Neural networks related to pro-saccades and anti-saccades revealed by independent component analysis

The saccadic eye movement system provides an excellent model for investigating basic cognitive processes and flexible control over behaviour. While the mechanism of pro-saccades (PS) is well known, in the case of the anti-saccade task (AS) it is still not clear which brain regions play a role in the inhibition of reflexive saccade to the target, nor what is the exact mechanism of vector inversion (i.e. orienting in the opposite direction). Independent component analysis (ICA) is one of the methods being used to establish temporally coherent brain regions, i.e. neural networks related to the task. In the present study ICA was applied to fMRI data from PS and AS experiments. The study revealed separate networks responsible for saccade generation into the desired direction, the inhibition of automatic responses, as well as vector inversion. The first function is accomplished by the eye fields network. The inhibition of automatic responses is associated with the executive control network. Vector inversion seems to be accomplished by the network comprising a large set of areas, including intraparietal sulcus, precuneus/posterior cingulate cortices, retrosplenial and parahippocampal. Those regions are associated with the parieto-medial temporal pathway, so far linked only to navigation. These results provide a new insight into understanding of the processes of the inhibition and vector inversion

Consciousness and the prefrontal parietal network: insights from attention, working memory, and chunking

Consciousness has of late become a “hot topic” in neuroscience. Empirical work has centered on identifying potential neural correlates of consciousness (NCCs), with a converging view that the prefrontal parietal network (PPN) is closely associated with this process. Theoretical work has primarily sought to explain how informational properties of this cortical network could account for phenomenal properties of consciousness. However, both empirical and theoretical research has given less focus to the psychological features that may account for the NCCs. The PPN has also been heavily linked with cognitive processes, such as attention. We describe how this literature is under-appreciated in consciousness science, in part due to the increasingly entrenched assumption of a strong dissociation between attention and consciousness. We argue instead that there is more common ground between attention and consciousness than is usually emphasized: although objects can under certain circumstances be attended to in the absence of conscious access, attention as a content selection and boosting mechanism is an important and necessary aspect of consciousness. Like attention, working memory and executive control involve the interlinking of multiple mental objects and have also been closely associated with the PPN. We propose that this set of cognitive functions, in concert with attention, make up the core psychological components of consciousness. One related process, chunking, exploits logical or mnemonic redundancies in a dataset so that it can be recoded and a given task optimized. Chunking has been shown to activate PPN particularly robustly, even compared with other cognitively demanding tasks, such as working memory or mental arithmetic. It is therefore possible that chunking, as a tool to detect useful patterns within an integrated set of intensely processed (attended) information, has a central role to play in consciousness. Following on from this, we suggest that a key evolutionary purpose of consciousness may be to provide innovative solutions to complex or novel problems

Oscillatory and Structural Signatures of Language Plasticity in Brain Tumor Patients: a Longitudinal Study

Recent evidence suggests that damage to the language network triggers its functional reorganization. Yet, the spectro-temporal fingerprints of this plastic rearrangement and its relation to anatomical changes is less well understood. Here, we combined magnetoencephalographic recordings with a proxy measure of white matter to investigate oscillatory activity supporting language plasticity and its relation to structural reshaping. First, cortical dynamics were acquired in a group of healthy controls during object and action naming. Results showed segregated beta (13-28 Hz) power decreases in left ventral and dorsal pathways, in a time-window associated to lexico-semantic processing (similar to 250-500 ms). Six patients with left tumors invading either ventral or dorsal regions performed the same naming task before and 3 months after surgery for tumor resection. When longitudinally comparing patients' responses we found beta compensation mimicking the category-based segregation showed by controls, with ventral and dorsal damage leading to selective compensation for object and action naming, respectively. At the structural level, all patients showed preoperative changes in white matter tracts possibly linked to plasticity triggered by tumor growth. Furthermore, in some patients, structural changes were also evident after surgery and showed associations with longitudinal changes in beta power lateralization toward the contralesional hemisphere. Overall, our findings support the existence of anatomo-functional dependencies in language reorganization and highlight the potential role of oscillatory markers in tracking longitudinal plasticity in brain tumor patients. By doing so, they provide valuable information for mapping preoperative and postoperative neural reshaping and plan surgical strategies to preserve language function and patient's quality of lifeBasque Government, Grant/Award Number: BERC 2018-2021; Spanish Ministry of Economy and Competitiveness, Grant/Award Number: RTI2018-096216-A-I00 (MEGLIOMA) and RTI2018-093547-B-I00 (LangConn); Spanish State Research Agency, Grant/Award Number: SEV-2015-0490 and IJCI-2017-3137

