Inhibitory mechanisms for visual learning in the human brain

Identifying targets in cluttered scenes is critical for our interactions in complex environments. Our visual system is challenged to both detect elusive targets that we may want to avoid or chase and discriminate between targets that are highly similar. These tasks require our visual system to become an expert at detecting distinctive features that help us differentiate between indistinguishable targets. As the human brain is trained on this type of visual tasks, we observe changes in its function that correspond to improved performance. We use functional brain imaging, to measure learning-dependent modulations of brain activation and investigate the processes that mediate functional brain plasticity. I propose that dissociable brain mechanisms are engaged when detecting targets in clutter vs. discriminating between highly similar targets: for the former, background clutter needs to be suppressed for the target to be recognised, whereas for the latter, neurons are tuned to respond to fine differences. Although GABAergic inhibition is known to suppress redundant neuronal populations and tune neuronal representations, its role in visual learning remains largely unexplored. Here, I propose that GABAergic inhibition plays an important role in visual plasticity through training on these tasks. The purpose of my PhD is to investigate the inhibitory mechanisms that mediate visual perceptual learning; in particular, learning to detect patterns in visual clutter and discriminate between highly similar patterns. I show that BOLD signals as measured by functional Magnetic Resonance Imaging (fMRI) do not differentiate between the two proposed mechanisms. In contrast, Magnetic Resonance Spectroscopy (MRS) provides strong evidence for the distinct involvement of GABAergic inhibition in visual plasticity. Further, my findings show GABA changes during the time-course of learning providing evidence for a distinct role of GABA in learning-dependent plasticity across different brain regions involved in visual learning. Finally, I test the causal link between inhibitory contributions and visual plasticity using a brain stimulation intervention that perturbs the excitation-inhibition balance in the visual cortex and facilitates learning

GABA, not BOLD, reveals dissociable learning-dependent plasticity mechanisms in the human brain.

Experience and training have been shown to facilitate our ability to extract and discriminate meaningful patterns from cluttered environments. Yet, the human brain mechanisms that mediate our ability to learn by suppressing noisy and irrelevant signals remain largely unknown. To test the role of suppression in perceptual learning, we combine fMRI with MR Spectroscopy measurements of GABA, as fMRI alone does not allow us to discern inhibitory vs. excitatory mechanisms. Our results demonstrate that task-dependent GABAergic inhibition relates to functional brain plasticity and behavioral improvement. Specifically, GABAergic inhibition in the occipito-temporal cortex relates to dissociable learning mechanisms: decreased GABA for noise filtering, while increased GABA for feature template retuning. Perturbing cortical excitability during training with tDCs alters performance in a task-specific manner, providing evidence for a direct link between suppression and behavioral improvement. Our findings propose dissociable GABAergic mechanisms that optimize our ability to make perceptual decisions through training

Development of an algorithm that identifies the optimum final point of the stimulation electrode for deep brain stimulation, using the beta frequency band of intranuclear recordings

88 σ.Αντικείμενο της παρούσας διπλωματικής εργασίας αποτελεί η δημιουργία ενός αλγορίθμου που υποστηρίζει την ταυτοποίηση της βέλτιστης τελικής θέσης του ηλεκτροδίου της εν τω βάθει εγκεφαλικής διέγερσης σε ασθενείς με τη νόσο του Parkinson. Σύμφωνα με πρόσφατη έρευνα στο εργαστήριο Βιοϊατρικών Προσομοιώσεων και Απεικονιστικής Τεχνολογίας του Εθνικού Μετσόβιου Πολυτεχνείου υπάρχουν ενδείξεις ότι η πυροδότηση στη β – ζώνη συνδέεται με αντίστοιχη αύξηση της ενέργειας στη β – ζώνη στα δυναμικά τοπικού πεδίου που λαμβάνονται από ενδοπυρηνικές καταγραφές μικροηλεκτροδίων εντός της αισθητικοκινητικής περιοχής του υποθαλαμικού πυρήνα. Η παρούσα διπλωματική εργασία διευρύνει τη μελέτη της σχέσης μεταξύ της αύξησης της ενέργειας στη β – ζώνη και των κλινικών αποτελεσμάτων της χειρουργικής διαδικασίας. Οι ενδοπυρηνικές καταγραφές μικροηλεκτροδίων προέρχονται από 18 ασθενείς της Νευροχειρουργικής Κλινικής του Νοσοκομείου Ευαγγελισμός. Η υπόθεση μας περί βελτίωσης των κλινικών κινητικών συμπτωμάτων όταν στην περιοχή όπου εφαρμόζεται η εν τω βάθει διέγερση έχει παρατηρηθεί η ύπαρξη μέγιστης κορύφωσης πλάτους της καταγραφής στη β – ζώνη συχνοτήτων υποστηρίζεται από τα αποτελέσματά μας. Στην περιοχή όπου εφαρμόζεται η εν τω βάθει διέγερση, 62.5% των ασθενών με καλή κλινική απόκριση εμφάνισαν μία μέγιστη κορύφωση πλάτους της καταγραφής στη β – ζώνη, ενώ 63.2% των ασθενών με κακή κλινική απόκριση δεν εμφάνισαν αντίστοιχη μέγιστη κορύφωση.The present thesis introduces an algorithm that supports the identification of the optimum final point of the stimulation electrode for the Deep Brain Stimulation (DBS) on Parkinson’s patients. A recent study at the Biomedical Simulations and Imaging (BIOSIM) Laboratory of the National Technical University of Athens shows evidence of the existence of correlations between beta band firing of the neurons and the increase of beta band energy in local field potentials acquired during intranuclear microelectrode recordings in the sensorimotor area of the subthalamic nucleus. The present study further expands the correlation study between this increase in beta band energy and the clinical outcome of DBS. The intranuclear microelectrode recordings were acquired by 18 Parkinson’s patients from the Neurosurgery Clinic of Evangelismos Hospital. Our hypothesis that the existence of a maximum beta band amplitude peak in the DBS area is related to an amelioration of clinical motor-related symptoms was supported by our results. More specifically, 62.5% of the patients with a good clinical response had a beta band amplitude peak in the DBS area, whereas 63.2% of the patients with poor clinical response didn’t have a beta band amplitude peak in the DBS area.Πολυτίμη Δ. Φράγγο

