IU Indianapolis ScholarWorks :: Browsing by Author "Goñi, Joaquín"

Browsing by Author "Goñi, Joaquín"

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A comparison of techniques for deriving clustering and switching scores from verbal fluency word lists
(Frontiers Media, 2022-09-14) Bushnell, Justin; Svaldi, Diana; Ayers, Matthew R.; Gao, Sujuan; Unverzagt, Frederick; Del Gaizo, John; Wadley, Virginia G.; Kennedy, Richard; Goñi, Joaquín; Clark, David Glenn; Neurology, School of Medicine
Objective: To compare techniques for computing clustering and switching scores in terms of agreement, correlation, and empirical value as predictors of incident cognitive impairment (ICI). Methods: We transcribed animal and letter F fluency recordings on 640 cases of ICI and matched controls from a national epidemiological study, amending each transcription with word timings. We then calculated clustering and switching scores, as well as scores indexing speed of responses, using techniques described in the literature. We evaluated agreement among the techniques with Cohen's κ and calculated correlations among the scores. After fitting a base model with raw scores, repetitions, and intrusions, we fit a series of Bayesian logistic regression models adding either clustering and switching scores or speed scores, comparing the models in terms of several metrics. We partitioned the ICI cases into acute and progressive cases and repeated the regression analysis for each group. Results: For animal fluency, we found that models with speed scores derived using the slope difference algorithm achieved the best values of the Watanabe-Akaike Information Criterion (WAIC), but with good net reclassification improvement (NRI) only for the progressive group (8.2%). For letter fluency, different models excelled for prediction of acute and progressive cases. For acute cases, NRI was best for speed scores derived from a network model (3.4%), while for progressive cases, the best model used clustering and switching scores derived from the same network model (5.1%). Combining variables from the best animal and letter F models led to marginal improvements in model fit and NRI only for the all-cases and acute-cases analyses. Conclusion: Speed scores improve a base model for predicting progressive cognitive impairment from animal fluency. Letter fluency scores may provide complementary information.
Aberrations of anterior insular cortex functional connectivity in nontreatment-seeking alcoholics
(Elsevier, 2019-02) Halcomb, Meredith E.; Chumin, Evgeny J.; Goñi, Joaquín; Dzemidzic, Mario; Yoder, Karmen K.; Radiology and Imaging Sciences, School of Medicine
An emergent literature suggests that resting state functional magnetic resonance imaging (rsfMRI) functional connectivity (FC) patterns are aberrant in alcohol use disorder (AUD) populations. The salience network (SAL) is an established set of brain regions prominent in salience attribution and valuation, and includes the anterior insular cortex (AIC). The SAL is thought to play a role in AUD through directing increased attention to interoceptive cues of intoxication. There is very little information on the salience network (SAL) in AUD, and, in particular, there are no data on SAL FC in currently drinking, nontreatment seeking individuals with AUD (NTS). rsfMRI data from 16 NTS and 21 social drinkers (SD) were compared using FC correlation maps from ten seed regions of interest in the bilateral AIC. As anticipated, SD subjects demonstrated greater insular FC with frontal and parietal regions. We also found that, compared to SD, NTS had higher insular FC with hippocampal and medial orbitofrontal regions. The apparent overactivity in brain networks involved in salience, learning, and behavioral control in NTS suggests possible mechanisms in the development and maintenance of AUD.
Brain Connectivity-Informed Regularization Methods for Regression
(Springer, 2017-12-06) Karas, Marta; Brzyski, Damian; Dzemidzic, Mario; Goñi, Joaquín; Kareken, David A.; Randolph, Timothy W.; Harezlak, Jaroslaw; Neurology, School of Medicine
One of the challenging problems in brain imaging research is a principled incorporation of information from different imaging modalities. Frequently, each modality is analyzed separately using, for instance, dimensionality reduction techniques, which result in a loss of mutual information. We propose a novel regularization method to estimate the association between the brain structure features and a scalar outcome within the linear regression framework. Our regularization technique provides a principled approach to use external information from the structural brain connectivity and inform the estimation of the regression coefficients. Our proposal extends the classical Tikhonov regularization framework by defining a penalty term based on the structural connectivity-derived Laplacian matrix. Here, we address both theoretical and computational issues. The approach is first illustrated using simulated data and compared with other penalized regression methods. We then apply our regularization method to study the associations between the alcoholism phenotypes and brain cortical thickness using a diffusion imaging derived measure of structural connectivity. Using the proposed methodology in 148 young male subjects with a risk for alcoholism, we found a negative associations between cortical thickness and drinks per drinking day in bilateral caudal anterior cingulate cortex, left lateral OFC, and left precentral gyrus.
