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Binocular disparity provides critical information about three-dimensional (3D) structures to support perception and action. In the past decade significant progress has been made in uncovering human brain areas engaged in the processing of binocular disparity signals. Yet, the fine-scale brain processing underlying 3D perception remains unknown. Here, we use ultra-high-field (7T) functional imaging at submillimeter resolution to examine fine-scale BOLD fMRI signals involved in 3D perception. In particular, we sought to interrogate the local circuitry involved in disparity processing by sampling fMRI responses at different positions relative to the cortical surface (i.e., across cortical depths corresponding to layers). We tested for representations related to 3D perception by presenting participants (male and female, N = 8) with stimuli that enable stable stereoscopic perception [i.e., correlated random dot stereograms (RDS)] versus those that do not (i.e., anticorrelated RDS). Using multivoxel pattern analysis (MVPA), we demonstrate cortical depth-specific representations in areas V3A and V7 as indicated by stronger pattern responses for correlated than for anticorrelated stimuli in upper rather than deeper layers. Examining informational connectivity, we find higher feedforward layer-to-layer connectivity for correlated than anticorrelated stimuli between V3A and V7. Further, we observe disparity-specific feedback from V3A to V1 and from V7 to V3A. Our findings provide evidence for the role of V3A as a key nexus for disparity processing, which is implicated in feedforward and feedback signals related to the perceptual estimation of 3D structures. SIGNIFICANCE STATEMENT Binocular vision plays a significant role in supporting our interactions with the surrounding environment. The fine-scale neural mechanisms that underlie the brain9s skill in extracting 3D structures from binocular signals are poorly understood. Here, we capitalize on recent advances in ultra-high-field functional imaging to interrogate human brain circuits involved in 3D perception at submillimeter resolution. We provide evidence for the role of area V3A as a key nexus for disparity processing, which is implicated in feedforward and feedback signals related to the perceptual estimation of 3D structures from binocular signals. These fine-scale measurements help bridge the gap between animal neurophysiology and human fMRI studies investigating cross-scale circuits, from micro circuits to global brain networks for 3D perception.

Learning and experience are known to improve our ability to make perceptual decisions. Yet, our understanding of the brain mechanisms that support improved perceptual decisions through training remains limited. Here, we test the neurochemical and functional interactions that support learning for perceptual decisions in the context of an orientation identification task. Using magnetic resonance spectroscopy (MRS), we measure neurotransmitters (i.e., glutamate, GABA) that are known to be involved in visual processing and learning in sensory [early visual cortex (EV)] and decision-related [dorsolateral prefrontal cortex (DLPFC)] brain regions. Using resting-state functional magnetic resonance imaging (rs-fMRI), we test for functional interactions between these regions that relate to decision processes. We demonstrate that training improves perceptual judgments (i.e., orientation identification), as indicated by faster rates of evidence accumulation after training. These learning-dependent changes in decision processes relate to lower EV glutamate levels and EV-DLPFC connectivity, suggesting that glutamatergic excitation and functional interactions between visual and dorsolateral prefrontal cortex facilitate perceptual decisions. Further, anodal transcranial direct current stimulation (tDCS) in EV impairs learning, suggesting a direct link between visual cortex excitation and perceptual decisions. Our findings advance our understanding of the role of learning in perceptual decision making, suggesting that glutamatergic excitation for efficient sensory processing and functional interactions between sensory and decision-related regions support improved perceptual decisions.NEW & NOTEWORTHY Combining multimodal brain imaging [magnetic resonance spectroscopy (MRS), functional connectivity] with interventions [transcranial direct current stimulation (tDCS)], we demonstrate that glutamatergic excitation and functional interactions between sensory (visual) and decision-related (dorsolateral prefrontal cortex) areas support our ability to optimize perceptual decisions through training.

Training is known to improve our ability to make decisions when interacting in complex environments. However, individuals vary in their ability to learn new tasks and acquire new skills in different settings. Here, we test whether this variability in learning ability relates to individual brain oscillatory states. We use a visual flicker paradigm to entrain individuals at their own brain rhythm (i.e. peak alpha frequency) as measured by resting-state electroencephalography (EEG). We demonstrate that this individual frequency-matched brain entrainment results in faster learning in a visual identification task (i.e. detecting targets embedded in background clutter) compared to entrainment that does not match an individual’s alpha frequency. Further, we show that learning is specific to the phase relationship between the entraining flicker and the visual target stimulus. EEG during entrainment showed that individualized alpha entrainment boosts alpha power, induces phase alignment in the pre-stimulus period, and results in shorter latency of early visual evoked potentials, suggesting that brain entrainment facilitates early visual processing to support improved perceptual decisions. These findings suggest that individualized brain entrainment may boost perceptual learning by altering gain control mechanisms in the visual cortex, indicating a key role for individual neural oscillatory states in learning and brain plasticity.

