Categorical evidence, confidence and urgency during the integration of multi-feature information

Includes bibliographical references.2015 Summer.The present experiment utilized a temporally-extended categorization task to investigate the neural substrates underlying our ability to integrate information over time and across multiple stimulus features. Importantly, the design allowed differentiation of three important decision functions: 1) categorical evidence, 2) decisional confidence (the choice-independent probability that a decision will lead to a desirable state), and 3) urgency (a hypothetical signal representing a growing pressure to produce a behavioral response within each trial). In conjunction with model-based fMRI, the temporal evolution of these variables were tracked as participants deliberated about impending choices. The approach allowed investigation of the independent effects of urgency across the brain, and also the investigation of how urgency might modulate representations of categorical evidence and confidence. Representations associated with prediction errors during feedback were also investigated. Many cortical and striatal somatomotor regions tracked the dynamical evolution of categorical evidence, while many regions of the dorsal and ventral attention networks (Corbetta and Shulman, 2002) tracked decisional confidence and uncertainty. Urgency influenced activity in regions known to be associated with flexible control of the speed-accuracy trade-off (particularly the pre- SMA and striatum), and additionally modulated representations of categorical evidence and confidence. The results, therefore, link the urgency signal to two hypothetical mechanisms underling flexible control of decision thresholding (Bogacz et al., 2010): gain modulation of the striatal thresholding circuitry, and gain modulation of the integrated categorical evidence

Theories of anterior cingulate cortex function : opportunity cost

The target article highlights the role of the anterior cingulate cortex (ACC) in conflict monitoring, but ACC function may be better understood in terms of the hierarchical organization of behavior. This proposal suggests that the ACC selects extended goal-directed actions according to their learned costs and benefits and executes those behaviors subject to depleting resources

Investigating the Cognitive and Neural Mechanisms underlying Multisensory Perceptual Decision-Making in Humans

