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Predicting the course of neurodegenerative disorders early has potential to greatly improve clinical management and patient outcomes. A key challenge for early prediction in real-world clinical settings is the lack of labeled data (i.e., clinical diagnosis). In contrast to supervised classification approaches that require labeled data, we propose an unsupervised multimodal trajectory modeling (MTM) approach based on a mixture of state space models that captures changes in longitudinal data (i.e., trajectories) and stratifies individuals without using clinical diagnosis for model training. MTM learns the relationship between states comprising expensive, invasive biomarkers (β-amyloid, grey matter density) and readily obtainable cognitive observations. MTM training on trajectories stratifies individuals into clinically meaningful clusters more reliably than MTM training on baseline data alone and is robust to missing data (i.e., cognitive data alone or single assessments). Extracting an individualized cognitive health index (i.e., MTM-derived cluster membership index) allows us to predict progression to AD more precisely than standard clinical assessments (i.e., cognitive tests or MRI scans alone). Importantly, MTM generalizes successfully from research cohort to real-world clinical data from memory clinic patients with missing data, enhancing the clinical utility of our approach. Thus, our multimodal trajectory modeling approach provides a cost-effective and non-invasive tool for early dementia prediction without labeled data (i.e., clinical diagnosis) with strong potential for translation to clinical practice. [ABSTRACT FROM AUTHOR]

Experience is known to facilitate our ability to interpret sequences of events and make predictions about the future by extracting temporal regularities in our environments. Here, we ask whether uncertainty in dynamic environments affects our ability to learn predictive structures. We exposed participants to sequences of symbols determined by first-order Markov models and asked them to indicate which symbol they expected to follow each sequence. We introduced uncertainty in this prediction task by manipulating the: (a) probability of symbol co-occurrence, (b) stimulus presentation rate. Further, we manipulated feedback, as it is known to play a key role in resolving uncertainty. Our results demonstrate that increasing the similarity in the probabilities of symbol cooccurrence impaired performance on the prediction task. In contrast, increasing uncertainty in stimulus presentation rate by introducing temporal jitter resulted in participants adopting a strategy closer to probability maximization than matching and improving in the prediction tasks. Next, we show that feedback plays a key role in learning predictive statistics. Trial-by-trial feedback yielded stronger improvement than block feedback or no feedback; that is, participants adopted a strategy closer to probability maximization and showed stronger improvement when trained with trial-by-trial feedback. Further, correlating individual strategy with learning performance showed better performance in structure learning for observers who adopted a strategy closer to maximization. Our results indicate that executive cognitive functions (i.e., selective attention) may account for this individual variability in strategy and structure learning ability. Taken together, our results provide evidence for flexible structure learning; individuals adapt their decision strategy closer to probability maximization, reducing uncertainty in temporal sequences and improving their ability to learn predictive statistics in variable environments. [ABSTRACT FROM AUTHOR]

Background: Cognitive flexibility refers to the capacity to shift between conceptual representations particularly in response to changes in instruction and feedback. It enables individuals to swiftly adapt to changes in their environment and has significant implications for learning. The present study focuses on investigating changes in cognitive flexibility following an intervention programme—Structure Learning training. Methods: Participants are pseudo-randomised to either the Training or Control group, while matched on age, sex, intelligence and cognitive flexibility performance. In the Training group, participants undergo around 2 weeks of training (at least 13 sessions) on Structure Learning. In the Control group, participants do not have to undergo any training and are never exposed to the Structure Learning task. The effects of Structure Learning training are investigated at both the behavioural and neural level. We measured covariates that can influence an individual's training performance before the training phase and outcome measures that can potentially show training benefits after the training phase. At the behavioural level, we investigated outcomes in both cognitive and social aspects with a primary focus on executive functions. At the neural level, we employed a multimodality approach and investigated potential changes to functional connectivity patterns, neurometabolite concentration in the frontal brain regions, and brain microstructure and myelination. Discussion: We reported the development of a novel training programme based on Structure Learning that aims to hone a general learning ability to potentially achieve extensive transfer benefits across various cognitive constructs. Potential transfer benefits can be exhibited through better performance in outcome measures between Training and Control participants, and positive associations between training performance and outcomes after the training in Training participants. Moreover, we attempt to substantiate behavioural findings with evidence of neural changes across different imaging modalities by the Structure Learning training. Trial registration: National Institutes of Health U.S. National Library of Medicine ClinicalTrials.gov NCT05611788. Registered on 7 November 2022. Protocol version: 11 May 2023. [ABSTRACT FROM AUTHOR]

