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Yucel, M.

Publications and source records attributed to Yucel, M..

11 recordsLinked to original sources

An Integrated EEG, TMS-EEG and Behavioural Dataset for Investigating the Neural Correlates of Working Memory

Working memory is a fundamental process that underlies cognition. Accordingly, the neural mechanisms that support working memory performance are of great interest in cognitive neuroscience. We present a publicly accessible dataset comprising 123 healthy adults (mean age = 28.58 years, SD = 7.56; age range = 18-46 years; 75 females) to facilitate investigation of the neural mechanisms underlying working memory performance in humans. Across two days of assessment, participants underwent a battery of cognitive tasks assessing working memory and other cognitive domains (day 1), followed by 62-channel EEG recordings (day 2). EEG data were acquired at rest, during a visual working memory task, and following single-pulse transcranial magnetic stimulation (TMS-EEG) targeting key brain regions involved in working memory. This article provides a detailed overview of the study design, methodology, and data characteristics.

neuroscience↗

Default mode and motor networks facilitate early learning of implicit motor sequences: a multimodal MR spectroscopy and fMRI study

Learning new motor skills is a fundamental process that involves the sequencing of actions. Skill develops with practice and time, and manifests as performance that is fast and accurate. Although we know that learning can occur through an implicit process in the absence of conscious awareness, and across multiple temporal scales, the precise neural mechanisms mediating implicit motor sequence learning remain poorly understood. Similarly, the capacity for interventions with known influence on learning and memory, such as cardiovascular exercise, to facilitate implicit learning is yet to be clearly established. Here, we investigated the neuroplasticity of implicit motor sequence learning and the effect of acute exercise priming. Healthy adults (39.5% female) aged 22.55 {+/-} 2.69 years were allocated to either a high-intensity exercise (HIIT) group (n = 16) or to a very low-intensity control group (n = 17). Following exercise, participants performed a serial reaction time task. MR spectroscopy estimates of sensorimotor gamma-aminobutyric acid (GABA) were acquired before and after exercise, and during task performance, and resting-state fMRI was acquired at the end of the protocol. We show that early stages of learning are linked to default mode network connectivity, while the overall degree of learning following sustained practice is associated with motor network connectivity. Sensorimotor GABA concentration was linked to the early stages of learning, and GABA concentration was modulated following HIIT, although the two were not related. Overall, via integration of multiple neuroimaging modalities we demonstrate that interactions between hippocampal and motor networks underlie implicit motor sequence learning. Key points summary- Motor learning occurs across different temporal scales and can arise implicitly in the absence of conscious awareness. - Explicit motor learning is linked to the brains primary inhibitory neurotransmitter, GABA, and interactions across motor and hippocampal networks. - Whether these same neural mechanisms are implicated in implicit learning is unclear. Similarly, the capacity to influence learning via priming with cardiovascular exercise is yet to be clearly established. - We show that early implicit learning is underpinned by default mode network connectivity and sensorimotor GABA concentration, while total learning following sustained practice is linked to motor network connectivity. We also found that HIIT exercise elevated sensorimotor GABA concentration, but not the magnitude of implicit learning. - Overall, our results highlight shared involvement of default mode and motor networks in implicit motor sequence learning.

neuroscience↗

Multiscale heterogeneity of white matter morphometry in psychiatric disorders

BackgroundInter-individual variability in neurobiological and clinical characteristics in mental illness is often overlooked by classical group-mean case-control studies. Studies using normative modelling to infer person-specific deviations of grey matter volume have indicated that group means are not representative of most individuals. The extent to which this variability is present in white matter morphometry, which is integral to brain function, remains unclear. MethodsWe applied Warped Bayesian Linear Regression normative models to T1- weighted magnetic resonance imaging data and mapped inter-individual variability in person-specific white matter volume deviations in 1,294 cases (58% male) diagnosed with one of six disorders (attention-deficit/hyperactivity, autism, bipolar, major depressive, obsessive-compulsive and schizophrenia) and 1,465 matched controls (54% male) recruited across 25 scan sites. We developed a framework to characterize deviation heterogeneity at multiple spatial scales, from individual voxels, through inter- regional connections, specific brain regions, and spatially extended brain networks. ResultsThe specific locations of white matter volume deviations were highly heterogeneous across participants, affecting the same voxel in fewer than 8% of individuals with the same diagnosis. For autism and schizophrenia, negative deviations (i.e., areas where volume is lower than normative expectations) aggregated into common tracts, regions and large-scale networks in up to 35% of individuals. ConclusionsThe prevalence of white matter volume deviations was lower than previously observed in grey matter, and the specific location of these deviations was highly heterogeneous when considering voxel-wise spatial resolution. Evidence of aggregation within common pathways and networks was apparent in schizophrenia and autism but not other disorders.

neuroscience↗

Decoding Attended Spatial Location during Complex Scene Analysis with fNIRS.