Background. The global COVID-19 pandemic has triggered a fundamental reexamination of how human psychological research can be conducted both safely and robustly in a new era of digital working and physical distancing. Online web-based testing has risen to the fore as a promising solution for rapid mass collection of cognitive data without requiring human contact. However, a long-standing debate exists over the data quality and validity of web-based studies. Here, we examine the opportunities and challenges afforded by the societal shift toward web-based testing, highlight an urgent need to establish a standard data quality assurance framework for online studies, and develop and validate a new supervised online testing methodology, remote guided testing (RGT). Methods. A total of 85 healthy young adults were tested on 10 cognitive tasks assessing executive functioning (flexibility, memory and inhibition) and learning. Tasks were administered either face-to-face in the laboratory (N=41) or online using remote guided testing (N=44), delivered using identical web-based platforms (CANTAB, Inquisit and i-ABC). Data quality was assessed using detailed trial-level measures (missed trials, outlying and excluded responses, response times), as well as overall task performance measures. Results. The results indicated that, across all measures of data quality and performance, RGT data was statistically-equivalent to data collected in person in the lab. Moreover, RGT participants out-performed the lab group on measured verbal intelligence, which could reflect test environment differences, including possible effects of mask-wearing on communication. Conclusions. These data suggest that the RGT methodology could help to ameliorate concerns regarding online data quality and - particularly for studies involving high-risk or rare cohorts - offer an alternative for collecting high-quality human cognitive data without requiring in-person physical attendance.

Interpreting cluttered scenes —a key skill for successfully interacting with our environment— relies on our ability to select relevant sensory signals while filtering out noise. Training is known to improve our ability to make these perceptual judgements by altering local processing in sensory brain areas. Yet, the brain-wide network mechanisms that mediate our ability for perceptual learning remain largely unknown. Here, we combine transcranial direct current stimulation (tDCS) with multi-modal brain measures to modulate cortical excitability during training on a signal-in-noise task (i.e. detection of visual patterns in noise) and test directly the link between processing in visual cortex and its interactions with decision-related areas (i.e. posterior parietal cortex). We test whether brain stimulation alters inhibitory processing in visual cortex, as measured by magnetic resonance spectroscopy (MRS) of GABA and functional connectivity between visual and posterior parietal cortex, as measured by resting state functional magnetic resonance imaging (rs-fMRI). We show that anodal tDCS during training results in faster learning and decreased GABA+ during training, before these changes occur for training without stimulation (i.e. sham). Further, anodal tDCS decreases occipito-parietal interactions and time-varying connectivity across the visual cortex. Our findings demonstrate that tDCS boosts learning by accelerating visual GABAergic plasticity and altering interactions between visual and decision-related areas, suggesting that training optimises gain control mechanisms (i.e. GABAergic inhibition) and functional inter-areal interactions to support perceptual learning.

The brain’s capacity to adapt to sensory inputs is key for processing sensory information efficiently and interacting in new environments. Following repeated exposure to the same sensory input, brain activity in sensory areas is known to decrease as inputs become familiar, a process known as adaptation. Yet, the brain-wide mechanisms that mediate adaptive processing remain largely unknown. Here, we combine multimodal brain imaging (functional magnetic resonance imaging [fMRI], magnetic resonance spectroscopy) with behavioral measures of orientation-specific adaptation (i.e., tilt aftereffect) to investigate the functional and neurochemical mechanisms that support adaptive processing. Our results reveal two functional brain networks: 1) a sensory-adaptation network including occipital and dorsolateral prefrontal cortex regions that show decreased fMRI responses for repeated stimuli and 2) a perceptual-memory network including regions in the parietal memory network (PMN) and dorsomedial prefrontal cortex that relate to perceptual bias (i.e., tilt aftereffect). We demonstrate that adaptation relates to increased occipito-parietal connectivity, while decreased connectivity between sensory-adaptation and perceptual-memory networks relates to GABAergic inhibition in the PMN. Thus, our findings provide evidence that suppressive interactions between sensory-adaptation (i.e., occipito-parietal) and perceptual-memory (i.e., PMN) networks support adaptive processing and behavior, proposing a key role of memory systems in efficient sensory processing.