INTRODUCTION: Although many cognitive measures have been developed to assess cognitive decline due to Alzheimer's disease (AD), there is little consensus on optimal measures, leading to varied assessments across research cohorts and clinical trials making it difficult to pool cognitive measures across studies. METHODS: We used a two‐stage approach to harmonize cognitive data across cohorts and derive a cross‐cohort score of cognitive impairment due to AD. First, we pool and harmonize cognitive data from international cohorts of varying size and ethnic diversity. Next, we derived cognitive composites that leverage maximal data from the harmonized dataset. RESULTS: We show that our cognitive composites are robust across cohorts and achieve greater or comparable sensitivity to AD‐related cognitive decline compared to the Mini‐Mental State Examination and Preclinical Alzheimer Cognitive Composite. Finally, we used an independent cohort validating both our harmonization approach and composite measures. DISCUSSION: Our easy to implement and readily available pipeline offers an approach for researchers to harmonize their cognitive data with large publicly available cohorts, providing a simple way to pool data for the development or validation of findings related to cognitive decline due to AD. [ABSTRACT FROM AUTHOR]

Experience and training are known to boost our skills and mold the brain's organization and function. Yet, structural plasticity and functional neurotransmission are typically studied at different scales (large-scale networks, local circuits), limiting our understanding of the adaptive interactions that support learning of complex cognitive skills in the adult brain. Here, we employ multimodal brain imaging to investigate the link between microstructural (myelination) and neurochemical (GABAergic) plasticity for decision-making. We test (in males, due to potential confounding menstrual cycle effects on GABA measurements in females) for changes in MRI-measured myelin, GABA, and functional connectivity before versus after training on a perceptual decision task that involves identifying targets in clutter. We demonstrate that training alters subcortical (pulvinar, hippocampus) myelination and its functional connectivity to visual cortex and relates to decreased visual cortex GABAergic inhibition. Modeling interactions between MRI measures of myelin, GABA, and functional connectivity indicates that pulvinar myelin plasticity interacts—through thalamocortical connectivity—with GABAergic inhibition in visual cortex to support learning. Our findings propose a dynamic interplay of adaptive microstructural and neurochemical plasticity in subcortico-cortical circuits that supports learning for optimized decision-making in the adult human brain. This multimodal brain imaging study reveals an interactive network of plasticity mechanisms: subcortical microstructural (myelination) plasticity and cortical neurochemical (GABAergic inhibition) plasticity interact through thalamocortical connectivity to support optimized perceptual decisions. [ABSTRACT FROM AUTHOR]

Cultural differences in visual perceptual learning (VPL) could be attributed to differences in the way that people from individualistic and collectivistic cultures preferentially attend to local objects (analytic) or global contexts (holistic). Indeed, individuals from different cultural backgrounds can adopt distinct processing styles and learn to differentially construct meaning from the environment. Therefore, the present work investigates if cross‐cultural differences in VPL can vary as a function of holistic processing. A shape discrimination task was used to investigate whether the individualistic versus collectivistic backgrounds of individuals affected the detection of global shapes embedded in cluttered backgrounds. Seventy‐seven participants—including Asian (collectivistic background) and European (individualistic background) students—were trained to discriminate between radial and concentric patterns. Singelis's self‐construal scale was also used to assess whether differences in learning could be attributed to independent or interdependent self‐construal. Results showed that collectivists had faster learning rates and better accuracy performance than individualists following training—thereby reflecting their tendency to attend holistically when learning to extract global forms. Further, we observed a negative association between independent self‐construal—which has previously been linked to analytic processing—with performance. This study provides insight into how socio‐cultural backgrounds affect VPL. [ABSTRACT FROM AUTHOR]

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. The authors present a machine learning approach that combines baseline multimodal data to accurately predict individualised trajectories of future pathological tau accumulation at asymptomatic and mildly impaired stages of Alzheimer's disease. [ABSTRACT FROM AUTHOR]

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 deci- sion 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 transcra- nial direct current stimulation (tDCS) in EV impairs learning, suggesting a direct link between visual cortex excitation and percep- tual 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. [ABSTRACT FROM AUTHOR]

