Authors:

Robotham, R.J., RICE, G.E., Leff, A.P., LAMBON RALPH, M.A., Starrfelt, R.

Reference:

Year of publication:

2023

CBU number:

8892

Abstract:

Knowledge about the consequences of stroke on high level vision comes primarily from single case studies of patients selected based on their behavioural profiles, typically patients with specific stroke syndromes like pure alexia or prosopagnosia. There are, however, no systematic, detailed, large-scale evaluations of the more typical clinical behavioural and lesion profiles of impairments in high-level vision after posterior cerebral artery (PCA) stroke. We present behavioural and lesion data from the Back of the Brain (BoB) project, to date the largest (N=64) and most detailed examination of patients with cortical PCA strokes selected based on lesion location. The aim of the current study was to relate behavioural performance with faces, objects and written words to lesion data through two complementary analyses: (1) a multivariate multiple regression analysis to establish the relationships between lesion volume, lesion laterality, and the presence of a bilateral lesion with performance; and, (2) a voxel-based correlational method (VBCM) analysis to establish whether there are distinct or separate regions within the PCA territory that underpin the visual processing of words, faces, and objects. Behaviourally, most patients showed more general deficits in high level vision (n=22) or no deficits at all (n=21). Category-selective deficits were rare (n=6), and were only found for words. Overall, total lesion volume was most strongly related to performance across all three domains. While behavioural impairment in all domains was observed following unilateral left and right as well as bilateral lesions, the regions most strongly related to performance mainly confirmed the pattern reported in more selective cases. For words, these included a left hemisphere cluster extending from the occipital pole along the fusiform and lingual gyri; for objects bilateral clusters which overlapped with the word cluster in the left occipital lobe. Face performance mainly correlated with a right hemisphere cluster within the white matter, partly overlapping with the object cluster. While the findings provide partial support for the relative laterality of posterior brain regions supporting reading and face processing, the results suggest that both hemispheres are involved in the visual processing of faces, words and objects.

URL:

Data available, click to request

Authors:

Su, Y., MACGREGOR, L., Olasagasti, Giraud, A-L

Reference:

Year of publication:

In Press

CBU number:

8891

Abstract:

Understanding speech requires mapping fleeting and often ambiguous soundwaves to meaning. While humans are known to exploit their capacity to contextualize to facilitate this process, how internal knowledge is deployed on-line remains an open question. Here, we present a model that extracts multiple levels of information from continuous speech online. The model applies linguistic and nonlinguistic knowledge to speech processing, by periodically generating top-down predictions and incorporating bottom-up incoming evidence in a nested temporal hierarchy. We show that a nonlinguistic context level provides semantic predictions informed by sensory inputs, which are crucial for disambiguating among multiple meanings of the same word. The explicit knowledge hierarchy of the model enables a more holistic account of the neurophysiological responses to speech compared to using lexical predictions generated by a neural-network language model (GPT-2). We also show that hierarchical predictions reduce peripheral processing via minimizing uncertainty and prediction error. With this proof-of-concept model we demonstrate that the deployment of hierarchical predictions is a possible strategy for the brain to dynamically utilize structured knowledge and make sense of the speech input. Summary data used in figures are available at https://osf.io/qvghf/ . MATLAB code for the model can be found at https://github.com/suyaqing/hierarchical-speech

Data available, click to request

Authors:

Lugtmeijerm, S., Geerligs, L., Tsvetanov, K.A., MITCHELL, D.J., Cam-Can, Campbell, K.L.

Reference:

Year of publication:

2023

CBU number:

8890

Abstract:

Working memory is critical to higher-order executive processes and declines throughout the adult lifespan. However, our understanding of the neural mechanisms underlying this decline is limited. Recent work suggests that functional connectivity between frontal control and posterior visual regions may be critical, but examinations of age differences therein have been limited to a small set of brain regions and extreme group designs (i.e., comparing young and older adults). In this study, we build on previous research by using a lifespan cohort and a whole-brain approach to investigate working memory load-modulated functional connectivity in relation to age and performance. The article reports on analysis of the Cambridge center for Ageing and Neuroscience (Cam-CAN) data. Participants from a population-based lifespan cohort (N = 101, age 23–86) performed a visual short-term memory task during functional magnetic resonance imaging. Visual short-term memory was measured with a delayed recall task for visual motion with three different loads. Whole-brain load-modulated functional connectivity was estimated using psychophysiological interactions in a hundred regions of interest, sorted into seven networks (Schaefer et al., 2018, Yeo et al., 2011). Results showed that load-modulated functional connectivity was strongest within the dorsal attention and visual networks during encoding and maintenance. With increasing age, load-modulated functional connectivity strength decreased throughout the cortex. Whole-brain analyses for the relation between connectivity and behavior were non-significant. Our results give additional support to the sensory recruitment model of working memory. We also demonstrate the widespread negative impact of age on the modulation of functional connectivity by working memory load. Older adults might already be close to ceiling in terms of their neural resources at the lowest load and therefore less able to further increase connectivity with increasing task demands.

