16 results on '"Vasilis M. Karlaftis"'
Search Results
2. Microstructural and neurochemical plasticity mechanisms interact to enhance human perceptual decision-making.
- Author
-
Joseph J Ziminski, Polytimi Frangou, Vasilis M Karlaftis, Uzay Emir, and Zoe Kourtzi
- Subjects
Biology (General) ,QH301-705.5 - Abstract
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.
- Published
- 2023
- Full Text
- View/download PDF
3. A New Remote Guided Method for Supervised Web-Based Cognitive Testing to Ensure High-Quality Data: Development and Usability Study
- Author
-
Victoria Leong, Kausar Raheel, Jia Yi Sim, Kriti Kacker, Vasilis M Karlaftis, Chrysoula Vassiliu, Kastoori Kalaivanan, S H Annabel Chen, Trevor W Robbins, Barbara J Sahakian, and Zoe Kourtzi
- Subjects
Computer applications to medicine. Medical informatics ,R858-859.7 ,Public aspects of medicine ,RA1-1270 - Abstract
BackgroundThe 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. ObjectiveThis study aims to develop and validate a new supervised online testing methodology, remote guided testing (RGT). MethodsA 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. ResultsThe 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
- Published
- 2022
- Full Text
- View/download PDF
4. Fine-scale computations for adaptive processing in the human brain
- Author
-
Elisa Zamboni, Valentin G Kemper, Nuno Reis Goncalves, Ke Jia, Vasilis M Karlaftis, Samuel J Bell, Joseph Giorgio, Reuben Rideaux, Rainer Goebel, and Zoe Kourtzi
- Subjects
visual cortex ,adaptation ,fMRI ,layer ,laminar ,functional connectivity ,Medicine ,Science ,Biology (General) ,QH301-705.5 - Abstract
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 capitalise on the sub-millimetre resolution of ultra-high field imaging to examine functional magnetic resonance imaging signals across cortical depth and discern competing hypotheses about the brain mechanisms (feedforward vs. feedback) that mediate adaptive processing. We demonstrate layer-specific suppressive processing within visual cortex, as indicated by stronger BOLD decrease in superficial and middle than deeper layers for gratings that were repeatedly presented at the same orientation. Further, we show altered functional connectivity for adaptation: enhanced feedforward connectivity from V1 to higher visual areas, short-range feedback connectivity between V1 and V2, and long-range feedback occipito-parietal connectivity. Our findings provide evidence for a circuit of local recurrent and feedback interactions that mediate rapid brain plasticity for adaptive information processing.
- Published
- 2020
- Full Text
- View/download PDF
5. Neurochemical and functional interactions for improved perceptual decisions through training
- Author
-
Ke Jia, Polytimi Frangou, Vasilis M. Karlaftis, Joseph J. Ziminski, Joseph Giorgio, Reuben Rideaux, Elisa Zamboni, Victoria Hodgson, Uzay Emir, Zoe Kourtzi, Frangou, Polytimi [0000-0003-3524-0306], Karlaftis, Vasilis M [0000-0003-1285-1593], Ziminski, Joseph J [0000-0003-4286-6868], Zamboni, Elisa [0000-0001-9200-8031], Kourtzi, Zoe [0000-0001-9441-7832], and Apollo - University of Cambridge Repository
- Subjects
perceptual decisions ,learning ,Physiology ,General Neuroscience ,MR spectroscopy ,functional connectivity ,Brain ,Glutamic Acid ,Prefrontal Cortex ,transcranial direct current stimulation ,Magnetic Resonance Imaging ,Visual Cortex - Abstract
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.
- Published
- 2022
6. A New Remote Guided Method for Supervised Web-Based Cognitive Testing to Ensure High Quality Data
- Author
-
Barbara J. Sahakian, Chrysoula Vassiliu, Kriti Kacker, Trevor W. Robbins, S.H. Annabel Chen, Kausar Raheel, Vasilis M. Karlaftis, Victoria Leong, Zoe Kourtzi, Jia Yi Sim, Karlaftis, Vasileios [0000-0003-1285-1593], Vassiliu, Chrysoula [0000-0001-9514-5074], Robbins, Trevor [0000-0003-0642-5977], Sahakian, Barbara [0000-0001-7352-1745], Kourtzi, Zoe [0000-0001-9441-7832], and Apollo - University of Cambridge Repository
- Subjects
Internet ,Information retrieval ,learning ,business.industry ,SARS-CoV-2 ,COVID-19 ,Neuropsychological Tests ,executive functions ,Cognitive test ,Young Adult ,web-based testing ,Data quality ,neurocognitive assessment ,Web application ,Humans ,Preprint ,business ,Psychology ,Pandemics - Abstract
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.