Extracting the structure of complex environments is at the core of our ability to interpret the present and predict the future. This skill is important for a range of behaviours from navigating a new city to learning music and language. Classical approaches that investigate our ability to extract the principles of organisation that govern complex environments focus on reward-based learning. Yet, the human brain is shown to be expert at learning generative structure based on mere exposure and without explicit reward. Individuals are shown to adapt to-unbeknownst to them-changes in the environment's temporal statistics and predict future events. Further, we present evidence for a common brain architecture for unsupervised structure learning and reward-based learning, suggesting that the brain is built on the premise that 'learning is its own reward' to support adaptive behaviour.

BACKGROUND The global COVID-19 pandemic has triggered a fundamental reexamination of how human psychological research can be conducted safely and robustly in a new era of digital working and physical distancing. Online web-based testing has risen to the forefront as a promising solution for the rapid mass collection of cognitive data without requiring human contact. However, a long-standing debate exists over the data quality and validity of web-based studies. This study examines the opportunities and challenges afforded by the societal shift toward web-based testing and highlights an urgent need to establish a standard data quality assurance framework for online studies. OBJECTIVE This study aims to develop and validate a new supervised online testing methodology, remote guided testing (RGT). METHODS A total of 85 healthy young adults were tested on 10 cognitive tasks assessing executive functioning (flexibility, memory, and inhibition) and learning. Tasks were administered either face-to-face in the laboratory (n=41) or online using remote guided testing (n=44) and delivered using identical web-based platforms (Cambridge Neuropsychological Test Automated Battery, Inquisit, and i-ABC). Data quality was assessed using detailed trial-level measures (missed trials, outlying and excluded responses, and response times) and overall task performance measures. RESULTS The results indicated that, across all data quality and performance measures, RGT data was statistically-equivalent to in-person data collected in the lab (P>.40 for all comparisons). Moreover, RGT participants out-performed the lab group on measured verbal intelligence (P CONCLUSIONS These data suggest that the RGT methodology could help ameliorate concerns regarding online data quality—particularly for studies involving high-risk or rare cohorts—and offer an alternative for collecting high-quality human cognitive data without requiring in-person physical attendance.

Background The global COVID-19 pandemic has triggered a fundamental reexamination of how human psychological research can be conducted safely and robustly in a new era of digital working and physical distancing. Online web-based testing has risen to the forefront as a promising solution for the rapid mass collection of cognitive data without requiring human contact. However, a long-standing debate exists over the data quality and validity of web-based studies. This study examines the opportunities and challenges afforded by the societal shift toward web-based testing and highlights an urgent need to establish a standard data quality assurance framework for online studies. Objective This study aims to develop and validate a new supervised online testing methodology, remote guided testing (RGT). Methods A total of 85 healthy young adults were tested on 10 cognitive tasks assessing executive functioning (flexibility, memory, and inhibition) and learning. Tasks were administered either face-to-face in the laboratory (n=41) or online using remote guided testing (n=44) and delivered using identical web-based platforms (Cambridge Neuropsychological Test Automated Battery, Inquisit, and i-ABC). Data quality was assessed using detailed trial-level measures (missed trials, outlying and excluded responses, and response times) and overall task performance measures. Results The results indicated that, across all data quality and performance measures, RGT data was statistically-equivalent to in-person data collected in the lab (P>.40 for all comparisons). Moreover, RGT participants out-performed the lab group on measured verbal intelligence (P Conclusions These data suggest that the RGT methodology could help ameliorate concerns regarding online data quality—particularly for studies involving high-risk or rare cohorts—and offer an alternative for collecting high-quality human cognitive data without requiring in-person physical attendance.

Adapting to the environment statistics by reducing brain responses to repetitive sensory information is key for efficient information processing. Yet, the fine-scale computations that support this adaptive processing in the human brain remain largely unknown. Here, we capitalize on the sub-millimetre resolution afforded by ultra-high field imaging to examine BOLD-fMRI signals across cortical depth and discern competing hypotheses about the brain mechanisms (feedforward vs. feedback) that mediate adaptive visual processing. We demonstrate suppressive recurrent processing within visual cortex, as indicated by stronger BOLD decrease in superficial than middle and deeper layers for gratings that were repeatedly presented at the same orientation. Further, we show dissociable connectivity mechanisms for adaptive processing: enhanced feedforward connectivity within visual cortex, while feedback occipito-parietal connectivity, reflecting top-down influences on visual processing. Our findings provide evidence for a circuit of local recurrent and feedback interactions that mediate rapid brain plasticity for adaptive information processing.