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 ultrahigh-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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Functional brain development is not well understood. In the visual system, neurophysiological studies in nonhuman primates show quite mature neuronal properties near birth although visual function is itself quite immature and continues to develop over many months or years after birth. Our goal was to assess the relative development of two main visual processing streams, dorsal and ventral, using BOLD fMRI in an attempt to understand the global mechanisms that support the maturation of visual behavior. Seven infant macaque monkeys (Macaca mulatta) were repeatedly scanned, while anesthetized, over an age range of 102 to 1431 days. Large rotating checkerboard stimuli induced BOLD activation in visual cortices at early ages. Additionally we used static and dynamic Glass pattern stimuli to probe BOLD responses in primary visual cortex and two extrastriate areas: V4 and MT-V5. The resulting activations were analyzed with standard GLM and multivoxel pattern analysis (MVPA) approaches. We analyzed three contrasts: Glass pattern present/absent, static/dynamic Glass pattern presentation, and structured/random Glass pattern form. For both GLM and MVPA approaches, robust coherent BOLD activation appeared relatively late in comparison to the maturation of known neuronal properties and the development of behavioral sensitivity to Glass patterns. Robust differential activity to Glass pattern present/absent and dynamic/static stimulus presentation appeared first in V1, followed by V4 and MT-V5 at older ages; there was no reliable distinction between the two extrastriate areas. A similar pattern of results was obtained with the two analysis methods, although MVPA analysis showed reliable differential responses emerging at later ages than GLM. Although BOLD responses to large visual stimuli are detectable, our results with more refined stimuli indicate that global BOLD activity changes as behavioral performance matures. This reflects an hierarchical development of the visual pathways. Since fMRI BOLD reflects neural activity on a population level, our results indicate that, although individual neurons might be adult-like, a longer maturation process takes place on a population level. [ABSTRACT FROM AUTHOR]

When immersed in a new environment, we are challenged to decipher initially incomprehensible streams of sensory information. However, quite rapidly, the brain finds structure and meaning in these incoming signals, helping us to predict and prepare ourselves for future actions. This skill relies on extracting the statistics of event streams in the environment that contain regularities of variable complexity from simple repetitive patterns to complex probabilistic combinations. Here, we test the brain mechanisms that mediate our ability to adapt to the environment's statistics and predict upcoming events. By combining behavioral training and multisession fMRI in human participants (male and female), we track the corticostriatal mechanisms that mediate learning of temporal sequences as they change in structure complexity. We show that learning of predictive structures relates to individual decision strategy; that is, selecting the most probable outcome in a given context (maximizing) versus matching the exact sequence statistics. These strategies engage distinct human brain regions: maximizing engages dorsolateral prefrontal, cingulate, sensory-motor regions, and basal ganglia (dorsal caudate, putamen), whereas matching engages occipitotemporal regions (including the hippocampus) and basal ganglia (ventral caudate). Our findings provide evidence for distinct corticostriatal mechanisms that facilitate our ability to extract behaviorally relevant statistics to make predictions. [ABSTRACT FROM AUTHOR]

Early diagnosis of dementia is critical for assessing disease progression and potential treatment. State-or-the-art machine learning techniques have been increasingly employed to take on this diagnostic task. In this study, we employed Generalized Matrix Learning Vector Quantization (GMLVQ) classifiers to discriminate patients with Mild Cognitive Impairment (MCI) from healthy controls based on their cognitive skills. Further, we adopted a "Learning with privileged information" approach to combine cognitive and fMRI data for the classification task. The resulting classifier operates solely on the cognitive data while it incorporates the fMRI data as privileged information (PI) during training. This novel classifier is of practical use as the collection of brain imaging data is not always possible with patients and older participants. MCI patients and healthy age-matched controls were trained to extract structure from temporal sequences. We ask whether machine learning classifiers can be used to discriminate patients from controls and whether differences between these groups relate to individual cognitive profiles. To this end, we tested participants in four cognitive tasks: working memory, cognitive inhibition, divided attention, and selective attention.We also collected fMRI data before and after training on a probabilistic sequence learning task and extracted fMRI responses and connectivity as features for machine learning classifiers. Our results show that the PI guided GMLVQ classifiers outperform the baseline classifier that only used the cognitive data. In addition, we found that for the baseline classifier, divided attention is the only relevant cognitive feature. When PI was incorporated, divided attention remained the most relevant feature while cognitive inhibition became also relevant for the task. Interestingly, this analysis for the fMRI GMLVQ classifier suggests that (1) when overall fMRI signal is used as inputs to the classifier, the post-training session is most relevant; and (2) when the graph feature reflecting underlying spatiotemporal fMRI pattern is used, the pre-training session is most relevant. Taken together these results suggest that brain connectivity before training and overall fMRI signal after training are both diagnostic of cognitive skills in MCI. [ABSTRACT FROM AUTHOR]