- Published
- 2022
7. Brain stimulation boosts perceptual learning by altering sensory GABAergic plasticity and functional connectivity
- Author
-
Vasilis M. Karlaftis, Zoe Kourtzi, Cameron Higgins, Polytimi Frangou, Charlotte J. Stagg, Uzay E. Emir, Diego Vidaurre, and Joseph J. Ziminski
- Subjects
genetic structures ,Transcranial direct-current stimulation ,medicine.medical_treatment ,media_common.quotation_subject ,Posterior parietal cortex ,Sensory system ,Visual cortex ,medicine.anatomical_structure ,Perceptual learning ,Brain stimulation ,Perception ,medicine ,GABAergic ,Psychology ,Neuroscience ,media_common - Abstract
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.
- Published
- 2021
8. Functional Interactions between Sensory and Memory Networks for Adaptive Behavior
- Author
-
Joseph J. Ziminski, Zoe Kourtzi, Polytimi Frangou, Reuben Rideaux, Joseph Giorgio, Elisa Zamboni, Vasilis M. Karlaftis, Karlaftis, Vasileios [0000-0003-1285-1593], Kourtzi, Zoe [0000-0001-9441-7832], and Apollo - University of Cambridge Repository
- Subjects
Sensory processing ,Computer science ,Brain activity and meditation ,Cognitive Neuroscience ,medicine.medical_treatment ,media_common.quotation_subject ,Prefrontal Cortex ,Sensory system ,adaptation ,Cellular and Molecular Neuroscience ,Neuroimaging ,Perception ,Adaptation, Psychological ,medicine ,AcademicSubjects/MED00385 ,media_common ,Adaptive behavior ,Brain Mapping ,medicine.diagnostic_test ,GABAergic inhibition ,AcademicSubjects/SCI01870 ,fMRI ,functional connectivity ,Brain ,Magnetic Resonance Imaging ,Dorsolateral prefrontal cortex ,medicine.anatomical_structure ,Original Article ,AcademicSubjects/MED00310 ,repetition suppression ,Functional magnetic resonance imaging ,Neuroscience - Abstract
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.
- Published
- 2021
- Full Text
- View/download PDF
9. A New Remote Guided Method for Supervised Web-Based Cognitive Testing to Ensure High-Quality Data: Development and Usability Study (Preprint)
- Author
-
Victoria Leong, Kausar Raheel, Jia Yi Sim, Kriti Kacker, Vasilis M Karlaftis, Chrysoula Vassiliu, Kastoori Kalaivanan, S H Annabel Chen, Trevor W Robbins, Barbara J Sahakian, and Zoe Kourtzi
- Abstract
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.
- Published
- 2021
10. A New Remote Guided Method for Supervised Web-Based Cognitive Testing to Ensure High-Quality Data: Development and Usability Study
- Author
-
Victoria Leong, Kausar Raheel, Jia Yi Sim, Kriti Kacker, Vasilis M Karlaftis, Chrysoula Vassiliu, Kastoori Kalaivanan, S H Annabel Chen, Trevor W Robbins, Barbara J Sahakian, Zoe Kourtzi, School of Social Sciences, Lee Kong Chian School of Medicine (LKCMedicine), Centre for Research and Development in Learning (CRADLE), and National Institute of Education
- Subjects
Internet ,Original Paper ,learning ,SARS-CoV-2 ,COVID-19 ,General [Social sciences] ,Health Informatics ,Neuropsychological Tests ,executive functions ,Web-Based Testing ,Neurocognitive Assessment ,Young Adult ,web-based testing ,neurocognitive assessment ,Humans ,Pandemics - Abstract
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.
