Biology subjects

Tozlu, C.

Publications and source records attributed to Tozlu, C..

11 recordsLinked to original sources

bioRxiv2025

Cocaine use disorder is associated with lower brain state transition energy particularly in higher order and excitatory networks

Cocaine use disorder (CUD) detrimentally impacts personal health, social relationships, and economic opportunity. Here, we assess CUD-associated shifts in brain dynamics using Network Control Theory and examine how they align with previously identified changes in neurological systems and behavioral profiles of people with CUD. The SUDMEX CONN dataset consists of multi-modal MRI, cocaine use metrics, behavioral measures, and demographics of individuals with CUD (N=132, 71 CUD). We identified recurring brain activity states and used NCT to calculate the transition energy (TE) between pairs of states. ANCOVAs examined global and regional TE associations with drug use group (CUD vs controls (NC)), years of CUD, and risk-taking behaviors. We identified potential mechanisms driving the differences by correlating CUD-related regional TE effects with neurotransmitter/receptor systems. People with CUD had significantly lower global TE and default mode, dorsal attention and limbic network TE compared to non-user controls, particularly in regions enriched for noradrenaline and mu opioid receptors. Longer duration of CUD was associated with more decreased global TE, top-down TE, default mode, control and ventral attention network TE, and regional TE enriched for excitatory neurotransmitters and receptors. People with CUD needed to expend more global and top-down TE to perform better on a risk-taking task (the Iowa Gambling task), an effect which was not found in NCs. Our analysis of whole-brain activity dynamics provides a link between the effects of upstream glutamatergic excitotoxicity and/or opioid receptor dysfunction, and downstream weakening of inhibitory control that is central to CUD.

neuroscience↗

bioRxiv2025

Disrupted Energy Landscape in Individuals with Mild Cognitive Impairment: Insights from Network Control Theory

IntroductionPatients with mild cognitive impairment (MCI) have shown disruptions in both brain structure and function, often studied separately. However, understanding the relationship between brain structure and function can provide valuable insights into this early stage of cognitive decline for better treatment strategies to avoid its progression. Network Control Theory (NCT) is a multi-modal approach that captures the alterations in the brains energetic landscape by combining the brains functional activity and the structural connectome. Our study aims to explore the differences in the brains energetic landscape between people with MCI and healthy controls (HC). MethodsFour hundred ninety-nine HC and 55 MCI patients were included. First, k-means was applied to functional MRI (fMRI) time series to identify commonly recurring brain activity states. Second, NCT was used to calculate the minimum energy required to transition between these brain activity states, otherwise known as transition energy (TE). The entropy of the fMRI time series as well as PET-derived amyloid beta (A{beta}) and tau deposition were measured for each brain region. The TE and entropy were compared between MCI and HC at the network, regional, and global levels using linear models where age, sex, and intracranial volume were added as covariates. The association of TE and entropy with A{beta} and tau deposition was investigated in MCI patients using linear models where age, sex, and intracranial volume were controlled. ResultsCommonly recurring brain activity states included those with high and low amplitude activity in visual (+/-), default mode (+/-), and dorsal attention (+/-) networks. Compared to HC, MCI patients required lower transition energy in the limbic network (adjusted p = 0.028). Decreased global entropy was observed in MCI patients compared to HC (p = 7.29e-7). There was a positive association between TE and entropy in the frontoparietal network (p = 7.03e-3). Increased global A{beta} was associated with higher global entropy in MCI patients ({rho} = 0.632, p = 0.041). ConclusionLower TE in the limbic network in MCI patients may indicate either neurodegeneration-related neural loss and atrophy or a potential functional upregulation mechanism in this early stage of cognitive impairment. Future studies that include people with AD are needed to better characterize the changes in the energetic landscape in the later stages of cognitive impairment.

neuroscience↗

bioRxiv2025

Predicting cognition using estimated structural and functional connectivity networks and artificial intelligence in multiple sclerosis