Predicting future events based on previous knowledge about the environment is critical for successful everyday interactions. Here, we ask which brain regions support our ability to predict the future based on implicit knowledge about the past in young and older age. Combining behavioral and fMRI measurements, we test whether training on structured temporal sequences improves the ability to predict upcoming sensory events; we then compare brain regions involved in learning predictive structures between young and older adults. Our behavioral results demonstrate that exposure to temporal sequences without feedback facilitates the ability of young and older adults to predict the orientation of an upcoming stimulus. Our fMRI results provide evidence for the involvement of corticostriatal regions in learning predictive structures in both young and older learners. In particular, we showed learning-dependent fMRI responses for structured sequences in frontoparietal regions and the striatum (putamen) for young adults. However, for older adults, learning-dependent activations were observed mainly in subcortical (putamen, thalamus) regions but were weaker in frontoparietal regions. Significant correlations of learning-dependent behavioral and fMRI changes in these regions suggest a strong link between brain activations and behavioral improvement rather than general overactivation. Thus, our findings suggest that predicting future events based on knowledge of temporal statistics engages brain regions involved in implicit learning in both young and older adults. [ABSTRACT FROM AUTHOR]

Learning the statistics of the environment is critical for predicting upcoming events. However, little is known about how we translate previous knowledge about scene regularities to sensory predictions. Here, we ask whether patients with mild cognitive impairment due to Alzheimer's disease (MCI-AD) that are known to have spared implicit but impaired explicit recognition memory are able to learn temporal regularities and predict upcoming events. We tested the ability of MCI-AD patients and age-matched controls to predict the orientation of a test stimulus following exposure to sequences of leftwards or rightwards oriented gratings. Our results demonstrate that exposure to temporal sequences without feedback facilitates the ability to predict an upcoming stimulus in both MCI-AD patients and controls. Further, we show that executive cognitive control may account for individual variability in predictive learning. That is, we observed significant positive correlations of performance in attentional and working memory tasks with post-training performance in the prediction task. Taken together, these results suggest a mediating role of circuits involved in cognitive control (i.e. frontal circuits) that may support the ability for predictive learning in MCI-AD. [ABSTRACT FROM AUTHOR]

Learning the structure of the environment is critical for interpreting the current scene and predicting upcoming events. However, the brain mechanisms that support our ability to translate knowledge about scene statistics to sensory predictions remain largely unknown. Here we provide evidence that learning of temporal regularities shapes representations in early visual cortex that relate to our ability to predict sensory events. We tested the participants' ability to predict the orientation of a test stimulus after exposure to sequences of leftward- or rightward-oriented gratings. Using fMRI decoding, we identified brain patterns related to the observers' visual predictions rather than stimulus-driven activity. Decoding of predicted orientations following structured sequences was enhanced after training, while decoding of cued orientations following exposure to random sequences did not change. These predictive representations appear to be driven by the same large-scale neural populations that encode actual stimulus orientation and to be specific to the learned sequence structure. Thus our findings provide evidence that learning temporal structures supports our ability to predict future events by reactivating selective sensory representations as early as in primary visual cortex. [ABSTRACT FROM AUTHOR]

Hierarchical models of visual processing assume that global pattern recognition is contingent on the progressive integration of local elements across larger spatial regions, operating from early through intermediate to higher-level cortical regions. Here, we present results from neuropsychological fMRI that refute such models. We report two patients, one with lesions to intermediate ventral regions and the other with damage around the intraparietal sulcus (IPS). The patient with ventral damage showed normal behavioral and BOLD responses to global Glass patterns. The patient with IPS damage was impaired in discriminating global patterns and showed a lack of significant responses to these patterns in intermediate visual regions spared by the lesion. However, this patient did show BOLD activity to translational patterns, where local element relations are important. These results suggest that activation of intermediate ventral regions is not necessary to code global patterns; instead global patterns are coded in a heterarchical fashion. High-level regions of dorsal cortex are necessary to generate global pattern coding in intermediate ventral regions; in contrast, local integration processes are not sufficient. [ABSTRACT FROM AUTHOR]

Visual judgments critically depend on (1) the detection of meaningful items from cluttered backgrounds and (2) the discrimination of an item from highly similar alternatives. Learning and experience are known to facilitate these processes, but the specificity with which these processes operate is poorly understood. Here we use psychophysical measures of human participants to test learning in two types of commonly used tasks that target segmentation (signal-in-noise, or "coarse" tasks) versus the discrimination of highly similar items (feature difference, or "fine" tasks). First, we consider the processing of binocular disparity signals, examining performance on signal-in-noise and feature difference tasks after a period of training on one of these tasks. Second, we consider the generality of learning between different visual features, testing performance on both task types for displays defined by disparity, motion, or orientation. We show that training on a feature difference task also improves performance on signal-in-noise tasks, but only for the same visual feature. By contrast, training on a signal-in-noise task has limited benefits for fine judgments of the same feature but supports learning that generalizes to signal-in-noise tasks for other features. These findings indicate that commonly used signal-in-noise tasks require at least three distinct components: feature representations, signal-specific selection, and a generalized process that enhances segmentation. As such, there is clear potential to harness areas of commonality (both within and between cues) to improve impaired perceptual functions. [ABSTRACT FROM AUTHOR]

