Biology subjects

Velioglu, H. A.

Publications and source records attributed to Velioglu, H. A..

4 recordsLinked to original sources

bioRxiv2026

Predicting cortical signal mixing in coarse-resolution fMRI: a forward-operator analysis of surface mapping and the conditional benefit of correction

Surface-based fMRI projection can mix signals from cortical locations that are close in the volume but distant along the folded cortical sheet. We constructed an anatomy- and acquisition-informed linear forward operator to estimate this mixing and evaluated a regularised surface reconstruction that inverts it. In HCP 7T geometry, the operator-predicted net far-cortex contribution to a standard surface value exceeded 5% in 4%, 10% and 22% of the group-average cortex at 1.6 mm and at emulated 2.4 and 3.2 mm resolution. Including the modelled far component improved predictions of emulated coarser maps and of real coarser StudyForrest sessions in high-leakage regions. Against finer-resolution, cross-run fMRI references, the regularised reconstruction reduced the residual error in HCP development data (n = 15) and in a pre-specified, family-independent confirmatory analysis (158 participants in 86 families). The improvement increased with a response-weighted contamination measure in HCP and in an independent 3T dataset (35 test participants), although a coarse-data-only version of the measure did not generalise to 3T, where an anatomical atlas predicted the gain at twofold coarsening. Overall performance improved at twofold, but not 1.5-fold, coarsening in the 3T sample. Ablation and data-reduction analyses were consistent with an important contribution from local deconvolution but did not uniquely identify the mechanism. Benefits were smaller or absent for multi-run averages, and no robust improvement across separately acquired sessions was established. The leakage maps characterise model-predicted, signed mixing rather than directly measured signal origins, and every gain is measured against an fMRI reference rather than against the true cortical response. Code, analysis plans and group leakage maps (7T and 3T) are provided in a public repository.

neuroscience↗

bioRxiv2026

Common schizophrenia heritability concentrates in an evolutionarily young, brain-regulatory subset of fine-mapped credible-set variants

Common variation explains a substantial fraction of schizophrenia heritability, yet why these risk alleles persist remains unresolved. Of 20,766 fine-mapped PGC3 schizophrenia credible-set variants, the 4,918 with an allele-age estimate, brain-versus-blood regulatory specificity and a haplotype-based selection signal were clustered into three age-ordered subsets (Young, Mid and Old), all predating the out-of-Africa dispersal (cluster medians {approx}113-508 kyr). The Young, brain-regulatory subset concentrated schizophrenia common-variant heritability after accounting for genome-wide allele age, frequency, linkage-disequilibrium and selection architecture (conditional coefficient Z = +3.05). The concentration generalized to East Asian schizophrenia (Z = +2.83), was balanced across sexes, and tracked genetic correlation across psychiatric disorders, including bipolar disorder, but not height or body-mass index. Their persistence at common frequency is most consistent with purifying selection and mutation-selection balance on these ancient, predominantly non-coding, brain-regulatory variants rather than recent adaptation.

evolutionary biology↗

bioRxiv2022

Resting State Neural Networks at Complex Visual Hallucinations in Charles Bonnet Syndrome

BackgroundCharles Bonnet syndrome (CBS) is a prototype phenomenon for investigating complex visual hallucination. Our research focuses on resting state neural networks features of CBS patients with a comparison of patients with equally matched visual loss and healthy subjects in order to investigate the mechanism behind complex visual hallucinations. Material and MethodsFour CBS patients CBS(+), three patients with visual loss but no visual hallucinations CBS(-) and 15 healthy individuals (HS) undergo resting state fMRI recordings and their resting state data is analyzed for Default Mode Network (DMN) changes through dual regression analysis. Cognitive functions of the participants were also evaluated through Mini Mental State Examination and University of Miami - Parkinsons Disease Hallucination Questionnaire (um-PDHQ) ResultsAlthough we found no difference in Default Mode Networks between CBS(-) and CBS(+), and between the CBS(-) and HC groups, we detected decreased connectivity in CBS(+) compared to the HC group especially in visual heteromodal association centers (bilateral lateral occipital gyrus, bilateral lingual gyrus, occipital pole, right medial temporal cortex, right temporo-occipital cortex) when left angular gyrus was selected as ROI. Similarly, we detected decreased connectivity in CBS(+) compared to HC in right medial frontal gyrus, right posterior cingulate gyrus, left inferior temporal gyrus, right supramarginal gyrus, and right angular gyrus when selected right superior frontal gyrus as ROI. In contrast, increased connectivity was detected in CBS +compared to HC, in bilateral occipital poles, bilateral occipital fusiform gyrus, bilateral intracalcarine cortex, right lingual gyrus and precuneus regions when left medial temporal gyrus was selected as ROI. ConclusionOur findings suggest a combined mechanism in CBS related to increased internal created images caused by decreased visual external input causing visual hallucinations as well as impaired frontotemporal resource tracking system that together impair cognitive processing.

neuroscience↗

bioRxiv2021

Stratification of the Gut Microbiota Composition Landscape Across the Alzheimer's Disease Continuum in a Turkish Cohort

Alzheimers disease (AD) is a heterogeneous neurodegenerative disorder that spans over a continuum with multiple phases including preclinical, mild cognitive impairment, and dementia. Unlike most other chronic diseases there are limited number of human studies reporting on AD gut microbiota in the literature. These published studies suggest that the gut microbiota of AD continuum patients varies considerably throughout the disease stages, raising expectations for existence of multiple microbiota community types. However, the community types of AD gut microbiota were not systematically investigated before, leaving important research gap for diet-based intervention studies and recently initiated precision nutrition approaches aiming at stratifying patients into distinct dietary subgroups. Here, we comprehensively assessed the community types of gut microbiota across the AD continuum. We analyze 16S rRNA amplicon sequencing of stool samples from 27 mild cognitive patients, 47 AD, and 51 non-demented control subjects using tools compatible with compositional nature of microbiota. To characterize gut microbiota community types, we applied multiple machine learning techniques including partitioning around the medoid clustering, fitting probabilistic Dirichlet mixture model, Latent Dirichlet Allocation model, and performed topological data analysis for population scale microbiome stratification based on Mapper algorithm. These four distinct techniques all converge on Prevotella and Bacteroides partitioning of the gut microbiota across AD continuum while some methods provided fine scale resolution in partitioning the community landscape. The Signature taxa and neuropsychometric parameters together robustly classify the heterogenous groups within the cohort. Our results provide a framework for precision nutrition approaches and diet-based intervention studies targeting AD cohorts. IMPORTANCEThe prevalence of AD worldwide is estimated to reach 131 million by 2050. Most disease modifying treatments and drug trials have failed due partly to the heterogeneous and complex nature of the disease. Unlike other neurodegenerative diseases gut microbiota of AD patients is poorly studied. Recently initiated ambitious precision nutrition initiative or other diet-based interventions can potentially be more effective if the heterogeneous disease such as AD is deconstructed into multiple strata allowing for better identification of biomarkers across narrower patient population for improved results. Because gut microbiota is inherently integral part of the nutritional interventions there is unmet need for microbiota-informed stratification of AD clinical cohorts in nutritional studies. Our study fills in this gap and draws attention to the need for microbiota stratification as one of the essential steps for precision nutrition interventions. We demonstrate that while Prevotella and Bacteroides clusters are the consensus partitions the newly developed probabilistic methods can provide fine scale resolution in partitioning the AD gut microbiome landscape.

microbiology↗