BackgroundOur prior work demonstrated that estimated structural and functional connectomes (eSC and eFC) generated using multiple sclerosis (MS) lesion masks and artificial intelligence (AI) models can predict disability as effectively as SC and FC derived from diffusion and functional MRI in MS. The goal of this study was to assess the ability of eSC and eFC in predicting baseline and 4-year follow-up cognition in MS patients. MethodsOne hundred seventy-one MS patients (age: 42.67{+/-}10.41, 74% females) were included. The Symbol Digit Modalities Test (SDMT), California Verbal Learning Test (CVLT), and Brief Visuospatial Memory Test (BVMT) were used to assess cognition. The Network Modification tool was performed to estimate SC, which was then used as an input to Krakencoder, an encoder-decoder model, to estimate FC. Ridge regression was performed to predict cognition using regional eSC and eFC, along with demographics and clinical information as well as conventional MRI metrics. Baseline cognition was added to the models that were used to predict the follow-up cognition. Spearmans correlation (r) was used to assess the prediction accuracy. ResultsThe highest accuracy was obtained when predicting follow-up SDMT using regional eSC or eFC (median r=0.58 for eSC and r=0.56 for eFC). Decreased eSC and eFC in the cerebellum and increased eFC in the default mode network were associated with lower follow-up SDMT scores. Baseline SDMT, clinical subtype, and age were the most important non-connectome metrics in predicting follow-up SDMT. ConclusionsOur findings demonstrate that eSC and eFC derived from clinically acquired MRI and AI models can effectively predict cognition. The use of lesion-based estimates of connectome disruptions may potentially improve cognition-related individualized treatment planning.

neuroscience↗

bioRxiv2025

TSPO-PET Reveals Higher Inflammation in White Matter Disrupted by Paramagnetic Rim Lesions in Multiple Sclerosis

ObjectiveTo explore whether the inflammatory activity is higher in white matter (WM) tracts disrupted by paramagnetic rim lesions (PRLs) and if inflammation in PRL-disrupted WM tracts is associated with disability in people with multiple sclerosis (MS). MethodsForty-four MS patients and 16 healthy controls were included. 18 kDa-translocator protein positron emission tomography (TSPO-PET) with the 11C-PK11195 radioligand was used to measure the neuroinflammatory activity. The Network Modification Tool was used to identify WM tracts disrupted by PRLs and non-PRLs that were delineated on MRI. The Expanded Disability Status Scale was used to measure disability. ResultsMS patients had higher inflammatory activity in whole brain WM compared to healthy controls (p=0.001). Compared to patients without PRLs, patients with PRLs exhibited higher levels of inflammatory activity in the WM tracts disrupted by any type of lesions (p=0.02) or PRLs (p=0.004). In patients with at least one PRL, inflammatory activity was higher in WM tracts highly disrupted by PRLs compared to WM tracts highly disrupted by non-PRLs (p=0.009). Elevated inflammatory activity in highly disrupted WM tracts was associated with increased disability in patients with PRL (p=0.03), but not in patients without PRL (p=0.2). InterpretationThis study suggests that patients with PRLs may exhibit more diffuse WM inflammation in addition to higher inflammation along WM tracts disrupted by PRLs compared to non-PRLs, which could contribute to larger lesion volumes and faster disability progression. Imaging PRLs may serve to identify patients with both focal and diffuse inflammation, guiding therapeutic interventions aimed at reducing inflammation and preventing progressive disability in MS.

neuroscience↗

bioRxiv2024

Sex-specific differences in brain activity dynamics of youth with a family history of substance use disorder

An individuals risk of substance use disorder (SUD) is shaped by a complex interplay of potent biosocial factors. Current neurodevelopmental models posit vulnerability to SUD in youth is due to an overreactive reward system and reduced inhibitory control. Having a family history of SUD is a particularly strong risk factor, yet few studies have explored its impact on brain function and structure prior to substance exposure. Herein, we utilized a network control theory approach to quantify sex-specific differences in brain activity dynamics in youth with and without a family history of SUD, drawn from a large cohort of substance-naive youth from the Adolescent Brain Cognitive Development Study. We summarize brain dynamics by calculating transition energy, which probes the ease with which a whole brain, region or network drives the brain towards a specific spatial pattern of activation (i.e., brain state). Our findings reveal that a family history of SUD is associated with alterations in the brains dynamics wherein: i) independent of sex, certain regions transition energies are higher in those with a family history of SUD and ii) there exist sex-specific differences in SUD family history groups at multiple levels of transition energy (global, network, and regional). Family history-by-sex effects reveal that energetic demand is increased in females with a family history of SUD and decreased in males with a family history of SUD, compared to their same-sex counterparts with no SUD family history. Specifically, we localize these effects to higher energetic demands of the default mode network in females with a family history of SUD and lower energetic demands of attention networks in males with a family history of SUD. These results suggest a family history of SUD may increase reward saliency in males and decrease efficiency of top-down inhibitory control in females. This work could be used to inform personalized intervention strategies that may target differing cognitive mechanisms that predispose individuals to the development of SUD.