Neural Correlates of Memory Decisions Made in the Face of Conflict

We’ve all experienced moments where, for some reason or another, we don’t want to reveal to others what we truly know. The current experiment investigated questions about the behavioral and neural correlates of these types of memory decisions made in the face of a conflicting goal. Participants in this experiment studied several scene-face pairs and were tested with three-face displays preceded by studied scene cues. They were instructed to indicate whether the three-face display contained the matching associate or not. Critically, half of the participants were instructed to simulate feigned memory impairment (i.e. simulators), while the remainder were instructed to perform optimally (i.e. controls). Eye movements and neural activity were recorded throughout this test. Consistent with the instructional manipulation, simulators performed worse than controls with their explicit responding. However, both groups showed comparable early viewing of the associate after the three-face display was presented. Analyses were conducted to identify the memory, attention, and cognitive control processes that contributed to these memory decisions while feigning memory impairment. Hippocampal activity during the scene cue predicted early viewing effects for simulators, even when they made incorrect responses. During the three-face display, hippocampal activity reflected memory accuracy among controls, but the opposite pattern was evident in simulator data. This pattern, with greater activity for incorrect than correct trials, was also seen for simulators in the anterior cingulate cortex, an outcome that likely reflects conflict between memory retrieval and decision making. Finally, group differences in parietal regions likely indicate greater reorienting of attention among simulators than controls. Together, these results suggest the recruitment of memory retrieval, attentional allocation, and cognitive control regions as individuals work to succeed at simulating memory impairment

A Behavioural and Electrophysiological exploration of the Working Memory impairment in Developmental Dyslexia

The current thesis provides a behavioural and electrophysiological exploration of Working Memory (WM) processing in developmental dyslexia. This thesis identifies a debate in the literature regarding the extent to which individuals with dyslexia have a specific phonological WM impairment, or a domain general Central Executive (CE) impairment. Predictions from the latter account suggest that dyslexics should show an impairment in visual, and verbal domains of WM. However, findings in the visual domain have been inconsistent, and research has predominantly focused on children. The experimental work in this thesis examines CE processing in dyslexic adults by assessing the behavioural and ERP responses associated with WM, across 8 experiments. Experiments 1-5 present stimuli in the visual domain, while Experiments 6-8 are conducted in the auditory domain. The results indicate that dyslexics are impaired for verbal information specifically, however subtle RT differences emerge during visual-spatial WM, when participants are required to manipulate information. In order to assess why effects are more robust in the phonological domain, Experiment 8 examines the contribution of auditory perceptual problems and phonological WM processing in dyslexia. The Temporal Sampling Theory of Developmental dyslexia (TSTDD; Goswami, 2011) specifies that dyslexics have a difficulty processing tones with long rise-times. In Experiment 8, dyslexic participants show a WM impairment that is specific to tones with long rise-times. The theoretical implications of these findings are discussed, and a new hypothesis regarding the phonological WM impairment in dyslexia is proposed. The original contribution to knowledge of this thesis are threefold. 1) The ERP responses associated with WM processing in developmental dyslexia are examined across modality, using a range of stimuli. 2) A novel task is used to directly investigate CE processing in dyslexia (Experiment 5). 3) The TSTDD is applied in order to investigate phonological WM in dyslexia

Task-phase fMRI in detection of improvements in working memory post-interventions for carotid stenosis and early Alzheimer’s disease

Working memory allows for coordination of complex goal-driven behavior. Decline of working memory is linked to severe cognitive disabilities and is an important feature of both severe carotid stenosis and Alzheimer\u27s disease. Functional Magnetic Resonance Imaging (fMRI) can help detect functional brain changes for the evaluation of the impact of standard clinical interventions for both diagnoses. This thesis used fMRI, coupled with cognitive tasks to investigate possible working memory improvements post-standard clinical interventions for both conditions. The study observed post-intervention improvements in task-phase fMRI brain activation patterns together with improvements in task performance. Meanwhile, patients demonstrated complex response patterns associated with disease expression and other individual variability, which were considered with results interpretation. This thesis showed that working memory improvements were possible following standard clinical treatments for both conditions. It also supports for tailoring interventions based on patient peculiarities to maximize treatment effectiveness

