Authors:

Sheykhangafshe, F.B., Farahanui, H., WATSON, P.

Reference:

Year of publication:

2024

CBU number:

8993

Abstract:

Background: Chronic pain’s influence on emotional well-being can be significant. It may evoke feelings of despair, frustration, nervousness, and melancholy in individuals, which often manifest as reactions to enduring pain and disruptions in their daily lives. In this study, we seek to perform Bootstrap Exploratory Graph Analysis (EGA) on the Persian Version of the Perth Alexithymia Questionnaire (PAQ) in a cohort of people with chronic pain. Methods: The research concentrated on the population of individuals encountering chronic pain within Tehran province from 2022 to 2023. Ultimately, the analysis comprised information from 234 male participants (with a mean age of 30.59, SD = 6.84) and 307 female participants (with a mean age of 30.16, SD = 6.65). After data collection, statistical analysis was conducted using the EGAnet2.0.4 package in R.4.3.2 software. Results: The outcome of bootstrapped EGA unveiled a two-dimensional configuration of the PAQ comprising Factor 1 denoted as negative difficulty in describing and identifying feelings (N-DDIF) and Factor 2 characterized as general-externally orientated thinking (GEOT), representing robust structural integrity and item consistency (all items have stabilities > 0.70). Conclusion: These findings endorse the validity of the PAQ, as evidenced by its confirmation in a broader sample using a novel methodology consistent with existing literature on two-factor decentering models.

URL:

Data available, click to request

Authors:

Bagheri, S., Farahani, H., WATSON, P., Bezdan, T. and Rezaiean, K.

Reference:

Year of publication:

2024

CBU number:

8992

Abstract:

Background This study explores the intricate web of symptoms experienced by academically gifted high school students, focusing on procrastination, rumination, perfectionism, and cognitive flexibility. The well-being of these gifted adolescents remains a pivotal concern, and understanding the dynamics of these symptoms is vital. Methods A diverse sample of 207 academically gifted high school students from Mashhad, Iran, participated in this study. Using convenience sampling, participants from grades 10, 11, and 12 were included, with detailed assessments conducted through questionnaires measuring the mentioned symptoms. Results Our network analysis uncovers compelling insights into the interplay of these symptoms: Procrastination, though moderately central, exerts significant influence within the network, underscoring its relevance. Cognitive flexibility, while centrally positioned, curiously exhibits a negative influence, potentially serving as a protective factor. Negative perfectionism emerges as the keystone symptom, with both high centrality and a positive influence. Rumination displays substantial centrality and a positive influence, indicating its role in symptom exacerbation. Positive perfectionism, moderately central, lacks direct influence on other symptoms. Conclusion This network analysis provides a nuanced understanding of the relationships among procrastination, rumination, perfectionism, and cognitive flexibility in academically gifted adolescents. Negative perfectionism and cognitive flexibility emerge as critical factors deserving attention in interventions aimed at enhancing the well-being of this unique group. Further research should explore causal relationships to refine targeted interventions.

URL:

Data available, click to request

Authors:

DEIGHTON MACINTYRE, A., CARLYLON, R.P., GOEHRING, T.

Reference:

Year of publication:

In Press

CBU number:

8991

Abstract:

During continuous speech perception, endogenous neural activity becomes time-locked to acoustic stimulus features, such as the speech amplitude envelope. This speech-brain coupling can be decoded using non-invasive brain imaging techniques, including electroencephalography (EEG). Neural decoding may provide clinical use as an objective measure of stimulus encoding by the brain – for example during cochlear implant (CI) listening, wherein the speech signal is severely spectrally degraded. Yet, interplay between acoustic and linguistic factors may lead to top-down modulation of perception, thereby complicating audiological applications. To address this ambiguity, we assess neural decoding of the speech envelope under spectral degradation with EEG in acoustically hearing listeners (n = 38; 18-35 years old) using vocoded speech. We dissociate sensory encoding from higher-order processing by employing intelligible (English) and non-intelligible (Dutch) stimuli, with auditory attention sustained using a repeated-phrase detection task. Subject-specific and group decoders were trained to reconstruct the speech envelope from held-out EEG data, with decoder significance determined via random permutation testing. Whereas speech envelope reconstruction did not vary by spectral resolution, intelligible speech was associated with better decoding accuracy in general. Results were similar across subject-specific and group analyses, with less consistent effects of spectral degradation in group decoding. Permutation tests revealed possible differences in decoder statistical significance by experimental condition. In general, while robust neural decoding was observed at the individual and group level, variability within participants would most likely prevent the clinical use of such a measure to differentiate levels of spectral degradation and intelligibility on an individual basis. The data and acoustic stimuli that support the findings of this study are openly available at DOI 10.17605/OSF.IO/CZWJ9