Data for this project is available at: https://osf.io/w3s74/

Authors:

King, D.L., HENSON, R.N., KIEVIT, R., Wolpe, N., Brayne, C., Tyler, L., ROWE, J.B., Cam-CAN, Tsvetanov, K.A.

Reference:

Year of publication:

2023

CBU number:

8887

Abstract:

Cardiovascular ageing contributes to cognitive impairment. However, the unique and synergistic contributions of multiple cardiovascular factors to cognitive function remain unclear because they are often condensed into a single composite score or examined in isolation. We hypothesized that vascular risk factors, electrocardiographic features and blood pressure indices reveal multiple latent vascular factors, with independent contributions to cognition. In a population-based deep-phenotyping study (n = 708, age 18–88), path analysis revealed three latent vascular factors dissociating the autonomic nervous system response from two components of blood pressure. These three factors made unique and additive contributions to the variability in crystallized and fluid intelligence. The discrepancy in fluid relative to crystallized intelligence, indicative of cognitive decline, was associated with a latent vascular factor predominantly expressing pulse pressure. This suggests that higher pulse pressure is associated with cognitive decline from expected performance. The effect was stronger in older adults. Controlling pulse pressure may help to preserve cognition, particularly in older adults. Our findings highlight the need to better understand the multifactorial nature of vascular aging. Scripts available here: https://github.com/DebsKing/Distinct_Vascular_Components_relate_To_Cognition Data available here: https://camcan-archive.mrc-cbu.cam.ac.uk/dataaccess

URL:

Data available, click to request

Authors:

MICHAEL, E., Covarrubias, L.S., Leong, V., Kourtzi, Z.

Reference:

Year of publication:

2022

CBU number:

8886

Abstract:

Training is known to improve our ability to make decisions when interacting in complex environments. However, individuals vary in their ability to learn new tasks and acquire new skills in different settings. Here, we test whether this variability in learning ability relates to individual brain oscillatory states. We use a visual flicker paradigm to entrain individuals at their own brain rhythm (i.e. peak alpha frequency) as measured by resting-state electroencephalography (EEG). We demonstrate that this individual frequency-matched brain entrainment results in faster learning in a visual identification task (i.e. detecting targets embedded in background clutter) compared to entrainment that does not match an individual’s alpha frequency. Further, we show that learning is specific to the phase relationship between the entraining flicker and the visual target stimulus. EEG during entrainment showed that individualized alpha entrainment boosts alpha power, induces phase alignment in the pre-stimulus period, and results in shorter latency of early visual evoked potentials, suggesting that brain entrainment facilitates early visual processing to support improved perceptual decisions. These findings suggest that individualized brain entrainment may boost perceptual learning by altering gain control mechanisms in the visual cortex, indicating a key role for individual neural oscillatory states in learning and brain plasticity.

URL:

Data available, click to request

Authors:

PETIT, S., WOOLGAR, A., BROWN, A., Jessen, E.T.,

Reference:

Year of publication:

In Press

CBU number:

8885

Abstract:

Electroencephalography may be a valuable tool for assessing lexico-semantic processing in conditions where behavioural measures are unreliable. Detecting and quantifying effects in individuals is crucial for clinical applications, but individual-subject analyses are frequently not reported, and are hampered by low signal-to-noise. Multivariate analyses (MVPA) may be more sensitive than traditional approaches, so we asked how robustly they could detect differential neural responses to semantically matched and mismatched word/picture pairs in individuals. With clinical application in mind, we compared data from a research-grade EEG system to concurrently recorded data from the wireless Emotiv EPOC+. In both EEG systems, despite robust group-level effects, we only detected statistically significant processing of lexico-semantic condition in 50% of individuals. Surprisingly, detection rates were similar for MVPA and univariate analyses. MVPA may be advantageous when individual responses are heterogeneous, but in this simple paradigm, lexico-semantic processing could not be reliably detected at the individual level.

Data available, click to request

Authors:

FLEMING, H., Robinson, O.J., Roiser, J.P.

Reference:

Year of publication:

-

CBU number:

8881

Abstract:

An important finding in the cognitive effort literature has been that sensitivity to the costs of effort varies between individuals, suggesting that some people find effort more aversive than others. It has been suggested this may explain individual differences in other aspects of cognition; in particular that greater effort sensitivity may underlie some of the symptoms of conditions such as depression and schizophrenia. In this paper we highlight a major problem with existing measures of cognitive effort that hampers this line of research, specifically the confounding of effort and difficulty. This means that behaviour thought to reveal effort costs could equally be explained by cognitive capacity, which influences the frequency of success and thereby the chance of obtaining reward. To address this shortcoming we introduce a new test, the Number Switching Task (NST), specially designed such that difficulty will be unaffected by the effort manipulation and can easily be standardised across participants. In a large, online sample we show that these criteria are met successfully and reproduce classic effort discounting results with the NST. We also demonstrate the use of Bayesian modelling with this task, producing behavioural parameters which can then be associated with other measures, and report a preliminary association with the Need for Cognition scale. All data and analysis scripts are provided at the Open Science Foundation repository, 10.17605/OSF.IO/X34KN, and code to run the Number Switching Task is deposited in the Gorilla Open Materials Repository, https://app.gorilla.sc/openmaterials/328049.