- Published
- 2021
11. Fine-scale computations for adaptive processing in the human brain
- Author
-
Zoe Kourtzi, Elisa Zamboni, Reuben Rideaux, Valentin G. Kemper, Vasilis M. Karlaftis, Nuno Reis Goncalves, Samuel Bell, Ke Jia, Joseph Giorgio, Rainer Goebel, Zamboni, Elisa [0000-0001-9200-8031], Karlaftis, Vasilis M [0000-0003-1285-1593], Rideaux, Reuben [0000-0001-8416-005X], Kourtzi, Zoe [0000-0001-9441-7832], Apollo - University of Cambridge Repository, Audition, Vision, and RS: FPN CN 1
- Subjects
Adult ,Male ,0301 basic medicine ,genetic structures ,QH301-705.5 ,Computer science ,Science ,Adaptation, Biological ,Sensory system ,adaptation ,General Biochemistry, Genetics and Molecular Biology ,Visual processing ,Young Adult ,03 medical and health sciences ,0302 clinical medicine ,Neuroplasticity ,medicine ,Humans ,Biology (General) ,visual cortex ,030304 developmental biology ,0303 health sciences ,General Immunology and Microbiology ,medicine.diagnostic_test ,Orientation (computer vision) ,General Neuroscience ,fMRI ,layer ,functional connectivity ,Information processing ,Feed forward ,General Medicine ,Human brain ,Magnetic Resonance Imaging ,laminar ,030104 developmental biology ,Visual cortex ,medicine.anatomical_structure ,Medicine ,Female ,Functional magnetic resonance imaging ,Neuroscience ,030217 neurology & neurosurgery ,Research Article ,Human - Abstract
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.
- Published
- 2020
12. Author response: Fine-scale computations for adaptive processing in the human brain
- Author
-
Joseph Giorgio, Nuno Reis Goncalves, Vasilis M. Karlaftis, Zoe Kourtzi, Valentin G. Kemper, Reuben Rideaux, Samuel Bell, Ke Jia, Rainer Goebel, and Elisa Zamboni
- Subjects
Scale (ratio) ,Computer science ,Computation ,Computational science - Published
- 2020
13. Multimodal imaging of brain connectivity reveals predictors of individual decision strategy in statistical learning
- Author
-
Peter Tino, Rui Wang, Joseph Giorgio, Vasilis M. Karlaftis, Petra E. Vértes, Yuan Shen, Zoe Kourtzi, Andrew E. Welchman, Karlaftis, Vasilis M [0000-0003-1285-1593], Kourtzi, Zoe [0000-0001-9441-7832], and Apollo - University of Cambridge Repository
- Subjects
2.3 Psychological, social and economic factors ,Social Psychology ,Biomedical ,Computer science ,1.2 Psychological and socioeconomic processes ,1.1 Normal biological development and functioning ,Experimental and Cognitive Psychology ,Bioengineering ,Basic Behavioral and Social Science ,Article ,03 medical and health sciences ,Behavioral Neuroscience ,0302 clinical medicine ,Neuroplasticity ,Behavioral and Social Science ,Prefrontal cortex ,030304 developmental biology ,Structure (mathematical logic) ,0303 health sciences ,Working memory ,Statistical learning ,FOS: Clinical medicine ,Neurosciences ,Brain Disorders ,Variable (computer science) ,Mental Health ,1701 Psychology ,Decision strategy ,Neurological ,Graph (abstract data type) ,1109 Neurosciences ,030217 neurology & neurosurgery ,Cognitive psychology - Abstract
Successful human behavior depends on the brain's ability to extract meaningful structure from information streams and make predictions about future events. Individuals can differ markedly in the decision strategies they use to learn the environment's statistics, yet we have little idea why. Here, we investigate whether the brain networks involved in learning temporal sequences without explicit reward differ depending on the decision strategy that individuals adopt. We demonstrate that individuals alter their decision strategy in response to changes in temporal statistics and engage dissociable circuits: extracting the exact sequence statistics relates to plasticity in motor cortico-striatal circuits, while selecting the most probable outcomes relates to plasticity in visual, motivational and executive cortico-striatal circuits. Combining graph metrics of functional and structural connectivity, we provide evidence that learning-dependent changes in these circuits predict individual decision strategy. Our findings propose brain plasticity mechanisms that mediate individual ability for interpreting the structure of variable environments.
