogenies, that displays the unfolding of the neural-evolutionary process.