neuroscience↗

bioRxiv2023

The sequence of regional structural disconnectivity due to multiple sclerosis lesions

ObjectivePrediction of disease progression is challenging in multiple sclerosis (MS) as the sequence of lesion development and retention of inflammation within a subset of chronic lesions is heterogeneous among patients. We investigated the sequence of lesion-related regional structural disconnectivity across the spectrum of disability and cognitive impairment in MS. MethodsIn a full cohort of 482 patients, the Expanded Disability Status Scale was used to classify patients into (i) no or mild vs (ii) moderate or severe disability groups. In 363 out of 482 patients, Quantitative Susceptibility Mapping was used to identify paramagnetic rim lesions (PRL), which are maintained by a rim of iron-laden innate immune cells. In 171 out of 482 patients, Brief International Cognitive Assessment was used to identify subjects with cognitive impairment. Network Modification Tool was used to estimate the regional structural disconnectivity due to MS lesions. Discriminative event-based modeling was applied to investigate the sequence of regional structural disconnectivity due to all representative lesions across the spectrum of disability and cognitive impairment. ResultsStructural disconnection in the ventral attention and subcortical networks was an early biomarker of moderate or severe disability. The earliest biomarkers of disability progression were structural disconnections due to PRL in the motor-related regions. Subcortical structural disconnection was an early biomarker of cognitive impairment. InterpretationMS lesion-related structural disconnections in the subcortex is an early biomarker for both disability and cognitive impairment in MS. PRL-related structural disconnection in the motor cortex may identify the patients at risk for moderate or severe disability in MS.

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bioRxiv2022

Larger lesion volume in people with multiple sclerosis is associated with increased transition energies between brain states and decreased entropy of brain activity

BackgroundNeuronal loss, demyelination, and inflammation in the brains of people with multiple sclerosis (pwMS) can cause impairment or disability across a range of domains. However, the mapping between severity or location of this brain tissue damage and an individuals impairment remains challenging. Advanced neuroimaging techniques may enable us to better understand the neuropathological or compensatory mechanisms in MS and quantify the link between the brains functional activity and its structural backbone. One recently developed approach uses network control theory to characterize the brains energetic landscape using an individuals structural connectome and brain activity dynamics. While brain activity dynamics have previously been analyzed in MS cohorts, investigating how structural network damage, one of the hallmarks of MS, relates to shifts in activity dynamics has not yet been performed. AimsWe apply brain activity dynamics analysis and network control theory (NCT) to functional time series and structural connectomes in healthy controls (HC) and pwMS with and without disability and compare the three groups resulting metrics. We also assessed the entropy of the three groups brain activity time series. Our hypotheses were that pwMS with disability would have increased brain state transition energies compared to pwMS without disability and controls, and that greater energies would be related to greater lesion burden and decreased entropy of brain activity. Materials and MethodsNinety-nine pwMS and 19 HC were included in our study; individuals underwent MRI, including anatomical, diffusion and resting-state functional scans. Expanded Disability Status Scale (EDSS) was used to assess disability; 66 pwMS with EDSS<2 were considered as not having disability. Commonly recurring brain states were identified using k-means clustering of the regional brain activity time series. NCT was used to compute the energy required to transition between each pair of states or to remain in the same state, taking into account an individuals structural connectome. The entropy of brain activity time series was quantified and correlated with overall transition energy and lesion volume. Finally, logistic regression with ridge regularization was used to classify the groups using transition energies and demographics/clinical information and the models feature importance assessed. ResultsSix brain states were identified, including pairs of states with high and low visual (VIS+/-), ventral attention (VAN)+/- and somatomotor (SOM+/-) network activity. Global entropy was negatively correlated with lesion volume (r=-0.20, p=0.03) as was global transition energy (r=-0.18, p=0.04) in pwMS. Smaller transition energies were associated with pwMS without disability, while larger transition energies were associated with pwMS with disability. DiscussionWe conjecture that functional compensation of brain activity in pwMS without disability results in decreased transition energies between states compared to controls, but, as this mechanism diminishes, transition energies increase and disability occurs. We also conjecture that larger volumes of MS-related lesions result in greater energetic demands to transition between brain states which then leads to lower overall entropy of brain activity.

neuroscience↗

bioRxiv2021

Estimated connectivity networks outperform observed connectivity networks when classifying people with multiple sclerosis into disability groups