Brain explorer for connectomic analysis
(Springer, 2017-08-23) Li, Huang; Fang, Shiaofen; Contreras, Joey A.; West, John D.; Risacher, Shannon L.; Wang, Yang; Sporns, Olaf; Saykin, Andrew J.; Goñi, Joaquín; Shen, Li; Radiology and Imaging Sciences, School of Medicine
Visualization plays a vital role in the analysis of multimodal neuroimaging data. A major challenge in neuroimaging visualization is how to integrate structural, functional, and connectivity data to form a comprehensive visual context for data exploration, quality control, and hypothesis discovery. We develop a new integrated visualization solution for brain imaging data by combining scientific and information visualization techniques within the context of the same anatomical structure. In this paper, new surface texture techniques are developed to map non-spatial attributes onto both 3D brain surfaces and a planar volume map which is generated by the proposed volume rendering technique, spherical volume rendering. Two types of non-spatial information are represented: (1) time series data from resting-state functional MRI measuring brain activation; (2) network properties derived from structural connectivity data for different groups of subjects, which may help guide the detection of differentiation features. Through visual exploration, this integrated solution can help identify brain regions with highly correlated functional activations as well as their activation patterns. Visual detection of differentiation features can also potentially discover image-based phenotypic biomarkers for brain diseases.
Characterizing neurodegeneration in the human connectome: a network science study of hereditary diffuse leukoencephalopathy with spheroids
(Office of the Vice Chancellor for Research, 2015-04-17) Contreras, Joey; Rishacher, Shannon L.; West, John D.; Wu, Yu-Chien; Wang, Yang; Murrell, Jill R.; Dzemidzic, Mario; Farlow, Martin R.; Unverzagt, Frederik; Ghetti, Bernardino; Matthews, Brandy R.; Quaid, Kimberly A.; Sporns, Olaf; Saykin, Andrew J.; Goñi, Joaquín
Abstract The effect of white matter neurodegeneration on the human connectome and its functional implications is an important topic with clinical applicability of advanced brain network analysis. The aim of this study was to evaluate integration and segregation changes in structural connectivity (SC) that arise as consequence of white matter lesions in hereditary diffuse leukoencephalopathy with spheroids (HDLS). Also, we assessed the relationship between HDLS induced structural changes and changes in restingstate functional connectivity (rsFC). HDLS is a rare autosomal dominant neurodegenerative disorder caused by mutations in the CSF1R gene. HDLS is characterized by severe white matter damage leading to prominent subcortical lesions detectable by structural MRI. Spheroids, an important feature of HDLS, are axonal swellings indicating damage. HDLS causes progressive motor and cognitive decline. The clinical symptoms of HDLS are often mistaken for other diseases such as Alzheimer’s disease, frontotemporal dementia, atypical Parkinsonism or multiple sclerosis. Our study is focused on the follow-up of two siblings, one being a healthy control (HC) and the other one being an HDLS patient. In this study, deterministic fiber-tractography of diffusion MRI with multi-tensor modeling was used in order to obtain reliable and reproducible SC matrices. Integration changes were measured by means of SC shortest-paths (including distance and number of edges), whereas segregation and community organization were measured by means of a multiplex modularity analysis on the SC matrices. Additionally, rsFC was modeled using state of the art preprocessing methods including motion regressors and scrubbing. This allowed us to characterize functional changes associated to the disease. Major integration disruption involved superior frontal (L,R), caudal middle frontal (R), precentral (L,R), inferior parietal (R), insula (R) and paracentral (L) regions. Major segregation changes were characterized by the disruption of a large bilateral module that was observed in the HC that includes the frontal pole (L,R), medial orbitofrontal (L,R), rostral middle frontal (L), superior frontal (L,R), precentral (L,R), paracentral (L,R), rostral anterior cingulate (L,R), caudal anterior cingulate (L,R), posterior cingulate (L,R), postcentral (L), precuneus (L,R), lateral orbitofrontal (R) and parsorbitalis (R). The combination of tractography and network analysis permitted the detection and characterization of profound cortical to cortical changes in integration and segregation associated with HDLS white matter lesions and its relationship with rsFC. Our preliminary findings suggest that advanced network analytic approaches show promising sensitivity to known white matter pathology and progression. Further Indiana Alzheimer Disease Center Symposium. March 6, 2015. research is needed to address the specificity of network profiles for differentiation among white matter pathologies and diseases.