When analyzing complex scenes, humans often focus their attention on an object at a particular spatial location. The ability to decode the attended spatial location would facilitate brain computer interfaces for complex scene analysis. Here, we investigated functional near-infrared spectroscopys (fNIRS) capability to decode audio-visual spatial attention in the presence of competing stimuli from multiple locations. We targeted dorsal frontoparietal network including frontal eye field (FEF) and intra-parietal sulcus (IPS) as well as superior temporal gyrus/planum temporal (STG/PT). They all were shown in previous functional magnetic resonance imaging (fMRI) studies to be activated by auditory, visual, or audio-visual spatial tasks. We found that fNIRS provides robust decoding of attended spatial locations for most participants and correlates with behavioral performance. Moreover, we found that FEF makes a large contribution to decoding performance. Surprisingly, the performance was significantly above chance level 1s after cue onset, which is well before the peak of the fNIRS response. Our results demonstrate that fNIRS is a promising platform for a compact, wearable technology that could be applied to decode attended spatial location and reveal contributions of specific brain regions during complex scene analysis.

bioengineering↗

The effects of multi-day rTMS and cardiorespiratory fitness on working memory and local GABA concentration

Working memory (WM) refers to the capacity to temporarily retain and manipulate finite amounts of information; a critical process in complex behaviours such as reasoning, comprehension, and learning. This cognitive function is supported by a parietal-prefrontal network and linked to the activity of key neurotransmitters, such as gamma-aminobutyric acid (GABA). Impairments in WM are seen in a range of psychiatric and neurological disorders, and there are currently no effective treatments. In this study, we analysed secondary outcome measures from a trial investigating the effects of multi-day rTMS on cognition. Participants received four days of 20 Hz rTMS to an individualised region of left parietal cortex in one week, and an individualised region of pre-supplementary motor area (pre-SMA) in a separate week. We assessed changes to WM function before and after each week of stimulation (N = 39), and changes to GABA concentration before and after stimulation in week one using MR spectroscopy (N = 18 per stimulation condition). We hypothesised that parietal rTMS would enhance WM and alter GABA concentration at the site of stimulation, but this was not observed. Instead, we report some evidence of improved WM function following the first week of pre-SMA rTMS stimulation, and a generalised increase in GABA concentration across both parietal and pre-SMA voxels following pre-SMA rTMS. Additionally, we found that higher cardiorespiratory fitness was associated with greater WM improvement following pre-SMA stimulation. This study does not support the use of parietal multi-day rTMS for the enhancement of working memory. In contrast, the results suggest that increasing cardiorespiratory fitness may provide a novel approach to enhance the effects of pre-SMA rTMS on cognition.

neuroscience↗

The effects of regular aerobic exercise on hippocampal structure and function

Regular exercise has numerous benefits for brain health, including the structure and function of the hippocampus. The hippocampus plays a critical role in memory function, and is altered in a number of psychiatric disorders associated with memory impairments (e.g. depression and schizophrenia), as well as healthy ageing. While many studies have focussed on how regular exercise may improve hippocampal integrity in older individuals, less is known about these effects in young to middle-aged adults. Therefore, we assessed the associations of regular exercise and cardiorespiratory fitness with hippocampal structure and function in these age groups. We recruited 40 healthy young to middle-aged adults, comprised of two groups (n = 20) who self-reported either high or low levels of exercise, according to World Health Organisation guidelines. We assessed cardiorespiratory fitness using a graded exercise test (VO2max) and hippocampal structure via manual tracing of T1-weighted magnetic resonance images. We also assessed hippocampal function using magnetic resonance spectroscopy to derive estimates of NAA concentration and hippocampal-dependent associative memory and pattern separation tasks. We observed evidence of increased N-acetyl-aspartate concentration and associative memory performance in individuals engaging in high levels of exercise. However, no differences in hippocampal volume or pattern separation capacity were observed between groups. Cardiorespiratory fitness was positively associated with hippocampal volume (left, right, and bilateral), N-acetyl-aspartate concentration, and pattern separation. However, no association was observed between cardiorespiratory fitness and associative memory. Therefore, we provide evidence that higher levels of exercise and cardiorespiratory fitness are associated with improved hippocampal structure and function. Exercise may provide a low-risk, effective method of improving hippocampal integrity in an early-to-mid-life stage.