Successful interactions with the environment entail interpreting ambiguous sensory information. To address this challenge it has been suggested that the brain optimizes performance through experience. Here we used functional magnetic resonance imaging ( fMRI) to investigate whether perceptual experience modulates the cortical circuits involved in visual awareness. Using ambiguous visual stimuli (binocular rivalry or ambiguous structure-from-motion) we were able to disentangle the co-occurring influences of stimulus repetition and perceptual repetition. For both types of ambiguous stimuli we observed that the mere repetition of the stimulus evoked an entirely different pattern of activity modulations than the repetition of a particular perceptual interpretation of the stimulus. Regarding stimulus repetition, decreased fMRI responses were evident during binocular rivalry but weaker during 3-D motion rivalry. Perceptual repetition, on the other hand, entailed increased activity in stimulus-specific visual brain regions - for binocular rivalry in the early visual regions and for ambiguous structure-from-motion in both early as well as higher visual regions. This indicates that the repeated activation of a visual network mediating a particular percept facilitated its later reactivation. Perceptual repetition was also associated with a response change in the parietal cortex that was similar for the two types of ambiguous stimuli, possibly relating to the temporal integration of perceptual information. We suggest that perceptual repetition is associated with a facilitation of neural activity within and between percept-specific visual networks and parietal networks involved in the temporal integration of perceptual information, thereby enhancing the stability of previously experienced percepts. [ABSTRACT FROM AUTHOR]

Learning is known to facilitate our ability to detect targets in clutter and optimize brain processes for successful visual recognition. Previous brain-imaging studies have focused on identifying spatial patterns (i.e., brain areas) that change with learning, implicating occipitotemporal and frontoparietal areas. However, little is known about the interactions within this network that mediate learningdependent improvement in complex perceptual tasks (i.e., discrimination of visual forms in clutter). Here we take advantage of the complementary high spatial and temporal resolution of simultaneous EEG-fMRI to identify the learning-dependent changes in spatiotemporal brain patterns that mediate enhanced behavioral sensitivity in the discrimination of global forms after training. We measured the observers' choiceswhendiscriminating between concentric and radial patterns presented in noise before and after training. Similarly, we measured the choices of a pattern classifier when predicting each stimulus from EEG-fMRI signals. By comparing the performance of human observers and classifiers, we demonstrated that learning alters sensitivity to visual forms and EEG-fMRI activation patterns related to distinct visual recognition processes. In particular, behavioral improvement after training was associated with changes in (1) early processes involved in the integration of global forms in higher occipitotemporal and parietal areas, and (2) later processes related to categorical judgments in frontal circuits. Thus, our findings provide evidence that learning acts on distinct visual recognition processes and shapes feedforward interactions across brain areas to support performance in complex perceptual tasks. [ABSTRACT FROM AUTHOR]

Object perception is one of the most remarkable capacities of the primate brain. Owing to the large and indeterminate dimensionality of object space, the neural basis of object perception has been difficult to study and remains controversial. Recent work has provided a more precise picture of how 2D and 3D object structure is encoded in intermediate and higher-level visual cortices. Yet, other studies suggest that higher-level visual cortex represents categorical identity rather than structure. Furthermore, object responses are surprisingly adaptive to changes in environmental statistics, implying that learning through evolution, development, and also shorter-term experience during adulthood may optimize the object code. Future progress in reconciling these findings will depend on more effective sampling of the object domain and direct comparison of these competing hypotheses. [ABSTRACT FROM AUTHOR]

The ability to make categorical decisions and interpret sensory experiences is critical for survival and interactions across the lifespan. However, little is known about the human brain mechanisms that mediate the learning and representation of visual categories in aging. Here we combine behavioral measurements and fMRI measurements to investigate the neural processes that mediate flexible category learning in the aging human brain. Our findings show that training changes the decision criterion (i.e., categorical boundary) that young and older observers use for making categorical judgments. Comparing the behavioral choices of human observers with those of a pattern classifier based upon multivoxel fMRI signals, we demonstrate learning-dependent changes in similar cortical areas for young and older adults. In particular, we show that neural signals in occipito-temporal and posterior parietal regions change through learning to reflect the perceived visual categories. Information in these areas about the perceived visual categories is preserved in aging, whereas information content is compromised in more anterior parietal and frontal circuits. Thus, these findings provide novel evidence for flexible category learning in aging that shapes the neural representations of visual categories to reflect the observers' behavioral judgments. [ABSTRACT FROM AUTHOR]