Research data supporting: "Microstructural and neurochemical plasticity mechanisms interact to enhance human perceptual decision making"

Data for all main text and supplementary figures for "Microstructural and neurochemical plasticity mechanisms interact to enhance human perceptual decision making.

Cultural differences in visual perceptual learning.

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

Microstructural and neurochemical plasticity mechanisms interact to enhance human perceptual decision-making.

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 work was supported by grants to ZK from the Wellcome Trust [grant number 205067/Z/16/Z, 221633/Z/20/Z], the Biotechnology and Biological Sciences Research Council [grant numbers H012508, BB/P021255/1]

Research data supporting "Functional interactions between sensory and memory networks for adaptive behaviour"

1. Behavioral data: Participants took part in a tilt aftereffect paradigm to test for perceptual adaptation. Participants were tested on two conditions: (a) adaptation: repeated presentation of the same gratings; (b) non-adaptation: presentation of varying gratings. We computed a perceptual adaptation index as the difference between the estimated mean parameter of the fitting functions for the adaptation and non-adaptation conditions. This measure was used for statistical analysis. Further, participants were tested in a visual short-term memory task. Memory score from this task was used for statistical analysis. 2. MRS data: MRS data were collected from two voxels on a 3T scanner: EV and PMN. MRS data were analysed with the LC-Model to quantify metabolite concentrations: GABA+, Glutamate, Glutamine and NAA. GABA+ concentration referenced to NAA was used for statistical analysis. GABA+/water, Glu/NAA and MRS quality indices were used for control analyses. 3. fMRI data: fMRI data were collected during perceptual adaptation (i.e. adaptation and non-adaptation conditions). The main study was conducted at a 3T scanner (2mm isotropic resolution), wheareas the replication study at a 7T scanner (0.8mm isotropic resolution). Data were pre-processed following the Human Connectome Project pipeline for multi-band data: motion correction, EPI-to-EPI coregistration, EPI-to-T1 coregistration, MNI normalisation, spatial smoothing, ICA denoising. fMRI data were then analysed to test for (a) BOLD differences between conditions, and (b) functional connectivity differences between conditions. Lastly, we also tested correlations of the above measures with behaviour and GABA+. 4. Whole brain clusters: The zip folder contains the t-maps of the significant clusters from all analyses tested (see point 3 above). The t-maps are in MNI space, are provided in .nii format (i.e. Nifti) and are named based on the corresponding Supplementary Table (Tables S3-S14). For more information, please see the Karlaftis_CC21_data_description.doc file

Functional Interactions between Sensory and Memory Networks for Adaptive Behavior.

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.EU Seventh Framework Programm

Research data supporting "Neurochemical and functional interactions for improved perceptual decisions through training"

For the main study, participants took part in one behavioural session in the lab and two brain imaging scans (before behavioural training) comprising rs-fMRI and MRS. For the tDCS study, participants took part in one behavioural session with stimulation (Anodal) or without stimulation (Sham) in the lab. Participants in both studies were trained on an orientaion identification task and completed a pre-training test block and five training blocks. 1. Behavioural data: First, we calculated performance accuracy per participant and block. We then fitted the behavioural data per block with a drift diffusion model to compute: (a) drift rate (DR), (b) decision threshold (TH), and (c) non-decision time. For statistical analysis, we computed change as the difference between the pre-training block and the max-training block (i.e. we selected the block with the higher accuracy between the last two training blocks per participant to account for potential fatigue effects towards the end of the training). DR change and TH change were further used for multiple regression analysis. 2. MRS data: MRS data were collected from two voxels on a 3T scanner: an Early Visual (EV) and a dorsolateral pre-frontal cortex (DLPFC) voxel. MRS data were analysed with the LC-Model to quantify metabolite concentrations: GABA+, Glutamate, Glutamine and NAA. GABA+/NAA, Glu/NAA and MRS quality indices (SNR, CRLB, linewidth, tissue composition) were used for control analyses. 3. rs-fMRI data: Resting-state fMRI data were collected on a 3T scanner (2mm isotropic resolution). Data were pre-processed following the Human Connectome Project pipeline for multi-band data: motion correction, EPI-to-EPI coregistration, EPI-to-T1 coregistration, MNI normalisation, spatial smoothing, wavelet despiking, ICA denoising. rs-fMRI data were then used to test for functional connectivity correlations with behaviour and GABA+. 4. Brain maps: The zip folder contains brain masks for: (a) EV voxel (50% overlap across participants), (b) DLPFC voxel (50% overlap across participants), and (c) M1 mask (control region). The masks are in MNI space and are provided in .nii format (i.e. Nifti). For more information, please see the Jia_Frangou_Karlaftis_Ziminski_data_description.doc file

Neurochemical and functional interactions for improved perceptual decisions through training.

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