Connectivity‐informed adaptive regularization for generalized outcomes
(Wiley, 2021-02) Brzyski, Damian; Karas, Marta; Ances, Beau M.; Dzemidzic, Mario; Goñi, Joaquín; Randolph, Timothy W.; Harezlak, Jaroslaw; Neurology, School of Medicine
One of the challenging problems in neuroimaging is the principled incorporation of information from different imaging modalities. Data from each modality are frequently analyzed separately using, for instance, dimensionality reduction techniques, which result in a loss of mutual information. We propose a novel regularization method, generalized ridgified Partially Empirical Eigenvectors for Regression (griPEER), to estimate associations between the brain structure features and a scalar outcome within the generalized linear regression framework. griPEER improves the regression coefficient estimation by providing a principled approach to use external information from the structural brain connectivity. Specifically, we incorporate a penalty term, derived from the structural connectivity Laplacian matrix, in the penalized generalized linear regression. In this work, we address both theoretical and computational issues and demonstrate the robustness of our method despite incomplete information about the structural brain connectivity. In addition, we also provide a significance testing procedure for performing inference on the estimated coefficients. Finally, griPEER is evaluated both in extensive simulation studies and using clinical data to classify HIV+ and HIV− individuals.
Cortical Connectivity in Alcoholism
(2019-09) Chumin, Evgeny Jenya; Kareken, David A.; Dzemidzic, Mario; Goñi, Joaquín; Harezlak, Jaroslaw; Lapish, Christopher C.; Yoder, Karmen K.
Alcoholism carries significant personal and societal burdens, and yet we still lack effective treatments for alcohol use disorders. Several lines of research have demonstrated disruption of major white matter (WM) tracts in the brains of detoxified alcoholics. Additionally, there are several reports of alterations in the dopaminergic system of alcoholics. A better understanding of the relationships of brain structure and function in the alcoholic brain is necessary to move toward more efficacious pharmacological interventions. In this dissertation, there are three main chapters. First, reduced WM integrity was reported in a sample of individuals with active alcohol use disorder (AUD). This is a relatively understudied population, which is believed to represent a less severe phenotype compared to the in-treatment samples that are typically studied. Second, higher WM integrity was reported in a sample of college-age, active AUD. In a subsample of these individuals, graph theory measures of structural brain network connectivity were shown to be altered in cigarette-smoking social-drinking controls and smoking AUD subjects, compared to nonsmoking healthy individuals. Finally, a novel multimodal approach that combines diffusion weighted imaging and [11C]raclopride positron emission tomography identified differential relationships between frontostriatal connectivity and striatal dopamine tone in active AUD versus social-drinking controls. This suggests that aberrations in frontostriatal connectivity may contribute to reported differences in dopaminergic function in AUD. In summary, these results show that similar to detoxified/in-treatment alcoholics, active AUD samples present with WM integrity alterations, and changes in both structural connectivity and frontostriatal structure/function relationships.