Statistical learning methods are emerging as a valuable tool for decoding information from neural imaging data. The noisy signal and the limited number of training patterns that are typically recorded from functional brain imaging experiments pose a challenge for the application of statistical learning methods in the analysis of brain data. To overcome this difficulty, we propose using prior knowledge based on the behavioral performance of human observers to enhance the training of support vector machines (SVMs). We collect behavioral responses from human observers performing a categorization task during functional magnetic resonance imaging scanning. We use the psychometric function generated based on the observers behavioral choices as a distance constraint for training an SVM. We call this method behavior-constrained SVM (BCSVM). Our findings confirm that BCSVM outperforms SVM consistently. [ABSTRACT FROM PUBLISHER]

In perceiving 3D shape from ambiguous shading patterns, humans use the prior knowledge that the light is located above their head and slightly to the left. Although this observation has fascinated scientists and artists for a long time, the neural basis of this "light from above left" preference for the interpretation of 3D shape remains largely unexplored. Combining behavioral and functional MRI measurements coupled with multivoxel pattern analysis, we show that activations in early visual areas predict best the light source direction irrespective of the perceived shape, but activations in higher occipitotemporal and parietal areas predict better the perceived 3D shape irrespective of the light direction. These findings demonstrate that illumination is processed earlier than the representation of 3D shape in the visual system. In contrast to previous suggestions, we propose that prior knowledge about illumination is processed in a bottom-up manner and influences the interpretation of 3D structure at higher stages of processing. [ABSTRACT FROM AUTHOR]

We describe a compelling motion illusion elicited by a huge billboard placed along a street, depicting a building that contains strong perspective cues. When observers move fast along the opposite sidewalk, they perceive the depicted building as rotating in their direction of travel. This is a special case of the 'following', or 'pointing out of the picture', illusion that elicits a strong illusory motion percept. Here we discuss the cause of the illusory motion and suggest that the brain relies on the depicted perspective cues to infer a 3-D shape and a concomitant motion that is incompatible with the physical pictorial surface. [ABSTRACT FROM AUTHOR]

Long-term experience through development and evolution and shorter-term training in adulthood have bath been suggested to contribute to the optimization of visual functions that mediate our ability to interpret complex scenes. However, the brain plasticity mechanisms that mediate the detection of objects in cluttered scenes remain largely unknown. Here, we combine behavioral and functional MRI (fMRI) measurements to investigate the human-brain mechanisms that mediate our ability to learn statistical regularities and detect targets in clutter. We show two different routes to visual learning in clutter with discrete brain plasticity signatures. Specifically, opportunistic learning of regularities typical in natural contours (i.e., collinearity) can occur simply through frequent exposure, generalize across untrained stimulus features, and shape processing in occipitotemporal regions implicated in the representation of global forms. In contrast, learning to integrate discontinuities (i.e., elements orthogonal to contour paths) requires task-specific training (bootstrap-based learning), is stimulus-dependent, and enhances processing in intraparietal regions implicated in attention-gated learning. We propose that long-term experience with statistical regularities may facilitate opportunistic learning of collinear contours, whereas learning to integrate discontinuities entails bootstrap-based training for the detection of contours in clutter. These findings provide insights in understanding how long-term experience and short-term training interact to shape the optimization of visual recognition processes. [ABSTRACT FROM AUTHOR]

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Recently, evidence has emerged for a radial orientation bias in early visual cortex. These results predict that in early visual cortex a tangential bias should be present for motion direction. We tested this prediction in a human imaging study, using a translating random dot pattern that slowly rotated its motion direction 360° in cycles of 54 s. In addition, polar angle and eccentricity mapping were performed. This allowed the measurement of the BOLD response across the visual representations of the different retinotopic areas. We found that, in V1, V2, and V3, BOLD responses were consistently enhanced for centrifugal and centripetal motion, relative to tangential motion. The relative magnitude of the centrifugal and centripetal response biases changed with visual eccentricity. We found no motion direction biases in MT+. These results are in line with previously observed anisotropies in motion sensitivity across the visual field. However, the observation of radial motion biases in early visual cortex is surprising considering the evidence for a radial orientation bias. An additional experiment was performed to resolve this apparent conflict in results. The additional experiment revealed that the observed motion direction biases most likely originate from anisotropies in long range horizontal connections within visual cortex. Hum Brain Mapp, 2009. © 2009 Wiley-Liss, Inc. [ABSTRACT FROM AUTHOR]