BackgroundMultiple Sclerosis (MS), a neurodegenerative and neuroinflammatory disease, causing lesions that disrupt the brains anatomical and physiological connectivity networks, resulting in cognitive, visual and/or motor disabilities. Advanced imaging techniques like diffusion and functional MRI allow measurement of the brains structural connectivity (SC) and functional connectivity (FC) networks, and can enable a better understanding of how their disruptions cause disability in people with MS (pwMS). However, advanced MRI techniques are used mainly for research purposes as they are expensive, time-consuming and require high-level expertise to acquire and process. As an alternative, the Network Modification (NeMo) Tool can be used to estimate SC and FC using lesion masks derived from pwMS and a reference set of controls connectivity networks. ObjectiveHere, we test the hypothesis that estimated SC and FC (eSC and eFC) from the NeMo Tool, based only on an individuals lesion masks, can be used to classify pwMS into disability categories just as well as SC and FC extracted from advanced MRI directly in pwMS. We also aim to find the connections most important for differentiating between no disability vs evidence of disability groups. Materials and MethodsOne hundred pwMS (age:45.5 {+/-} 11.4 years, 66% female, disease duration: 12.97 8.07 years) were included in this study. Expanded Disability Status Scale (EDSS) was used to assess disability, 67 pwMS had no disability (EDSS < 2). Observed SC and FC were extracted from diffusion and functional MRI directly in pwMS, respectively. The NeMo Tool was used to estimate the remaining structural connectome (eSC), by removing streamlines in a reference set of tractograms that intersected the lesion mask. The NeMo Tools eSC was used then as input to a deep neural network to estimate the corresponding FC (eFC). Logistic regression with ridge regularization was used to classify pwMS into disability categories (no disability vs evidence of disability), based on demographics/clinical information (sex, age, race, disease duration, clinical phenotype, and spinal lesion burden) and either pairwise entries or regional summaries from one of the following matrices: SC, FC, eSC, and eFC. The area under the ROC curve (AUC) was used to assess the classification performance. Both univariate statistics and parameter coefficients from the classification models were used to identify features important to differentiating between the groups. ResultsThe regional eSC and eFC models outperformed their observed FC and SC counterparts (p-value<0.05), while the pairwise eSC and SC performed similarly (p=0.10). Regional eSC and eFC models had higher AUC (0.66-0.68) than the pairwise models (0.60-0.65), with regional eFC having highest classification accuracy across all models. Ridge regression coefficients for the regional eFC and regional observed FC models were significantly correlated (Pearsons r = 0.52, p-value < 10e-7). Decreased estimated SC node strength in default mode and ventral attention networks and increased eFC node strength in visual networks was associated with evidence of disability. DiscussionHere, for the first time, we use clinically-acquired lesion masks to estimate both structural and functional connectomes in patient populations to better understand brain lesion-dysfunction mapping in pwMS. Models based on the NeMo Tools estimates of SC and FC better classified pwMS by disability level than SC and FC observed directly in the individual using advanced MRI. This work provides a viable alternative to performing high-cost, advanced MRI in patient populations, bringing the connectome one step closer to the clinic. HighlightsO_LIWe compared the accuracy of models based on observed functional connectivity (FC) and structural connectivity (SC) networks extracted from advanced MRI and estimated FC and SC networks derived using only lesion masks from conventional MRI in classifying people with multiple sclerosis (pwMS) into disability groups. C_LIO_LIEstimated SC and FC generally outperformed observed SC and FC in classifying pwMS into no disability vs evidence of disability groups, with regional estimated SC and FC having the best performance. C_LIO_LIIncreased estimated FC node strength of regions in the visual network was associated with disability. C_LIO_LIDecreased estimated SC node strength of regions in the default mode and ventral attention networks was associated with disability. C_LIO_LIDespite their varied sources of origin, feature weights for the regional estimated FC and the regional observed FC classification models was significantly correlated (Pearsons r = 0.52, p-value < 10e-7). C_LI

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bioRxiv2019

Modeling individual disability evolution in multiple sclerosis patients based on longitudinal multimodal imaging and clinical data