On a frequent day-to-day basis, we encounter situations that require the formation of decisions based on ambiguous and often incomplete sensory information. Perceptual decision-making defines the process by which sensory information is consolidated and accumulated towards one of multiple possible choice alternatives, which inform our behavioural responses. Perceptual decision-making can be understood both theoretically and neurologically as a process of stochastic sensory evidence accumulation towards some choice threshold. Once this threshold is exceeded, a response is facilitated, informing the overt actions undertaken. Prevalent progress has been made towards understanding the cognitive and neural mechanisms underlying perceptual decision-making. Analyses of Reaction Time (RTs; typically constrained to milliseconds) and choice accuracy; reflecting decision-making behaviour, can be coupled with neuroimaging methodologies; notably electroencephalography (EEG) and functional Magnetic Resonance Imaging (fMRI), to identify spatiotemporal components representative of the neural signatures corresponding to such accumulation-to-bound decision formation on a single-trial basis. Taken together, these provide us with an experimental framework conceptualising the key computations underlying perceptual decision-making. Despite this, relatively little remains known about the enhancements or alternations to the process of perceptual decision-making from the integration of information across multiple sensory modalities. Consolidating the available sensory evidence requires processing information presented in more than one sensory modality, often near-simultaneously, to exploit the salient percepts for what we term as multisensory (perceptual) decision-making. Specifically, multisensory integration must be considered within the perceptual decision-making framework in order to understand how information becomes stochastically accumulated to inform overt sensory-motor choice behaviours. Recently, substantial progress in research has been made through the application of behaviourally-informed, and/or neurally-informed, modelling approaches to benefit our understanding of multisensory decision-making. In particular, these approaches fit a number of model parameters to behavioural and/or neuroimaging datasets, in order to (a) dissect the constituent internal cognitive and neural processes underlying perceptual decision-making with both multisensory and unisensory information, and (b) mechanistically infer how multisensory enhancements arise from the integration of information across multiple sensory modalities to benefit perceptual decision formation. Despite this, the spatiotemporal locus of the neural and cognitive underpinnings of enhancements from multisensory integration remains subject to debate. In particular, our understanding of which brain regions are predictive of such enhancements, where they arise, and how they influence decision-making behaviours requires further exploration. The current thesis outlines empirical findings from three studies aimed at providing a more complete characterisation of multisensory perceptual decision-making, utilising EEG and accumulation-to-bound modelling methodologies to incorporate both behaviourally-informed and neurally-informed modelling approaches, investigating where, when, and how perceptual improvements arise during multisensory perceptual decision-making. Pointedly, these modelling approaches sought to probe the exerted modulatory influences of three factors: unisensory formulated cross-modal associations (Chapter 2), natural ageing (Chapter 3), and perceptual learning (Chapter 4), on the integral cognitive and neural mechanisms underlying observable benefits towards multisensory decision formation. Chapter 2 outlines secondary analyses, utilising a neurally-informed modelling approach, characterising the spatiotemporal dynamics of neural activity underlying auditory pitch-visual size cross-modal associations. In particular, how unisensory auditory pitch-driven associations benefit perceptual decision formation was functionally probed. EEG measurements were recorded from participants during performance of an Implicit Association Test (IAT), a two-alternative forced-choice (2AFC) paradigm which presents one unisensory stimulus feature per trial for participants to categorise, but manipulates the stimulus feature-response key mappings of auditory pitch-visual size cross-modal associations from unisensory stimuli alone, thus overcoming the issue of mixed selectivity in recorded neural activity prevalent in previous cross-modal associative research, which near-simultaneously presented multisensory stimuli. Categorisations were faster (i.e., lower RTs) when stimulus feature-response key mappings were associatively congruent, compared to associatively incongruent, between the two associative counterparts, thus demonstrating a behavioural benefit to perceptual decision formation. Multivariate Linear Discriminant Analysis (LDA) was used to characterise the spatiotemporal dynamics of EEG activity underpinning IAT performance, in which two EEG components were identified that discriminated neural activity underlying the benefits of associative congruency of stimulus feature-response key mappings. Application of a neurally-informed Hierarchical Drift Diffusion Model (HDDM) demonstrated early sensory processing benefits, with increases in the duration of non-decisional processes with incongruent stimulus feature-response key mappings, and late post-sensory alterations to decision dynamics, with congruent stimulus feature-response key mappings decreasing the quantity of evidence required to facilitate a decision. Hence, we found that the trial-by-trial variability in perceptual decision formation from unisensory facilitated cross-modal associations could be predicted by neural activity within our neurally-informed modelling approach. Next, Chapter 3 outlines cognitive research investigating age-related impacts on the behavioural indices of multisensory perceptual decision-making (i.e., RTs and choice accuracy). Natural ageing has been demonstrated to diversely affect multisensory perceptual decision-making dynamics. However, the constituent cognitive processes affected remain unclear. Specifically, a mechanistic insight reconciling why older adults may exhibit preserved multisensory integrative benefits, yet display generalised perceptual deficits, relative to younger adults, remains inconclusive. To address this limitation, 212 participants performed an online variant of a well-established audiovisual object categorisation paradigm, whereby age-related differences in RTs and choice accuracy (binary responses) between audiovisual (AV), visual (V), and auditory (A) trial types could be assessed between Younger Adults (YAs; Mean ± Standard Deviation = 27.95 ± 5.82 years) and Older Adults (OAs; Mean ± Standard Deviation = 60.96 ± 10.35 years). Hierarchical Drift Diffusion Modelling (HDDM) was fitted to participants’ RTs and binary responses in order to probe age-related impacts on the latent underlying processes of multisensory decision formation. Behavioural results found that whereas OAs were typically slower (i.e., ↑ RTs) and less accurate (i.e., ↓ choice accuracy), relative to YAs across all sensory trial types, they exhibited greater differences in RTs between AV and V trials (i.e., ↑ AV-V RT difference), with no significant effects of choice accuracy, implicating preserved benefits of multisensory integration towards perceptual decision formation. HDDM demonstrated parsimonious fittings for characterising these behavioural discrepancies between YAs and OAs. Notably we found slower rates of sensory evidence accumulation (i.e., ↓ drift rates) for OAs across all sensory trial types, coupled with (1) higher rates of sensory evidence accumulation (i.e., ↑ drift rates) for OAs between AV versus V trial types irrespective of stimulus difficulty, coupled with (2) increased response caution (i.e., ↑ decision boundaries) between AV versus V trial types, and (3) decreased non-decisional processing duration (i.e., ↓ non-decision times) between AV versus V trial types for stimuli of increased difficulty respectively. Our findings suggest that older adults trade-off multisensory decision-making speed for accuracy to preserve enhancements towards perceptual decision formation relative to younger adults. Hence, they display an increased reliance on integrating multimodal information; through the principle of inverse effectiveness, as a compensatory mechanism for a generalised cognitive slowing when processing unisensory information. Overall, our findings demonstrate how computational modelling can reconcile contrasting hypotheses of age-related changes in processes underlying multisensory perceptual decision-making behaviour. Finally, Chapter 4 outlines research probing the exerted influence of perceptual learning on multisensory perceptual decision-making. Views of unisensory perceptual learning imply that improvements in perceptual sensitivity may be due to enhancements in early sensory representations and/or modulations to post-sensory decision dynamics. We sought to assess whether these views could account for improvements in perceptual sensitivity for multisensory stimuli, or even exacerbations of multisensory enhancements towards decision formation, by consolidating the spatiotemporal locus of where and when in the brain they may be observed. We recorded EEG activity from participants who completed the same audiovisual object categorisation paradigm (as outlined in Chapter 3), over three consecutive days. We used single-trial multivariate LDA to characterise the spatiotemporal trajectory of the decision dynamics underlying any observed multisensory benefits both (a) within and (b) between visual, auditory, and audiovisual trial types. While found significant decreases were found in RTs and increases in choice accuracy over testing days, we did not find any significant effects of perceptual learning on multisensory nor unisensory perceptual decision formation. Similarly, EEG analysis did not find any neural components indicative of early or late modulatory effects from perceptual learning in brain activity, which we attribute to (1) a long duration of stimulus presentations (300ms), and (2) a lack of sufficient statistical power for our LDA classifier to discriminate face-versus-car trial types. We end this chapter with considerations for discerning multisensory benefits towards perceptual decision formation, and recommendations for altering our experimental design to observe the effects of perceptual learning as a decision neuromodulator. These findings contribute to literature justifying the increasing relevance of utilising behaviourally-informed and/or neurally-informed modelling approaches for investigating multisensory perceptual decision-making. In particular, a discussion of the underlying cognitive and/or neural mechanisms that can be attributed to the benefits of multisensory integration towards perceptual decision formation, as well as the modulatory impact of the decision modulators in question, can contribute to a theoretical reconciliation that multisensory integrative benefits are not ubiquitous to specific spatiotemporal neural dynamics nor cognitive processes