Recognition of actions and complex movements is fundamental for social interactions and action understanding. While the relationship between motor expertise and visual recognition of body movements has received a vast amount of interest, the role of visual learning remains largely unexplored. Combining psychophysics and functional magnetic resonance imaging (fMRI) experiments, we investigated neural correlates of visual learning of complex movements. Subjects were trained to visually discriminate between very similar complex movement stimuli generated by motion morphing that were either compatible (experiments 1 and 2) or incompatible (experiment 3) with human movement execution. Employing an fMRI adaptation paradigm as index of discriminability, we scanned human subjects before and after discrimination training. The results of experiment 1 revealed three different effects as a consequence of training: (1) Emerging fMRI-selective adaptation in general motion-related areas (hMT/V5+, KO/V3b) for the differences between human-like movements. (2) Enhanced of fMRI-selective adaptation already present before training in biological motion-related areas (pSTS, FBA). (3) Changes covarying with task difficulty in frontal areas. Moreover, the observed activity changes were specific to the trained movement patterns (experiment 2). The results of experiment 3, testing artificial movement stimuli, were strikingly similar to the results obtained for human movements. General and biological motion-related areas showed movement-specific changes in fMRI-selective adaptation for the differences between the stimuli after training. These results support the existence of a powerful visual machinery for the learning of complex motion patterns that is independent of motor execution. We thus propose a key role of visual learning in action recognition. [ABSTRACT FROM AUTHOR]

Perceiving the three-dimensional (3D) properties of the environment relies on the brain bringing together ambiguous cues (e.g., binocular disparity, shading, texture) with information gained from short- and long-term experience. Perceptual aftereffects, in which the perception of an ambiguous 3D stimulus is biased away from the shape of a previously viewed stimulus, provide a sensitive means of probing this process, yet little is known about their neural basis. Here, we investigate 3D aftereffects using psychophysical and functional MRI(fMRI) adaptation paradigms to gain insight into the cortical circuits that mediate the perceptual interpretation of ambiguous depth signals. Using two classic bistable stimuli (Mach card, kinetic depth effect), we test aftereffects produced by 3D shapes defined by binocular (disparity) or monocular (texture, shading) depth cues. We show that the processing of ambiguous 3D stimuli in dorsal visual cortical areas (V3B/KO, V7) and posterior parietal regions is modulated by adaptation in line with perceptual aftereffects. Similar behavioral and fMRI adaptation effects for the two types of bistable stimuli suggest common neural substrates for depth aftereffects independent of the inducing depth cues (disparity, texture, shading). In line with current thinking about the role of adaptation in sensory optimization, our findings provide evidence that estimation of 3D shape in dorsal cortical areas takes account of the adaptive context to resolve depth ambiguity and interpret 3D structure. [ABSTRACT FROM AUTHOR]

Processing of binocular disparity is thought to be widespread throughout cortex, highlighting its importance for perception and action. Yet the computations and functional roles underlying this activity across areas remain largely unknown. Here, we trace the neural representations mediating depth perception across human brain areas using multivariate analysis methods and high-resolution imaging. Presenting disparity-defined planes, we determine functional magnetic resonance imaging (fMRI) selectivity to near versus far depth positions. First, we test the perceptual relevance of this selectivity, comparing the pattern-based decoding of fMRI responses evoked by random dot stereograms that support depth perception (correlated RDS) with the decoding of stimuli containing disparities to which the perceptual system is blind (anticorrelated RDS). Preferential disparity selectivity for correlated stimuli in dorsal (visual and parietal) areas and higher ventral area LO (lateral occipital area) suggests encoding of perceptually relevant information, in contrast to early (V1, V2) and intermediate ventral (V3v, V4) visual cortical areas that show similar selectivity for both correlated and anticorrelated stimuli. Second, manipulating disparity parametrically, we show that dorsal areas encode the metric disparity structure of the viewed stimuli (i.e., disparity magnitude), whereas ventral area LO appears to represent depth position in a categorical manner (i.e., disparity sign). Our findings suggest that activity in both visual streams is commensurate with the use of disparity for depth perception but the neural computations may differ. Intriguingly, perceptually relevant responses in the dorsal stream are tuned to disparity content and emerge at a comparatively earlier stage than categorical representations for depth position in the ventral stream. [ABSTRACT FROM AUTHOR]