Data for this project is held by an external institution. Please contact the authors to request a copy.

Authors:

RAMANAN, S., AKARCA, D., HENDERSON, S.K., ROUSE, M.A., Allinson, K., PATTERSON, K., ROWE, J.B., LAMBON RALPH, M.A.

Reference:

Year of publication:

-

CBU number:

8990

Abstract:

Clinical variants of Alzheimer’s disease and frontotemporal lobar degeneration display a spectrum of cognitive-behavioural changes varying between individuals and over time. Understanding the landscape of these graded individual-/group-level longitudinal variations is critical for precise phenotyping; however, this remains challenging to model. Addressing this challenge, we leverage the National Alzheimer’s Coordinating Center database to derive a unified geometric framework of graded longitudinal phenotypic variation in Alzheimer’s disease and frontotemporal lobar degeneration. We included three time-point, cognitive-behavioural and clinical data from 390 typical, atypical and intermediate Alzheimer’s disease and frontotemporal lobar degeneration variants (114 typical Alzheimer’s disease; 107 behavioural variant frontotemporal dementia; 42 motor variants of frontotemporal lobar degeneration; and 103 primary progressive aphasia patients). On this data, we applied advanced data-science approaches to derive low-dimensional geometric spaces capturing core features underpinning clinical progression of Alzheimer’s disease and frontotemporal lobar degeneration syndromes. To do so, we first used principal component analysis to derive six axes of graded longitudinal phenotypic variation capturing patient-specific movement along and across these axes. Then, we distilled these axes into a visualisable 2D manifold of longitudinal phenotypic variation using Uniform Manifold Approximation and Projection. Both geometries together enabled the assimilation and inter-relation of paradigmatic and mixed cases, capturing dynamic individual trajectories, and linking syndromic variability to neuropathology and key clinical end-points such as survival. Through these low-dimensional geometries, we show that (i) specific syndromes (Alzheimer’s disease and primary progressive aphasia) converge over time into a de-differentiated pooled phenotype, while others (frontotemporal dementia variants) diverge to look different from this generic phenotype; (ii) phenotypic diversification is predicted by simultaneous progression along multiple axes, varying in a graded manner between individuals and syndromes; and (iii) movement along specific principal axes predicts survival at 36 months in a syndrome-specific manner and in individual pathological groupings. The resultant mapping of dynamics underlying cognitive-behavioural evolution potentially holds paradigm-changing implications to predicting phenotypic diversification and phenotype-neurobiological mapping in Alzheimer’s disease and frontotemporal lobar degeneration. The National Alzheimer’s Coordinating Center dataset are freely available through request on their official website (https://naccdata.org/). Code for all analyses from this study have been made available at: https://github.com/siddharthramanan/NACC_UMAP.

Data for this project is held by an external institution. Please contact the authors to request a copy.

Authors:

BRADSHAW, A.R., Wheeler, E.D., McGettigan, C., Lametti, D.R.