Dynamic speech networks in the brain: Dual contribution of incrementality and constraints in access to semantics

This thesis explores the spatiotemporal network dynamics underlying natural speech comprehension, as measured by electro-magnetoencephalography (E/MEG). I focus on the transient effects of incrementality and constraints in speech on access to lexical semantics. Through three E/MEG experiments I address two core issues in systems neuroscience of language: 1) What are the network dynamics underpinning cognitive computations that take place when we map sounds to rich semantic representations? 2) How do the prior semantic and syntactic contextual constraints facilitate this mapping? Experiment 1 investigated the cognitive processes and relevant networks that come online prior to a word’s recognition point (e.g. “f” for butterfly) as we access meaning through speech in isolation. The results revealed that 300 ms before the word is recognised, the speech incrementally activated matching phonological and semantic representations resulting in transient competition. This competition recruited LIFG, and modality specific regions (LSMG, LSTG for the phonological; LAG and MTG for the semantic domain). Immediately after the word’s recognition point the semantic representation of the target concept was boosted, and rapidly accessed recruiting bilateral MTG and AG. Experiment 2 explored the cortical networks underpinning contextual semantic processing in speech. Participant listened to two-word spoken phrases where the semantic constraint provided by the modifier was manipulated. To separate out cognitive networks that are modulated by semantic constraint from task positive networks I performed a temporal independent component analysis. Among 14 networks extracted, only the activity of bilateral AG was modulated by semantic constraint between -400 to -300 ms before the noun’s recognition point. Experiment 3 addressed the influence of sentential syntactic constraint on anticipation and activation of upcoming syntactic frames in speech. Participants listened to sentences with local syntactic ambiguities. The analysis of the connectivity dynamics in the left frontotemporal syntax network showed that the processing of sentences that contained the less anticipated syntactic structure showed early increased feedforward information flow in 0-100 ms, followed by increased recurrent connectivity between LIFG and LpMTG from the 200-500 ms from the verb onset. Altogether the three experiments reveal novel insights into transient cognitive networks recruited incrementally over time both in the absence of and with context, as the speech unfolds, and how the activation of these networks are modulated by contextual syntactic and semantic constraints. Further I provide neural evidence that contextual constraints serve to facilitate speech comprehension, and how the speech networks recover from failed anticipations.Istanbul Chamber of Commerce Turkish Petrol Foundatio

Conduct Problems, Callous-Unemotional Traits and Emotion Processing: Adversity and Diversity, a Functional Neuroimaging Study