The earliest stages of Alzheimer’s disease (AD) involve interactions between multiple pathophysiological processes. Although these processes are well studied, we still lack robust tools to predict individualised trajectories of disease progression. Here, we employ a robust and interpretable machine learning approach to combine multimodal biological data and predict future tau accumulation, translating predictive information from deep phenotyping cohorts at early stages of AD to cognitively normal individuals. In particular, we use machine learning to quantify interactions between key pathological markers (β-amyloid, medial temporal atrophy, tau and APOE 4) at early and asymptomatic stages of AD. We next derive a predictive index that stratifies individuals based on future pathological tau accumulation, highlighting two critical features for optimal clinical trial design. First, future tau accumulation provides a better outcome measure compared to changes in cognition. Second, stratification based on multimodal data compared to β-amyloid alone reduces the sample size required to detect a clinically meaningful change in tau accumulation. Further, we extend our machine learning approach to derive individualised trajectories of future pathological tau accumulation in early AD patients and accurately predict regional future rate of tau accumulation in an independent sample of cognitively unimpaired individuals. Our results propose a robust approach for fine scale stratification and prognostication with translation impact for clinical trial design at asymptomatic and early stages of AD. One Sentence Summary Our machine learning approach combines baseline multimodal data to make individualised predictions of future pathological tau accumulation at prodromal and asymptomatic stages of Alzheimer’s disease with high accuracy and regional specificity.

The early stages of Alzheimer's disease (AD) involve interactions between multiple pathophysiological processes. Although these processes are well studied, we still lack robust tools to predict individualised trajectories of disease progression. Here, we employ a robust and interpretable machine learning approach to combine multimodal biological data and predict future pathological tau accumulation. In particular, we use machine learning to quantify interactions between key pathological markers (β-amyloid, medial temporal lobe atrophy, tau and APOE 4) at mildly impaired and asymptomatic stages of AD. Using baseline non-tau markers we derive a prognostic index that: (a) stratifies patients based on future pathological tau accumulation, (b) predicts individualised regional future rate of tau accumulation, and (c) translates predictions from deep phenotyping patient cohorts to cognitively normal individuals. Our results propose a robust approach for fine scale stratification and prognostication with translation impact for clinical trial design targeting the earliest stages of AD.

Murley et al. use ultra-high field (7T) magnetic resonance spectroscopy to measure in vivo glutamate and GABA in frontotemporal lobar degeneration syndromes, and show that deficits in these neurotransmitters are associated with behavioural disinhibition., Behavioural disinhibition is a common feature of the syndromes associated with frontotemporal lobar degeneration (FTLD). It is associated with high morbidity and lacks proven symptomatic treatments. A potential therapeutic strategy is to correct the neurotransmitter deficits associated with FTLD, thereby improving behaviour. Reductions in the neurotransmitters glutamate and GABA correlate with impulsive behaviour in several neuropsychiatric diseases and there is post-mortem evidence of their deficit in FTLD. Here, we tested the hypothesis that prefrontal glutamate and GABA levels are reduced by FTLD in vivo, and that their deficit is associated with impaired response inhibition. Thirty-three participants with a syndrome associated with FTLD (15 patients with behavioural variant frontotemporal dementia and 18 with progressive supranuclear palsy, including both Richardson’s syndrome and progressive supranuclear palsy-frontal subtypes) and 20 healthy control subjects were included. Participants undertook ultra-high field (7 T) magnetic resonance spectroscopy and a stop-signal task of response inhibition. We measured glutamate and GABA levels using semi-LASER magnetic resonance spectroscopy in the right inferior frontal gyrus, because of its strong association with response inhibition, and in the primary visual cortex, as a control region. The stop-signal reaction time was calculated using an ex-Gaussian Bayesian model. Participants with frontotemporal dementia and progressive supranuclear palsy had impaired response inhibition, with longer stop-signal reaction times compared with controls. GABA concentration was reduced in patients versus controls in the right inferior frontal gyrus, but not the occipital lobe. There was no group-wise difference in partial volume corrected glutamate concentration between patients and controls. Both GABA and glutamate concentrations in the inferior frontal gyrus correlated inversely with stop-signal reaction time, indicating greater impulsivity in proportion to the loss of each neurotransmitter. We conclude that the glutamatergic and GABAergic deficits in the frontal lobe are potential targets for symptomatic drug treatment of frontotemporal dementia and progressive supranuclear palsy.