Differences in White Matter Microstructure and Connectivity in Nontreatment‐Seeking Individuals with Alcohol Use Disorder
(Wiley, 2018-05) Chumin, Evgeny J.; Goñi, Joaquín; Halcomb, Meredith E.; Durazzo, Timothy C.; Džemidžić, Mario; Yoder, Karmen K.; Radiology and Imaging Sciences, School of Medicine
Background Diffusion‐weighted imaging (DWI) has been widely used to investigate the integrity of white matter (WM; indexed by fractional anisotropy [FA]) in alcohol dependence and cigarette smoking. These disorders are highly comorbid, yet cigarette use has often not been adequately controlled in neuroimaging studies of alcohol‐dependent populations. In addition, information on WM deficits in currently drinking, nontreatment‐seeking (NTS) individuals with alcohol dependence is limited. Therefore, the aim of this work was to investigate WM microstructural integrity in alcohol use disorder by comparing matched samples of cigarette smoking NTS and social drinkers (SD). Methods Thirty‐eight smoking NTS and 19 smoking SD subjects underwent DWI as well as structural magnetic resonance imaging. After an in‐house preprocessing of the DWI data, FA images were analyzed with tract‐based spatial statistics (TBSS). FA obtained from the TBSS skeleton was tested for correlation with recent alcohol consumption. Results Smoking NTS had lower FA relative to smoking SD, predominantly in the left hemisphere (p < 0.05, family‐wise error rate corrected across FA skeleton). Across the full sample, FA and number of drinks per week were negatively related (ρ = −0.348, p = 0.008). Qualitative analyses of the structural connections through compromised WM as identified by TBSS showed differential connectivity of gray matter in NTS compared to SD subjects of left frontal, temporal, and parietal regions. Conclusions NTS subjects had lower WM FA than SD, indicating compromised WM integrity in the NTS population. The inverse relationship of entire WM skeleton FA with self‐reported alcohol consumption supports previous evidence of a continuum of detrimental effects of alcohol consumption on WM. These results provide additional evidence that alcohol dependence is associated with reduced WM integrity in currently drinking NTS alcohol‐dependent individuals, after controlling for the key variable of cigarette smoking.
The disengaging brain: Dynamic transitions from cognitive engagement and alcoholism risk
(Elsevier, 2020-04) Amico, Enrico; Dzemidzic, Mario; Oberlin, Brandon G.; Carron, Claire R.; Harezlak, Jaroslaw; Goñi, Joaquín; Kareken, David A.; Neurology, School of Medicine
Human functional brain connectivity is usually measured either at “rest” or during cognitive tasks, ignoring life’s moments of mental transition. We propose a different approach to understanding brain network transitions. We applied a novel independent component analysis of functional connectivity during motor inhibition (stop signal task) and during the continuous transition to an immediately ensuing rest. A functional network reconfiguration process emerged that: (i) was most prominent in those without familial alcoholism risk, (ii) encompassed brain areas engaged by the task, yet (iii) appeared only transiently after task cessation. The pattern was not present in a pre-task rest scan or in the remaining minutes of post-task rest. Finally, this transient network reconfiguration related to a key behavioral trait of addiction risk: reward delay discounting. These novel findings illustrate how dynamic brain functional reconfiguration during normally unstudied periods of cognitive transition might reflect addiction vulnerability, and potentially other forms of brain dysfunction.
Functional network connectivity in early-stage schizophrenia
(Elsevier, 2020-04) Hummer, Tom A.; Yung, Matthew G.; Goñi, Joaquín; Conroy, Susan K.; Francis, Michael M.; Mehdiyoun, Nicole F.; Breier, M. A. Alan; Psychiatry, School of Medicine
Schizophrenia is a disorder of altered neural connections resulting in impaired information integration. Whole brain assessment of within- and between-network connections may determine how information processing is disrupted in schizophrenia. Patients with early-stage schizophrenia (n = 56) and a matched control sample (n = 32) underwent resting-state fMRI scans. Gray matter regions were organized into nine distinct functional networks. Functional connectivity was calculated between 278 gray matter regions for each subject. Network connectivity properties were defined by the mean and variance of correlations of all regions. Whole-brain network measures of global efficiency (reflecting overall interconnectedness) and locations of hubs (key regions for communication) were also determined. The control sample had greater connectivity between the following network pairs: somatomotor-limbic, somatomotor-default mode, dorsal attention-default mode, ventral attention-limbic, and ventral attention-default mode. The patient sample had greater variance in interactions between ventral attention network and other functional networks. Illness duration was associated with overall increases in the variability of network connections. The control group had higher global efficiency and more hubs in the cerebellum network, while patient group hubs were more common in visual, frontoparietal, or subcortical networks. Thus, reduced functional connectivity in patients was largely present between distinct networks, rather than within-networks. The implications of these findings for the pathophysiology of schizophrenia are discussed.

Browsing by Author "Goñi, Joaquín"

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