neuroscience↗

Multi-day rTMS exerts site-specific effects on functional connectivity but does not influence associative memory performance

Transcranial magnetic stimulation (TMS) is a non-invasive brain stimulation technique with the capacity to modulate brain network connectivity and cognitive function. Recent studies have demonstrated long-lasting improvements in associative memory and resting-state connectivity following multi-day repetitive TMS (rTMS) to individualised parietal-hippocampal networks. We aimed to assess the reproducibility and network- and cognitive-specificity of these effects following multi-day rTMS. Participants received four days of 20 Hz rTMS to a subject-specific region of left lateral parietal cortex exhibiting peak functional connectivity to the left hippocampus. In a separate week, the same stimulation protocol was applied to a subject-specific region of pre-supplementary motor area (pre-SMA) exhibiting peak functional connectivity to the left putamen. We assessed changes to associative memory before and after each week of stimulation (N = 39), and changes to resting-state functional connectivity before and after stimulation in week one (N = 36). We found no evidence of long-lasting enhancement of associative memory or increased parieto-hippocampal connectivity following multi-day rTMS to the parietal cortex, nor increased pre-SMA-putamen connectivity following multi-day rTMS to pre-SMA. Instead, we observed some evidence of site-specific modulations of functional connectivity lasting [~]24 hours, with reduced connectivity within targeted networks and increased connectivity across distinct non-targeted networks. Our findings suggest a complex interplay between multi-day rTMS and network connectivity. Further work is required to develop reliable rTMS paradigms for driving changes in functional connectivity between cortical and subcortical regions.

neuroscience↗

Fractionation of impulsive and compulsive trans-diagnostic phenotypes and their longitudinal associations

ObjectiveYoung adulthood is a crucial neurodevelopmental period during which impulsive and compulsive problem behaviours commonly emerge. While traditionally considered diametrically opposed, impulsive and compulsive symptoms tend to co-occur. The objectives of this study were: (i) to identify the optimal trans-diagnostic structural framework for measuring impulsive and compulsive problem behaviours; and (ii) to use this optimal framework to identify common/distinct antecedents of these latent phenotypes.\n\nMethods654 young adults were recruited as part of the Neuroscience in Psychiatry Network (NSPN), a population-based cohort in the United Kingdom. The optimal trans-diagnostic structural model capturing 33 types of impulsive and compulsive problem behaviours was identified. Baseline predictors of subsequent impulsive and compulsive trans-diagnostic phenotypes were characterised, along with cross-sectional associations, using Partial Least Squares (PLS).\n\nResultsCurrent problem behaviours were optimally explained by a bi-factor model, which yielded dissociable measures of impulsivity and compulsivity, as well as a general disinhibition factor. Impulsive problem behaviours were significantly explained by prior antisocial and impulsive personality traits, male gender, general distress, perceived dysfunctional parenting, and teasing/arguments within friendships. Compulsive problem behaviours were significantly explained by prior compulsive traits, and female gender.\n\nConclusionsThis study demonstrates that trans-diagnostic phenotypes of 33 impulsive and compulsive problem behaviours are identifiable in young adults, utilizing a bi-factor model based on responses to a single questionnaire. Furthermore, these phenotypes have different antecedents. The findings yield a new framework for fractionating impulsivity and compulsivity; and suggest different early intervention targets to avert emergence of problem behaviours. This framework may be useful for future biological and clinical dissection of impulsivity and compulsivity.

neuroscience↗

Transdiagnostic variations in impulsivity and compulsivity in obsessive-compulsive disorder and gambling disorder correlate with effective connectivity in cortical-striatal-thalamic-cortical circuits.