Reference:

Year of publication:

In Press

CBU number:

8989

Abstract:

This study tested the hypothesis that speaking with other voices can influence sensorimotor predictions of one’s own voice. Real-time manipulations of auditory feedback were used to drive sensorimotor adaptation in speech, while participants spoke sentences in synchrony with another voice, a task known to induce implicit imitation (phonetic convergence). The acoustic-phonetic properties of the other voice were manipulated between-groups, such that convergence with it would either oppose (incongruent group, n = 15) or align with speech motor adaptation (congruent group, n = 16). As predicted, significantly greater adaptation was seen in the congruent compared to the incongruent group. This suggests the use of shared sensory targets in speech for predicting the sensory outcomes of both the actions of others (speech perception) and the actions of the self (speech production). This finding has important implications for wider theories of shared predictive mechanisms across perception and action, such as active inference. Data available at: https://osf.io/h26ur/

Data for this project is held by an external institution. Please contact the authors to request a copy.

Authors:

ZHOU, A., DUNCAN, J., MITCHELL, D.

Reference:

Year of publication:

In Press

CBU number:

8988

Abstract:

Default mode network (DMN) activity, measured with fMRI, typically increases during internally directed thought, and decreases during tasks that demand externally focused attention. However, Crittenden et al. (2015) and Smith et al. (2018) reported increased DMN activity during demanding external task switches between different cognitive domains, compared to within-domain switches and task repeats. This finding is hard to reconcile with many dominant views of DMN function. Here, we aimed to replicate this DMN task-switch effect in a similar paradigm and test whether it reflects increased representation of broader context, specifically of a scene presented behind the focal task. In the Core DMN, we found significant activity for all task switches, compared to task repeats, and stronger activity for switches between rest and task. Although the content of the background scene was attended, recalled, and neurally decodable, there was no evidence that this differed by switch type. Therefore, external task switches activated DMN without enhanced processing of the surrounding background. Surprisingly, DMN activity at within-domain switches was no less than at between-domain switches. We suggest that modulation of DMN activity by task switches reflects a shift in the current cognitive model and depends on the overall complexity of that model.

Data available, click to request

Authors:

VEDECJINA, M., ASTLE, D.E., Holmes, J.

Reference:

Year of publication:

In Press

CBU number:

8987

Abstract:

Early life adversity is associated with differences in brain function and an elevated risk for poor mental health. Using data from children aged 10 (N = 5,798) from the Adolescent Brain Cognitive Development (ABCD) cohort, we investigated how adversity relates to functional brain organisation using a network neuroscience approach. We derived four data-driven categories of adversity by fitting a mixed graphical model: household/community instability, physical/sexual abuse, parental neglect, and financial difficulties. Analyses revealed that multiple forms of adversity were associated with reduced clustering and increased assortativity across the entire brain and that these local measures of organisation captured greater adversity-related variance than mesoscale measures like modularity. The most pronounced effects were in the somatosensory and subcortical networks. Financial difficulties showed the strongest and most widespread associations with brain organisation, with evidence of a mediating effect of assortativity on the association between financial difficulties and internalising symptoms. Adding race as a covariate attenuated most brain-adversity relationships, suggesting that experiences of adversity are strongly related to race/ethnicity in the ABCD sample. These results demonstrate that different forms of adversity are associated with both shared and unique variations in functional brain organisation, highlighting its potential significance for explaining individual differences in mental health outcomes following early life adversity.

Authors:

Ingram, R.U., Ocal, D., HALAI, A.D., Pobric, G., Cash, D., Crutch, S.J., Yong, K., LAMBON RALPH, M.A.

Reference:

Year of publication:

In Press

CBU number:

8986

Abstract:

Background and Objectives: Alzheimer’s disease spans heterogeneous typical and atypical phenotypes. Posterior cortical atrophy is one striking example, characterised by prominent impairment in visual and other posterior functions in contrast to typical, amnestic Alzheimer’s disease. The primary study objective was to establish how the similarities and differences of cognition and brain volumes within Alzheimer’s disease and posterior cortical atrophy (and by extension other Alzheimer’s disease variants), can be conceptualised as systematic variations across a transdiagnostic, graded multidimensional space. Methods: This was a cross-sectional, single-center, observational, cohort study performed at the National Hospital for Neurology & Neurosurgery, London, UK. Data were collected from a cohort of PCA and AD patients, matched for age, disease duration and MMSE scores. There were two sets of outcome measures: (1) scores on a neuropsychological battery containing 22 tests spanning visuoperceptual and visuospatial processing, episodic memory, language, executive functions, calculation, and visuospatial processing; and (2) measures extracted from high-resolution T1-weighted volumetric MRI scans. Principal component analysis was used to extract the transdiagnostic dimensions of phenotypical variation from the detailed neuropsychological data. Voxel-based morphometry was used to examine associations between the PCA-derived clinical phenotypes and the structural measures. Results: We enrolled 93 PCA participants (mean: age = 59.9 yrs, MMSE = 21.2; 59/93 female) and 58 AD participants (mean: age = 57.1 yrs, MMSE = 19.7; 22/58 female). The principal component analysis for posterior cortical atrophy (sample adequacy confirmed: Kaiser Meyer-Olkin = 0.865) extracted three dimensions accounting for 61.0% of variance in patients’ performance, reflecting general cognitive impairment, visuoperceptual deficits and visuospatial impairments. Plotting Alzheimer’s disease cases into the posterior cortical atrophy derived multidimensional space, and vice versa, revealed graded, overlapping variations between cases along these dimensions, with no evidence for categorical-like patient clustering. Likewise, the relationship between brain volumes and scores on the extracted dimensions was overlapping for posterior cortical atrophy and Alzheimer’s disease cases. Discussion: These results provide evidence supporting a reconceptualization of clinical and radiological variation in these heterogenous Alzheimer’s disease phenotypes as being along shared phenotypic continua spanning posterior cortical atrophy and Alzheimer’s disease, arising from systematic graded variations within a transdiagnostic, multidimensional neurocognitive geometry.

Authors:

GAULTIER, C., GOEHRING, T.

Reference:

Year of publication:

In Press

CBU number:

8985

Abstract:

For cochlear implant (CI) listeners, holding a conversation in noisy and reverberant environ- ments is often challenging. Deep learning algorithms can potentially mitigate these difficul- ties by enhancing speech in everyday listening environments. This study compared several deep learning algorithms with access to one, two unilateral or six bilateral microphones that were trained to recover speech signals by jointly removing noise and reverberation. The noisy-reverberant speech and an ideal noise-reduction algorithm served as lower and upper references. Objective signal metrics were compared with results from two listening tests, including 15 typical hearing listeners with CI simulations and 12 CI listeners. Large and statistically significant improvements in speech reception thresholds of 7.4 and 10.3 dB were found for the multi-microphone algorithms. For the single-microphone algorithm, there was an improvement of 2.3 dB, but only for the CI listener group. The objective signal met- rics correctly predicted the rank order of results for CI listeners, and there was an overall agreement for most effects and variances between results for CI simulations and CI listeners. These algorithms hold promise to improve speech intelligibility for CI listeners in environ- ments with noise and reverberation, and benefit from a boost in performance when using features extracted from multiple microphones.

Authors:

Kong, Y., Zhao, C., Li, D., Li, B., Hu, Y., Liu, H., WOOLGAR, A., Guo, J., Song, Y.

Reference:

Year of publication:

In Press

CBU number:

8984

Abstract:

While the auditory and visual systems each provide distinct information to our brain, they also work together to process and prioritize input to address ever-changing conditions. Previous studies highlighted the trade-off between auditory change detection and visual selective attention, however, the relationship between them is still unclear. Here, we recorded electroencephalography signals from 106 healthy adults in three experiments. Our findings revealed a positive correlation at the population level between the amplitudes of ERP indices associated with auditory change detection (MMN) and visual selective attention (N2pc) when elicited in separate tasks. This correlation persisted even when participants performed a visual task while disregarding simultaneous auditory stimuli. Interestingly, as visual attention demand increased, participants whose N2pc amplitude increased the most exhibited the largest reduction in MMN, suggesting a within-subject trade-off between the two processes. Taken together, our results suggest an intimate relationship and potential shared mechanism between auditory change detection and visual selective attention. We liken this to a total capacity limit that varies between individuals, which could drive correlated individual differences in auditory change detection and visual selective attention, and also within-subject competition between the two, with task-based modulation of visual attention causing within-participant decrease in auditory change detection sensitivity.

Data available, click to request