An Event-Related fMRI Study of Phonological Verbal Working Memory in Schizophrenia

Background: While much is known about the role of prefrontal cortex (PFC) in working memory (WM) deficits of schizophrenia, the nature of the relationship between cognitive components of WM and brain activation patterns remains unclear. We aimed to elucidate the neural correlates of the maintenance component of verbal WM by examining correct and error trials with event-related fMRI. Methodology/Findings: Twelve schizophrenia patients (SZ) and thirteen healthy control participants (CO) performed a phonological delayed-matching-to-sample-task in which a memory set of three nonsense words was presented, followed by a 6-seconds delay after which a probe nonsense word appeared. Participants decided whether the probe matched one of the targets, and rated the confidence of their decision. Blood-oxygen-level-dependent (BOLD) activity during WM maintenance was analyzed in relation to performance (correct/error) and confidence ratings. Frontal and parietal regions exhibited increased activation on correct trials for both groups. Correct and error trials were further segregated into true memory, false memory, guess, and true error trials. True memory trials were associated with increased bilateral activation of frontal and parietal regions in both groups but only CO showed deactivation in PFC. There was very little maintenancerelated cortical activity during guess trials. False memory was associated with increased left frontal and parietal activation in both groups. Conclusion: These findings suggest that a wider network of frontal and parietal regions support WM maintenance in correct trials compared with error trials in both groups. Furthermore, a more extensive and dynamic pattern of recruitment of the frontal and parietal networks for true memory was observed in healthy controls compared with schizophrenia patients. These results underscore the value of parsing the sources of memory errors in fMRI studies because of the non-linear nature of the brain-behavior relationship, and suggest that group comparisons need to be interpreted in more specific behavioral contexts

Parallelizing support vector machines for scalable image annotation

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.Machine learning techniques have facilitated image retrieval by automatically classifying and annotating images with keywords. Among them Support Vector Machines (SVMs) are used extensively due to their generalization properties. However, SVM training is notably a computationally intensive process especially when the training dataset is large. In this thesis distributed computing paradigms have been investigated to speed up SVM training, by partitioning a large training dataset into small data chunks and process each chunk in parallel utilizing the resources of a cluster of computers. A resource aware parallel SVM algorithm is introduced for large scale image annotation in parallel using a cluster of computers. A genetic algorithm based load balancing scheme is designed to optimize the performance of the algorithm in heterogeneous computing environments. SVM was initially designed for binary classifications. However, most classification problems arising in domains such as image annotation usually involve more than two classes. A resource aware parallel multiclass SVM algorithm for large scale image annotation in parallel using a cluster of computers is introduced. The combination of classifiers leads to substantial reduction of classification error in a wide range of applications. Among them SVM ensembles with bagging is shown to outperform a single SVM in terms of classification accuracy. However, SVM ensembles training are notably a computationally intensive process especially when the number replicated samples based on bootstrapping is large. A distributed SVM ensemble algorithm for image annotation is introduced which re-samples the training data based on bootstrapping and training SVM on each sample in parallel using a cluster of computers. The above algorithms are evaluated in both experimental and simulation environments showing that the distributed SVM algorithm, distributed multiclass SVM algorithm, and distributed SVM ensemble algorithm, reduces the training time significantly while maintaining a high level of accuracy in classifications