Understanding complex movements and abstract action goals is an important skill for our social interactions. Successful social interactions entail understanding of actions at different levels of action description, ranging from detailed movement trajectories that support learning of complex motor skills through imitation to distinct features of actions that allow us to discriminate between action goals and different action styles. Previous studies have implicated premotor, parietal, and superior temporal areas in action understanding. However, the role of these different cortical areas in action understanding at different levels of action description remains largely unknown. We addressed this question using advanced animation and stimulus generation techniques in combination with sensitive functional magnetic resonance imaging adaptation or repetition suppression methods. We tested the neural sensitivity of fronto-parietal and visual areas to differences in the kinematics and goals of actions using kinematic morphs of arm movements. Our findings provide novel evidence for differential involvement of ventral premotor, parietal, and temporal regions in action understanding. We show that the ventral premotor cortex encodes the physical similarity between movement trajectories and action goals that are important for exact copying of actions and the acquisition of complex motor skills. In contrast, parietal regions and the superior temporal sulcus process the perceptual similarity between movements and may support the perception and imitation of abstract action goals and movement styles. Thus, our findings propose that fronto-parietal and visual areas involved in action understanding mediate a cascade of visual-motor processes at different levels of action description from exact movement copies to abstract action goals achieved with different movement styles. [ABSTRACT FROM AUTHOR]

Despite the importance of visual categorization for interpreting sensory experiences, little is known about the neural representations that mediate categorical decisions in the human brain. Here, we used psychophysics and pattern classification for the analysis of functional magnetic resonance imaging data to predict the features critical for categorical decisions from brain activity when observers categorized the same stimuli using different rules. Although a large network of cortical and subcortical areas contain information about visual categories, we show that only a subset of these areas shape their selectivity to reflect the behaviorally relevant features rather than simply physical similarity between stimuli. Specifically, temporal and parietal areas show selectivity for the perceived form and motion similarity, respectively. In contrast, frontal areas and the striatum represent the conjunction of spatiotemporal features critical for complex and adaptive categorization tasks and potentially modulate selectivity in temporal and parietal areas. These findings provide novel evidence for flexible neural coding in the human brain that translates sensory experiences to categorical decisions by shaping neural representations across a network of areas with dissociable functional roles in visual categorization. [ABSTRACT FROM AUTHOR]

The perception of motion provides valuable interpolations of the visual scene. This fundamental capacity of the visual system is evident in apparent rotation: by presenting only two images of an object rotated in space, a vivid illusion of a smooth apparent motion in three dimensions can be induced. The unseen interpolated rotation views are filled in by the visual system. In the present study, we identified the cortical network responsible for this filling-in process. We argue that cross talk between areas of the ventral and dorsal visual pathways promote the illusion of smooth apparent rotation. Most interestingly, the network represents the unseen object views. Using functional magnetic resonance adaptation, we are able to show that the cortical network selectively adapts to the illusory object views. Our findings provide strong evidence for cortical representations of three-dimensional rotating objects that are view invariant with respect to the rotation path. Furthermore, our results confirm psychophysical investigations that unseen interpolated rotation views can be primed by apparent motion. By applying functional magnetic resonance adaptation, we show for the first time cortical adaptation to unseen objects. Together, our neuroimaging study advances the understanding of the cortical mechanisms mediating the influence of motion on object processing. [ABSTRACT FROM AUTHOR]

Expertise in recognizing objects in cluttered scenes is a critical skill for our interactions in complex environments and is thought to develop with learning. However, the neural implementation of object learning across stages of visual analysis in the human brain remains largely unknown. Using combined psychophysics and functional magnetic resonance imaging (fMRI), we show a link between shape-specific learning in cluttered scenes and distributed neuronal plasticity in the human visual cortex. We report stronger fMRI responses for trained than untrained shapes across early and higher visual areas when observers learned to detect low-salience shapes in noisy backgrounds. However, training with high-salience pop-out targets resulted in lower fMRI responses for trained than untrained shapes in higher occipitotemporal areas. These findings suggest that learning of camouflaged shapes is mediated by increasing neural sensitivity across visual areas to bolster target segmentation and feature integration. In contrast, learning of prominent pop-out shapes is mediated by associations at higher occipitotemporal areas that support sparser coding of the critical features for target recognition. We propose that the human brain learns novel objects in complex scenes by reorganizing shape processing across visual areas, while taking advantage of natural image correlations that determine the distinctiveness of target shapes. Learning to recognize objects involves distinct neural changes in several visual cortical areas. [ABSTRACT FROM AUTHOR]