URL:

Data for this project is available at: https://osf.io/x34kn/

Authors:

Bozhilova, N., Welham, A., Adams, D., Bissell, S., Bruining, H., Crawford, H., Eden, K., Nelson, L., Oliver, C., Powis, L., Richards, C., Waite, J., WATSON, P., Rhys, H., Wilde, L,. Woodcock, K., Moss, J.

Reference:

Year of publication:

2023

CBU number:

8880

Abstract:

Background Phenotypic studies have identified distinct patterns of autistic characteristics in genetic syndromes associated with intellectual disability (ID), leading to diagnostic uncertainty and compromised access to autism-related support. Previous research has tended to include small samples and diverse measures, which limits the generalisability of findings. In this study, we generated detailed profiles of autistic characteristics in a large sample of > 1500 individuals with rare genetic syndromes. Methods Profiles of autistic characteristics based on the Social Communication Questionnaire (SCQ) scores were generated for thirteen genetic syndrome groups (Angelman n = 154, Cri du Chat n = 75, Cornelia de Lange n = 199, fragile X n = 297, Prader–Willi n = 278, Lowe n = 89, Smith–Magenis n = 54, Down n = 135, Sotos n = 40, Rubinstein–Taybi n = 102, 1p36 deletion n = 41, tuberous sclerosis complex n = 83 and Phelan–McDermid n = 35 syndromes). It was hypothesised that each syndrome group would evidence a degree of specificity in autistic characteristics. To test this hypothesis, a classification algorithm via support vector machine (SVM) learning was applied to scores from over 1500 individuals diagnosed with one of the thirteen genetic syndromes and autistic individuals who did not have a known genetic syndrome (ASD; n = 254). Self-help skills were included as an additional predictor. Results Genetic syndromes were associated with different but overlapping autism-related profiles, indicated by the substantial accuracy of the entire, multiclass SVM model (55% correctly classified individuals). Syndrome groups such as Angelman, fragile X, Prader–Willi, Rubinstein–Taybi and Cornelia de Lange showed greater phenotypic specificity than groups such as Cri du Chat, Lowe, Smith–Magenis, tuberous sclerosis complex, Sotos and Phelan-McDermid. The inclusion of the ASD reference group and self-help skills did not change the model accuracy. Limitations The key limitations of our study include a cross-sectional design, reliance on a screening tool which focuses primarily on social communication skills and imbalanced sample size across syndrome groups. Conclusions These findings replicate and extend previous work, demonstrating syndrome-specific profiles of autistic characteristics in people with genetic syndromes compared to autistic individuals without a genetic syndrome. This work calls for greater precision of assessment of autistic characteristics in individuals with genetic syndromes associated with ID.

URL:

Data available, click to request

Authors:

ASSEM, M., Hart, M.G., Coelho, P., Romero-Garcia, R., McDonald, A., Woodberry, E., Morris, R.C., Price, S.J., Suckling, J., Santarius, T., DUNCAN, J., EREZ, Y.

Reference:

Year of publication:

In Press

CBU number:

8878

Abstract:

Though the lateral frontal cortex is broadly implicated in cognitive control, functional MRI (fMRI) studies suggest fine-grained distinctions within this region. To examine this question electrophysiologically, we placed electrodes on the lateral frontal cortex in patients undergoing awake craniotomy for tumor resection. Patients performed verbal tasks with a manipulation of attentional switching, a canonical control demand. Power in the high gamma range (70-250 Hz) distinguished electrodes based on their location within a high-resolution fMRI network parcellation of the frontal lobe. Electrodes within the canonical fronto-parietal control network showed increased power in the switching condition, a result absent in electrodes within default mode, language, cingulo-opercular and somato-motor networks. High gamma results contrasted with spatially distributed power decreases in the beta range (12-30 Hz). These results confirm the importance of fine-scale functional distinctions within the human frontal lobe, and pave the way for increased precision of functional mapping in tumor surgeries.

Data available, click to request

Authors:

Baud, O., ARZOUNIAN, D, Bourel-Ponchel, E.

Reference:

Year of publication:

2022

CBU number:

8875

Abstract:

Montazeri Moghadam et al.1 report an automated algorithm to visually convert EEG recordings to real-time quantified interpretations of EEG in neonates. The resulting measure of the brain state of the newborn (BSN) bridges several gaps in neurocritical care monitoring.

URL:

Data available, click to request