- Published
- 2019
14. Learning to optimize perceptual decisions through suppressive interactions in the human brain
- Author
-
Polytimi, Frangou, Uzay E, Emir, Vasilis M, Karlaftis, Caroline, Nettekoven, Emily L, Hinson, Stephanie, Larcombe, Holly, Bridge, Charlotte J, Stagg, and Zoe, Kourtzi
- Subjects
Adult ,Male ,Magnetic Resonance Spectroscopy ,Decision Making ,Brain ,Magnetic Resonance Imaging ,Article ,Judgment ,Young Adult ,Visual Perception ,Humans ,Learning ,Female ,Photic Stimulation ,gamma-Aminobutyric Acid ,Visual Cortex - Abstract
Translating noisy sensory signals to perceptual decisions is critical for successful interactions in complex environments. Learning is known to improve perceptual judgments by filtering external noise and task-irrelevant information. Yet, little is known about the brain mechanisms that mediate learning-dependent suppression. Here, we employ ultra-high field magnetic resonance spectroscopy of GABA to test whether suppressive processing in decision-related and visual areas facilitates perceptual judgments during training. We demonstrate that parietal GABA relates to suppression of task-irrelevant information, while learning-dependent changes in visual GABA relate to enhanced performance in target detection and feature discrimination tasks. Combining GABA measurements with functional brain connectivity demonstrates that training on a target detection task involves local connectivity and disinhibition of visual cortex, while training on a feature discrimination task involves inter-cortical interactions that relate to suppressive visual processing. Our findings provide evidence that learning optimizes perceptual decisions through suppressive interactions in decision-related networks., Learning improves perceptual decisions by enhancing the brain's ability to filter noise and irrelevant information. Here, the authors show that GABAergic inhibition in decision-making circuits supports our ability to optimize perceptual judgments through learning and experience.
- Published
- 2018
15. White-Matter Pathways for Statistical Learning of Temporal Structures
- Author
-
Vasilis M, Karlaftis, Rui, Wang, Yuan, Shen, Peter, Tino, Guy, Williams, Andrew E, Welchman, and Zoe, Kourtzi
- Subjects
Adult ,Male ,vision ,Decision Making ,Brain ,brain imaging ,New Research ,diffusion tensor imaging ,White Matter ,Markov Chains ,Young Adult ,statistical learning ,Neural Pathways ,8.1 ,Humans ,Learning ,Sensory and Motor Systems ,Female ,brain plasticity - Abstract
Extracting the statistics of event streams in natural environments is critical for interpreting current events and predicting future ones. The brain is known to rapidly find structure and meaning in unfamiliar streams of sensory experience, often by mere exposure to the environment (i.e., without explicit feedback). Yet, we know little about the brain pathways that support this type of statistical learning. Here, we test whether changes in white-matter (WM) connectivity due to training relate to our ability to extract temporal regularities. By combining behavioral training and diffusion tensor imaging (DTI), we demonstrate that humans adapt to the environment’s statistics as they change over time from simple repetition to probabilistic combinations. In particular, we show that learning relates to the decision strategy that individuals adopt when extracting temporal statistics. We next test for learning-dependent changes in WM connectivity and ask whether they relate to individual variability in decision strategy. Our DTI results provide evidence for dissociable WM pathways that relate to individual strategy: extracting the exact sequence statistics (i.e., matching) relates to connectivity changes between caudate and hippocampus, while selecting the most probable outcomes in a given context (i.e., maximizing) relates to connectivity changes between prefrontal, cingulate and basal ganglia (caudate, putamen) regions. Thus, our findings provide evidence for distinct cortico-striatal circuits that show learning-dependent changes of WM connectivity and support individual ability to learn behaviorally-relevant statistics.