BackgroundIndividual differences in impulsivity and compulsivity is thought to underlie vulnerability to a broad range of disorders and are closely tied to cortical-striatal-thalamic-cortical (CSTC) function. However, whether impulsivity and compulsivity in clinical disorders is continuous with the healthy population and explains CSTC dysfunction across different disorders remains unclear.\n\nMethodsWe characterized the relationship between CSTC effective connectivity, estimated using dynamic causal modelling of functional magnetic resonance imaging data, and dimensional phenotypes of impulsivity and compulsivity in two symptomatically distinct but phenotypically related disorders, obsessive-compulsive disorder (OCD) and gambling disorder (GD). 487 online participants provided data for modelling of dimensional phenotypes. These data were combined with 34 OCD patients, 22 GD patients, and 39 healthy controls, who underwent functional magnetic resonance imaging.\n\nResultsThree core dimensions were identified: disinhibition, impulsivity, and compulsivity. Patients scores on these dimensions were continuously distributed with the healthy participants, supporting a continuum model of psychopathology. Across all participants, higher disinhibition correlated with lower bottom-up connectivity in the dorsal circuit and increased bottom-up connectivity in the ventral circuit, and higher compulsivity correlated with reduced bottom-up connectivity in the dorsal circuit. Similar changes in effective connectivity were observed with increasing clinical severity that were not accounted for by phenotypic variation, demonstrating convergence towards behaviourally and clinically relevant changes in brain dynamics. Effective connectivity did not differ as a function of traditional diagnostic labels.\n\nConclusionsCSTC dysfunction across OCD and GD is better characterized by dimensional phenotypes than diagnostic comparisons, supporting investigation of quantitative liability phenotypes.

neuroscience↗

An evaluation of the efficacy, reliability, and sensitivity of motion correction strategies for resting-state functional MRI

Estimates of functional connectivity derived from resting-state functional magnetic resonance imaging (rs-fMRI) are sensitive to artefacts caused by in-scanner head motion. This susceptibility has motivated the development of numerous denoising methods designed to mitigate motion-related artefacts. Here, we compare popular retrospective rs-fMRI denoising methods, such as regression of head motion parameters and mean white matter (WM) and cerebrospinal fluid (CSF) (with and without expansion terms), aCompCor, volume censoring (e.g., scrubbing and spike regression), global signal regression and ICA-AROMA, combined into 19 different pipelines. These pipelines were evaluated across five different quality control benchmarks in four independent datasets associated with varying levels of motion. Pipelines were benchmarked by examining the residual relationship between in-scanner movement and functional connectivity after denoising; the effect of distance on this residual relationship; whole-brain differences in functional connectivity between high- and low-motion healthy controls (HC); the temporal degrees of freedom lost during denoising; and the test-retest reliability of functional connectivity estimates. We also compared the sensitivity of each pipeline to clinical differences in functional connectivity in independent samples of schizophrenia and obsessive-compulsive disorder. Our results indicate that (1) simple linear regression of regional fMRI time series against head motion parameters and WM/CSF signals (with or without expansion terms) is not sufficient to remove head motion artefacts; (2) aCompCor pipelines may only be viable in low-motion data; (3) volume censoring performs well at minimising motion-related artefact but a major benefit of this approach derives from the exclusion of high-motion individuals; (4) while not as effective as volume censoring, ICA-AROMA performed well across our benchmarks for relatively low cost in terms of data loss; and (5) group comparisons in functional connectivity between healthy controls and schizophrenia patients are highly dependent on preprocessing strategy. We offer some recommendations for best practice and outline some simple analyses to facilitate transparent reporting of the degree to which a given set of findings may be affected by motion-related artefact.

neuroscience↗

Transcriptional signatures of connectomic subregions of the human striatum

Functionally distinct regions of the brain are thought to possess a characteristic connectional fingerprint - a profile of incoming and outgoing connections that defines the function of that area. This observation has motivated efforts to subdivide cortical and subcortical areas using their patterns of connectivity. However, it remains unclear whether these connectomically-defined subregions of the brain can be distinguished at the molecular level. Here, we combine high-resolution diffusion-weighted magnetic resonance imaging with comprehensive transcriptomic data to show that connectomically-defined subregions of the striatum carry distinct transcriptional signatures. Using data-driven clustering of diffusion tractography, seeded from the striatum, in 100 healthy individuals, we identify a tripartite organization of the caudate and putamen that comprises ventral, dorsal, and caudal subregions. We then use microarray data of gene expression levels in 19 343 genes, taken from 98 tissue samples distributed throughout the striatum, to accurately discriminate the three connectomically-defined subregions with 80-90% classification accuracy using linear support vector machines. This classification accuracy was robust at the group and individual level. Genes contributing strongly to the classification were enriched for gene ontology categories including dopamine signaling, glutamate secretion, response to amphetamine, and metabolic pathways, and were implicated in risk for disorders such as schizophrenia, autism, and Parkinsons disease. Our findings highlight a close link between regional variations in transcriptional activity and interregional connectivity in the brain, and suggest that there may be a strong genomic signature of connectomically-defined subregions of the brain.

neuroscience↗