BackgroundThe individual disease evolution of multiple sclerosis (MS) is very different from one patient to another. Therefore, the prediction of long-term disability evolution is difficult based on only clinical information. Magnetic resonance imaging (MRI) provides a very efficient tool to distinguish between healthy and abnormal brain tissue, monitor disease evolution, and help decision-making for personalized treatment of MS patients.\n\nObjectiveWe aim to develop a patient-specific model to predict individual disease evolution in MS, using demographic, clinical, and imaging data that were collected at study onset.\n\nMethodsThe study included 75 patients tracked over 5 years. The latent class linear mixed model was used to consider individual and unobserved subgroup variability. First, the clinical model was established with demographic and clinical variables to predict clinical disease evolution. Second, the imaging model was built using the multimodal imaging variables. Third, the imaging variables were added one by one, two by two, and all three together to investigate their contribution to the clinical model. The clinical disability is measured with the Expanded Disability Status Scale (EDSS). The performances of the clinical, imaging, and the combined models were compared mainly using the Bayesian Information Criterion (BIC). The mean of the posterior probabilities was also given as the secondary performance evaluation criterion.\n\nResultsThe clinical model gave higher BIC value than imaging and any combined models. The means of the posterior probabilities given by the three models were over 0.94. The clinical model clustered the patients into two latent classes: stable evolution class (n=6, 88%) and severe evolution class (n=9, 12%).\n\nConclusionThe latent class linear mixed model may provide a well-fitted prediction for the disability evolution in MS patients, thus giving further information for personalized treatment decisions after thorough validation with a larger and independent dataset.

neuroscience↗

bioRxiv2019

Comparison of classification methods for tissue outcome after ischemic stroke

In acute ischemic stroke, identifying brain tissue at high risk of infarction is important for clinical decision-making. This tissue may be identified with suitable classification methods from magnetic resonance imaging (MRI) data. The aim of the present study was to assess comparatively the performance of five popular classification methods (Adaptive Boosting (ADA), Logistic Regression (LR), Artificial Neural Networks (ANN), Random Forest (RF), and Support Vector Machine (SVM)) in identifying tissue at high risk of infarction on human voxel-based brain imaging data. The classification methods were used with eight MRI parameters including diffusion-weighted imaging (DWI) and perfusion-weighted imaging (PWI) obtained in 55 patients. Sensitivity, specificity, the area under the receiver operating curve (ROC) as well as the area under the precision-recall curve criteria were used to compare the method performances. The methods performed equally in terms of sensitivity and specificity while the results of the area under the ROC were significantly better for ADA, LR, ANN and RF. However, there was no statistically significant difference between the performances of these five classification methods regarding the area under the precision-recall curve, which was the main comparison metric.

neuroscience↗

bioRxiv2018

Predicting response to motor therapy in chronic stroke patients using Machine Learning

Accurate predictions of motor improvement resulting from intensive therapy in chronic stroke patients is a difficult task for clinicians, but is key in prescribing appropriate therapeutic strategies. Statistical methods, including machine learning, are a highly promising avenue with which to improve prediction accuracy in clinical practice. The first main objective of this study was to use machine learning methods to predict a chronic stroke individuals motor function improvement after 6 weeks of intervention using pre-intervention demographic, clinical, neurophysiological and imaging data. The second main objective was to identify which data elements were most important in predicting chronic stroke patients impairment after 6 weeks of intervention. Data from one hundred and two patients (Female: 31%, age 61{+/-}11 years) who suffered first ischemic stroke 3-12 months prior were included in this study. After enrollment, patients underwent 6 weeks of intensive motor and transcranial magnetic stimulation therapy. Age, gender, handedness, time since stroke, pre-intervention Fugl-Meyer Assessment, stroke lateralization, the difference in motor threshold between the unaffected and affected hemispheres, absence or presence of motor evoked potential in the affected hemisphere and various imaging metrics were used as predictors of post-intervention Fugl-Meyer Assessment. Five machine learning methods, including Elastic-Net, Support Vector Machines, Artificial Neural Networks, Classification and Regression Trees, and Random Forest, were used to predict post-intervention Fugl-Meyer Assessment based on either demographic, clinical and neurophysiological data alone or in combination with the imaging metrics. Cross-validated R-squared and root of mean squared error were used to assess the prediction accuracy and compare the performance of methods. Elastic-Net performed significantly better than the other methods for the model containing pre-intervention Fugl-Meyer Assessment, demographic, clinical and neurophysiological data as predictors of post-intervention Fugl-Meyer Assessment ([Formula]). Pre-intervention Fugl-Meyer Assessment and difference in motor threshold between affected and unaffected hemispheres were commonly found as the strongest two predictors in the clinical model. The difference in motor threshold had greater importance than the absence or presence of motor evoked potential in the affected hemisphere. The various imaging metrics, including lesion overlap with the spinal cord, largely did not improve the model performance. The approach implemented here may enable clinicians to more accurately predict a chronic stroke patients individual response to intervention. The predictive models used in this study could assist clinicians in making treatment decisions and improve the accuracy of prognosis in chronic stroke patients.

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