This thesis focused on youths who present with conduct problems (CP), callous-unemotional traits and functional neuroimaging. PART 1: A narrative review of current neuroimaging literature regarding youths with CP. Firstly, this review outlined general CP related considerations regarding neuroimaging literature and common CP risk factors before summarising structural neuroimaging literature. Functional neuroimaging research was then summarised using neurocognitive domains of functioning: acute threat response, social cognition, cognitive control and reinforcement learning. Findings were discussed with reference to how risk factors and neurocognitive functioning interact to produce behavioural syndromes associated with CP. Future CP related neuroimaging research should focus on domains of functioning and the influence of risk factors on heterogeneity. PART 2: A functional MRI study that used facial expressions (angry/sad/happy) to investigate neural differences in emotion processing amongst boys with CP split between high and low callous-unemotional (CU) traits, compared to matched controls. Findings highlighted perturbations in limbic, frontal, temporal and medial regions for both high and low CU trait boys compared to controls. CP boys demonstrated specific atypical activation in the amygdala, insula and prefrontal cortex when processing negative facial expressions and were associated with more severe pathological parenting practices than controls. Potential explanations and clinical implications were explored. PART 3: A critical appraisal of my learning regarding neuroimaging and youths with CP including my perspective from clinical practice. This appraisal focused on the theoretical, diagnostic, research, clinical and narrative implications of transitioning understandings of neural function from a behavioural, damaged and functionally specialised paradigm toward a dimensional, adapted and interrelated paradigm

Cerebellar contribution to Cognitive Impairment in early stages of Relapsing-Remitting Multiple Sclerosis: a conventional and rs-fMRI study

Background. The cerebellum is a primary site of Multiple Sclerosis (MS) pathology. Structural and functional MRI studies have demonstrated the role of the posterior cerebellum in cognitive functions. To date, the “Cerebellar Cognitive Affective Syndrome” (CCAS) scale has never been used to test MS-related Cognitive Impairment (CI) and its association with cerebellar involvement. Objectives. We investigated the association of MRI structural and functional abnormalities of the cognitive cerebellum with CI and tested the role of the CCAS scale in detecting CI in a cohort of very early RRMS patients. Methods. 37 patients with early RRMS and 4 age- and sex-matched healthy controls (HC) were enrolled in this cross-sectional, exploratory study. Cognitive performances were assessed through BICAMS, D-KEFS ST, and CCAS scale. Using a CCAS scale score cut-off (based on a 50 HC sample), 26/37 (70%) patients were classified as “Normal-CCAS” and 11/37 (30%) as “Impaired-CCAS”. All subjects underwent a conventional and resting-state functional MRI (rs-fMRI) protocol. Comparisons between groups were assessed for structural and functional MRI parameters. Moreover, correlations between cognitive test scores and structural-functional MRI parameters were evaluated. Results. Patients with pathological score on CCAS also showed CVLT-II and D-KEFS ST low scores. A significant reduction in cerebellar volumetric parameters was found in the CCAS-impaired MS group compared to the normal one, albeit whole brain WM and thalamic volumes were also significantly reduced. The rs-fMRI analysis revealed higher functional connectivity (FC) between the cognitive cerebellum and most of the functional brain cortical networks in the CCAS-impaired group compared to the normal one. Conclusions. Our findings suggest that CI in early RRMS is associated with pathological alterations in both structural and functional MRI parameters. Higher FC between cerebellar-brain networks in CCAS-impaired patients might be the expression of a compensatory hyperactivation of altered cognitive cerebellar connections. Finally, although the CCAS scale has proven able to detect CI in MS patients, its specificity for cerebellar pathology needs to be further investigated