- Published
- 2017
16. How structure sculpts function: Unveiling the contribution of anatomical connectivity to the brain’s spontaneous correlation structure
- Author
-
Timothy J. Van Hartevelt, Gorka Zamora-López, Morten L. Kringelbach, Vasilis M. Karlaftis, Henrique M. Fernandes, Zoe Kourtzi, Gustavo Deco, Ruggero G. Bettinardi, Karlaftis, Vasileios [0000-0003-1285-1593], Kourtzi, Zoe [0000-0001-9441-7832], and Apollo - University of Cambridge Repository
- Subjects
Similarity (geometry) ,Computer science ,Generalization ,General Physics and Astronomy ,Topology ,Network topology ,01 natural sciences ,Measure (mathematics) ,Diffusion ,03 medical and health sciences ,0302 clinical medicine ,0103 physical sciences ,Journal Article ,Humans ,Computer Simulation ,010306 general physics ,Mathematical Physics ,Topology (chemistry) ,Applied Mathematics ,Brain ,Statistical and Nonlinear Physics ,Numerical Analysis, Computer-Assisted ,Function (mathematics) ,Complex network ,FOS: Biological sciences ,Quantitative Biology - Neurons and Cognition ,Content (measure theory) ,Neurons and Cognition (q-bio.NC) ,Networks ,Nerve Net ,030217 neurology & neurosurgery - Abstract
Intrinsic brain activity is characterized by highly organized co-activations between different regions, forming clustered spatial patterns referred to as resting-state networks. The observed co-activation patterns are sustained by the intricate fabric of millions of interconnected neurons constituting the brain's wiring diagram. However, as for other real networks, the relationship between the connectional structure and the emergent collective dynamics still evades complete understanding. Here, we show that it is possible to estimate the expected pair-wise correlations that a network tends to generate thanks to the underlying path structure. We start from the assumption that in order for two nodes to exhibit correlated activity, they must be exposed to similar input patterns from the entire network. We then acknowledge that information rarely spreads only along a unique route but rather travels along all possible paths. In real networks, the strength of local perturbations tends to decay as they propagate away from the sources, leading to a progressive attenuation of the original information content and, thus, of their influence. Accordingly, we define a novel graph measure, topological similarity, which quantifies the propensity of two nodes to dynamically correlate as a function of the resemblance of the overall influences they are expected to receive due to the underlying structure of the network. Applied to the human brain, we find that the similarity of whole-network inputs, estimated from the topology of the anatomical connectome, plays an important role in sculpting the backbone pattern of time-average correlations observed at rest. The quest to understand how structure shapes function lies at the heart of a broad spectrum of disciplines, ranging from biology to network science. For over a decade, many efforts have been devoted to investigating the impact of different network features, e.g., hubs, clustering, or communities, on the collective behaviour of dynamical processes on complex networks such as spreading phenomena and synchronization. However, a unique answer to this question is not possible, because the emerging network activity is a product of the interplay between the network's topology, the particular local dynamics governing nodes' behavior, and the coupling function defining how information is transferred: network's topology shapes, but does not determine, the collective dynamics. The question is thus whether we can estimate what is the contribution of the structure alone, and which are the most relevant topological features in sculpting the emergent functional relations. Here, we have shown that the global path structure of the network is what truly determines the contribution of the network over the collective dynamics, as it implicitly incorporates information about all other network features, e.g., degree-distributions or modules. The expected magnitude of synchrony or correlation between two nodes is largely governed by the common inputs they receive from all other nodes, given that information propagates along all possible paths of any length. We quantify this pair-wise, whole-network affinity introducing a network measure, the topological similarity ( T). Formally, T is the direct relation between the structure of a network and the pattern of functional relations that it tends to produce. Applied to the human brain, we find that the similarity of whole-network inputs, defined by the topology of the underlying anatomical connectome, plays an important role in sculpting the backbone pattern of time-average correlations observed at rest. This confirms the pivotal relevance of the path structure in sculpting the network's correlations due to spontaneous activity. This work was supported by (R.G.B.) the FI-DGR scholarship of the Catalan Government through the Agència de Gestió d'Ajuts Universitari i de Recerca, under Agreement No. 2013FI-B1-00099, (G.Z.L.) the European Union's Horizon 2020 research and innovation programme under Grant Agreement No. 720270 (HBP SGA1), (G.D.) the European Research Council Advanced Grant: DYSTRUCTURE (295129) and the Spanish Research Project No. PSI2013-42091-P, (Z.K.) European Community's Seventh Framework Programme [FP7/2007-2013] under agreement PITN-GA-2011-290011, (V.M.K.) European Community's Seventh Framework Programme [FP7/2007-2013] under Agreement No. PITN-GA-2012-316746 and (M.L.K.) by the European Research Council Consolidator Grant No. CAREGIVING (615539).
- Published
- 2017
Catalog
Discovery Service for Jio Institute Digital Library
For full access to our library's resources, please sign in.