Learning and experience are critical for translating ambiguous sensory information from our environments to perceptual decisions. Yet, evidence on how training molds the adult human brain remains controversial, as fMRI at standard resolution does not allow us to discern the finer-scale mechanisms that underlie sensory plasticity. Here, we combine ultra-high field (7T) functional imaging at sub-millimetre resolution with orientation discrimination training to interrogate experience-dependent plasticity across cortical depths. Our results provide evidence for recurrent plasticity, by contrast to sensory encoding vs. feedback mechanisms. We demonstrate that learning alters orientation-specific representations in superficial rather than middle V1 layers, suggesting changes in read-out rather than input signals. Further, learning increases feedforward rather than feedback layer-to-layer connectivity in occipito-parietal regions, suggesting that sensory plasticity gates perceptual decisions. Our findings propose finer-scale plasticity mechanisms that re-weight sensory signals to inform improved decisions, bridging the gap between micro- and macro-circuits of experience-dependent plasticity.

Highlights • Metric learning reveals continuous prognostic scores of cognitive decline due to AD. • Individualised disease trajectory modelling benefits from adding non-invasive biomarkers. • Interpretable and interoperable markers of progression to dementia for patient stratification., Alzheimer's disease (AD) is characterised by a dynamic process of neurocognitive changes from normal cognition to mild cognitive impairment (MCI) and progression to dementia. However, not all individuals with MCI develop dementia. Predicting whether individuals with MCI will decline (i.e. progressive MCI) or remain stable (i.e. stable MCI) is impeded by patient heterogeneity due to comorbidities that may lead to MCI diagnosis without progression to AD. Despite the importance of early diagnosis of AD for prognosis and personalised interventions, we still lack robust tools for predicting individual progression to dementia. Here, we propose a novel trajectory modelling approach based on metric learning (Generalised Metric Learning Vector Quantization) that mines multimodal data from MCI patients in the Alzheimer's disease Neuroimaging Initiative (ADNI) cohort to derive individualised prognostic scores of cognitive decline due to AD. We develop an integrated biomarker generation– using partial least squares regression– and classification methodology that extends beyond binary patient classification into discrete subgroups (i.e. stable vs. progressive MCI), determines individual profiles from baseline (i.e. cognitive or biological) data and predicts individual cognitive trajectories (i.e. change in memory scores from baseline). We demonstrate that a metric learning model trained on baseline cognitive data (memory, executive function, affective measurements) discriminates stable vs. progressive MCI individuals with high accuracy (81.4%), revealing an interaction between cognitive (memory, executive functions) and affective scores that may relate to MCI comorbidity (e.g. affective disturbance). Training the model to perform the same binary classification on biological data (mean cortical β-amyloid burden, grey matter density, APOE 4) results in similar prediction accuracy (81.9%). Extending beyond binary classifications, we develop and implement a trajectory modelling approach that shows significantly better performance in predicting individualised rate of future cognitive decline (i.e. change in memory scores from baseline), when the metric learning model is trained with biological (r = −0.68) compared to cognitive (r = −0.4) data. Our trajectory modelling approach reveals interpretable and interoperable markers of progression to AD and has strong potential to guide effective stratification of individuals based on prognostic disease trajectories, reducing MCI patient misclassification, that is critical for clinical practice and discovery of personalised interventions.

Summary Ultra-high field (UHF) neuroimaging affords the sub-millimeter resolution that allows researchers to interrogate brain computations at a finer scale than that afforded by standard fMRI techniques. Here, we present a step-by-step protocol for using UHF imaging (Siemens Terra 7T scanner) to measure activity in the human brain. We outline how to preprocess the data using a pipeline that combines tools from SPM, FreeSurfer, ITK-SNAP, and BrainVoyager and correct for vasculature-related confounders to improve the spatial accuracy of the fMRI signal. For complete details on the use and execution of this protocol, please refer to Jia et al. (2020) and Zamboni et al. (2020)., Graphical abstract, Highlights • Protocol for using ultra-high field imaging to measure activity in the human brain • Defining cortical layers using high-resolution anatomical scans • Preprocessing functional brain imaging data across cortical depth • Correcting for vasculature-related confounds to improve spatial specificity, Ultra-high field (UHF) neuroimaging affords the sub-millimeter resolution that allows researchers to interrogate brain computations at a finer scale than that afforded by standard fMRI techniques. Here, we present a step-by-step protocol for using UHF imaging (Siemens Terra 7T scanner) to measure activity in the human brain. We outline how to preprocess the data using a pipeline that combines tools from SPM, FreeSurfer, ITK-SNAP, and BrainVoyager and correct for vasculature-related confounders to improve the spatial accuracy of the fMRI signal.