Functional imaging in neuroenhancement

Increasingly demanding tasks, competition for competence and time pressure have lead to attempts of neuroenhancement (NE) among students and employees. NE is designed to increase cognitive abilities by modulating brain processes through the use of pharmaceutics. Substances such as methylphenidate (i.e. Ritalin®), modafinil (i.e. Vigil®) and caffeine are common candidates for enhancing cognitive abilities such as executive functions, inhibition control and memory (Wood et al., 2014). Until today, there has not been a study investigating memory enhancement in functional magnetic resonance imaging (fMRI). Using fMRI, 48 healthy participants were tested for drug effects in a single-dose, double-blind and randomized study using a declarative memory task. During memory recall, methylphenidate dependent deactivations were found in the fronto-parietal and temporal regions whereas no BOLD alterations were seen during encoding. On the behavioral level, methylphenidate enhanced subject’s judgement confidence and performance during late recall. During encoding, caffeine led to deactivations in the precentral gyrus whereas modafinil did not show any BOLD signal alterations at all. To get an overview over the existing neuroimaging literature, all published studies on the effects of the aforementioned drug agents were reviewed in addition. In line with this study, previous publications emphasized that methylphenidate seems to alter task relevant brain areas. Our main finding of task-related deactivations may point to the reduction of task-functioning distractions. Thereby, we conclude a drug-dependent increase of efficiency in data processing.Zunehmende Arbeitsbelastung, erhöhter Zeitdruck und größere Verantwortung haben dazu geführt, dass für Studenten und Arbeitnehmer das Phänomen Neuroenhancement (NE) eine zunehmende Relevanz erlangt hat. Darunter wird die Steigerung der kognitiven Leistung durch pharmazeutischen Eingriff auf zentralnervöse Prozesse verstanden. Substanzen wie z.B. Methylphenidat (Ritalin®), Modafinil (Vigil®) und Koffein gelten als aussichtsreiche Kandidaten zur Leistungssteigerung, die möglicherweise Einfluss auf kognitive Prozesse, wie z.B. Exekutive Funktionen, Inhibitionskontrolle und Gedächtnis ausüben können (Wood et al., 2014). Keine bisher publizierte Studie hat den Fokus auf neuronale Korrelate der deklarativen Gedächtnissteigerung gelegt. Aus dem Grund sind zusätzlich alle bisher veröffentlichten bildgebenden Studien zu Methylphenidat, Modafinil und Koffein zu einer strukturierten Übersicht zusammengefasst worden. Mittels funktionaler Magnetresonanztomographie (fMRT) wurden 48 gesunde Probanden, doppelt verblindet und randomisiert auf Steigerung der deklarativen Gedächtnisleistung getestet. Obwohl die Wirksamkeit der drei Substanzen ausführlich für klinische Patientenpopulationen untersucht wurde, gibt es kaum Wissen über die möglichen behavioralen und neuronalen Auswirkungen auf gesunde, erwachsene Menschen. Entgegen der Erwartung, dass die getesteten Substanzen klassische Gedächtnis assoziierte Regionen aktivieren, wurden unterschiedliche substanzspezifische Effekte gefunden. Wahrend des Abrufs von Gedächtnisinhalten deaktivierte Methylphenidat fronto-parietale und temporale Regionen. Dagegen führte die Applikation von Koffein zu einer verringerten BOLD Antwort im Gyrus Präcentralis während der Lernphase. Modafinil führte zu keiner Veränderung im Vergleich zu Placebo. Auf Verhaltensebene förderte Methylphenidat den späten Abruf von Gedächtnisinhalten, wohingegen die beiden anderen Substanzen keine Effekte hinsichtlich der Lernleistung vorwiesen. Vor dem Hintergrund bisheriger bildgebender Studien zeigt die vorliegende Arbeit, dass Neuroenhancement neben der Aktivierung leistungsrelevanter Gehirnregionen auch durch Reduzierung von störenden Einwirkungen funktionieren kann und damit womöglich die Effektivität der Informationsverarbeitung erhöht

Efficient Methods for the Design and Training of Neural Networks

The field of artificial intelligence has seen significant advancements with the development of neural networks, which have numerous applications in computer vision, natural language processing, and speech processing. Despite these advancements, designing and training these networks still pose numerous challenges. This thesis aims to address two critical aspects of neural network development, design and training, within the context of computer vision tasks. The thesis focuses on three main challenges in the development of neural networks. The first challenge is finding an efficient way to perform architecture search in an extremely large or even unlimited search space. To address this challenge, the thesis proposes a Neural Search-space Evolution (NSE) scheme that enables efficient and effective architecture search in large-scale search spaces. The second challenge is to improve the efficiency of self-supervised learning for model pretraining. To address this challenge, the thesis proposes a combinatorial patches approach that significantly improves the efficiency of self-supervised learning. The third challenge is to develop an efficient and versatile multitask model that can leverage the benefits of large-scale multitask training. To address this challenge, the thesis proposes a Unified model for Human-Centric Perceptions (UniHCP) as a simple and scalable solution for a human-centric perception system that unifies multiple human-centric tasks into a neat, efficient, and scalable model. The results of this thesis demonstrate the effectiveness of the proposed methods in improving the practicality and performance of neural network design and training. The NSE scheme, combinatorial patches approach, and UniHCP have been tested on a broad range of datasets, tasks, and settings, yielding impressive results. These findings affirm the efficacy of the proposed methods in enhancing the efficiency of the design and training process of neural networks