A computational approach to motivated behaviour and apathy

The loss of motivation and goal-directed behaviour is characteristic of apathy. Across a wide range of neuropsychiatric disorders, including Huntington’s disease (HD), apathy is poorly understood, associated with significant morbidity, and is hard to treat. One of the challenges in understanding the neural basis of apathy is moving from phenomenology and behavioural dysfunction to neural circuits in a principled manner. The computational framework offers one such approach. I adopt this framework to better understand motivated behaviour and apathy in four complementary projects. At the heart of many apathy formulations is impaired self-initiation of goal-directed behaviour. An influential computational theory proposes that “opportunity cost”, the amount of reward we stand to lose by not taking actions per unit time, is a key variable in governing the timing of self-initiated behaviour. Using a novel task, I found that free-operant behaviour in healthy participants both in laboratory conditions and in online testing, conforms to predictions of this computational model. Furthermore, in both studies I found that in younger adults sensitivity to opportunity cost predicted behavioural apathy scores. Similar pilot results were found in a cohort of patients with HD. These data suggest that opportunity cost may be an important computational variable relevant for understanding a core feature of apathy – the timing of self-initiated behaviour. In my second project, I used a reinforcement learning paradigm to probe for early dysfunction in a cohort of HD gene carriers approximately 25 years from clinical onset. Based on empirical data and computational models of basal ganglia function I predicted that asymmetry in learning from gains and losses may be an early feature of carrying the HD gene. As predicted, in this task fMRI study, HD gene carriers demonstrated an exaggerated neural response to gains as compared to losses. Gene carriers also differed in the neural response to expected value suggesting that carrying the HD gene is associated with altered processing of valence and value decades from onset. Finally, based on neurocomputational models of basal ganglia pathway function, I tested the hypothesis that apathy in HD would be associated with the involvement of the direct pathway. Support for this hypothesis was found in two related projects. Firstly, using data from a large international HD cohort study, I found that apathy was associated with motor features of the disease thought to represent direct pathway involvement. Secondly, I tested this hypothesis in vivo using resting state fMRI data and a model of basal ganglia connectivity in a large peri-manifest HD cohort. In keeping with my predictions, whilst emerging motor signs were associated with changes in the indirect pathway, apathy scores were associated with connectivity changes in the direct pathway connectivity within my model. For patients with apathy across neuropsychiatry there is an urgent need to understand the neural basis of motivated behaviour in order to develop novel therapies. In this thesis, I have used a computational framework to develop and test a range of hypotheses to advance this understanding. In particular, I have focussed on the computational factors which drive us to self-initiate, their potential neural underpinnings and the relevance of these models for apathy in patients with HD. The data I present supports the hypothesis that opportunity cost and basal ganglia pathway connectivity may be two important components necessary to generate motivated behaviour and contribute to the development of apathy in HD

Functional neuroanatomy of action selection in schizophrenia

Schizophrenia remains an enigmatic disorder with unclear neuropathology. Recent advances in neuroimaging and genetic research suggest alterations in glutamate-dopamine interactions adversely affecting synaptic plasticity both intracortically and subcortically. Relating these changes to the manifestation of symptoms presents a great challenge, requiring a constrained framework to capture the most salient elements. Here, a biologically-grounded computational model of basal ganglia-mediated action selection was used to explore two pathological processes that hypothetically underpin schizophrenia. These were a drop in the efficiency of cortical transmission, reducing both the signal-to-noise ratio (SNR) and overall activity levels; and an excessive compensatory upregulation of subcortical dopamine release. It was proposed that reduced cortical efficiency was the primary process, which led to a secondary disinhibition of subcortical dopamine release within the striatum. This compensation was believed to partly recover lost function, but could then induce disorganised-type symptoms - summarised as selection ”Instability” - if it became too pronounced. This overcompensation was argued to be countered by antipsychotic medication. The model’s validity was tested during an fMRI (functional magnetic resonance imaging) study of 16 healthy volunteers, using a novel perceptual decision-making task, and was found to provide a good account for pallidal activation. Its account for striatum was developed and improved with a small number of principled model modifications: the inclusion of fast spiking interneurons within striatum, and their inhibition by the basal ganglia’s key regulatory nucleus, external globus pallidus. A key final addition was the explicit modelling of dopaminergic midbrain, which is dynamically regulated by both cortex and the basal ganglia. This enabled hypotheses concerning the effects of cortical inefficiency, compensatory dopamine release and medication to be directly tested. The new model was verified with a second set of 12 healthy controls. Its pathological predictions were compared to data from 12 patients with schizophrenia. Model simulations suggested that Instability went hand-in-hand with cortical inefficiency and secondary dopamine upregulation. Patients with high Instability scores showed a loss of SNR within decision-related cortex (consistent with cortical inefficiency); an exaggerated response to task demands within substantia nigra (consistent with dopaminergic upregulation); and had an improved fit to simulated data derived from increasingly cortically-inefficient models. Simulations representing the healthy state provided a good account for patients’ motor putamen, but only cortically-inefficient simulations representing the ill state provided a fit for ventral-anterior striatum. This fit improved as the simulated model became more medicated (increased D2 receptor blockade). The relative improvement of this account correlated with patients’ medication dosage. In summary, by distilling the hypothetical neuropathology of schizophrenia into two simplified umbrella processes, and using a computational model to consider their effects within action selection, this work has successfully related patients’ fMRI activation to particular symptomatology and antipsychotic medication. This approach has the potential to improve patient care by enabling a neurobiological appreciation of their current illness state, and tailoring their medication level appropriately