Author name cluster

Joaquín Goñi

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

13 papers

1 author row

YNICL Journal 2026 Journal Article

Resting state functional connectivity patterns associate with alcohol use disorder characteristics: Insights from the triple network model

Daniel Guerrero
Mario Dzemidzic
Mahdi Moghaddam
Mintao Liu
Andrea Avena-Koenigsberger
Jaroslaw Harezlak
David A. Kareken
Martin H. Plawecki

Prolonged alcohol use results in neuroadaptations that mark more severe and treatment-resistant alcohol use. The goal of this study was to identify functional connectivity brain patterns underlying Alcohol Use Disorder (AUD)-related characteristics in fifty-five adults (31 female) who endorsed heavy alcohol use. We hypothesized that resting-state functional connectivity (rsFC) of the Salience (SN), Frontoparietal (FPN), and Default Mode (DMN) networks would reflect self-reported recent and lifetime alcohol use, laboratory-based alcohol seeking, urgency, and sociodemographic characteristics related to AUD. To test our hypothesis, we combined the triple network model (TNM) of psychopathology with a multivariate data-driven approach, regularized partial least squares (rPLS), to unfold concurrent functional connectivity (FC) patterns and their association with AUD-related characteristics. We observed three concurrent associations of interest: i) drinking and age-related cross communication between the SN and both the FPN and DMN; ii) family history density of AUD and urgency anticorrelations between the SN and FPN; and iii) alcohol seeking and sex-associated SN and DMN interactions. These findings provide an integrative interpretation for many individual findings reported in the literature relating functional connectivity signatures and AUD factors. Moreover, we identified a set of neural mechanisms and brain regions concomitant with AUD-related characteristics that can serve as potential treatment targets across clinical and preclinical models.

Details DOI

YNICL Journal 2023 Journal Article

Intra and inter-individual variability in functional connectomes of patients with First Episode of Psychosis

Ángeles Tepper
Javiera Vásquez Núñez
Juan Pablo Ramirez-Mahaluf
Juan Manuel Aguirre
Daniella Barbagelata
Elisa Maldonado
Camila Díaz Dellarossa
Ruben Nachar

Patients with Schizophrenia may show different clinical presentations, not only regarding inter-individual comparisons but also in one specific subject over time. In fMRI studies, functional connectomes have been shown to carry valuable individual level information, which can be associated with cognitive and behavioral variables. Moreover, functional connectomes have been used to identify subjects within a group, as if they were fingerprints. For the particular case of Schizophrenia, it has been shown that there is reduced connectome stability as well as higher inter-individual variability. Here, we studied inter and intra-individual heterogeneity by exploring functional connectomes' variability and related it with clinical variables (PANSS Total scores and antipsychotic's doses). Our sample consisted of 30 patients with First Episode of Psychosis and 32 Healthy Controls, with a test-retest approach of two resting-state fMRI scanning sessions. In our patients' group, we found increased deviation from healthy functional connectomes and increased intragroup inter-subject variability, which was positively correlated to symptoms' levels in six subnetworks (visual, somatomotor, dorsal attention, ventral attention, frontoparietal and DMN). Moreover, changes in symptom severity were positively related to changes in deviation from healthy functional connectomes. Regarding intra-subject variability, we were unable to replicate previous findings of reduced connectome stability (i.e., increased intra-subject variability), but we found a trend suggesting that result. Our findings highlight the relevance of variability characterization in Schizophrenia, and they can be related to evidence of Schizophrenia patients having a noisy functional connectome.

Details DOI

YNIMG Journal 2020 Journal Article

GEFF: Graph embedding for functional fingerprinting

Kausar Abbas
Enrico Amico
Diana Otero Svaldi
Uttara Tipnis
Duy Anh Duong-Tran
Mintao Liu
Meenusree Rajapandian
Jaroslaw Harezlak

It has been well established that Functional Connectomes (FCs), as estimated from functional MRI (fMRI) data, have an individual fingerprint that can be used to identify an individual from a population (subject-identification). Although identification rate is high when using resting-state FCs, other tasks show moderate to low values. Furthermore, identification rate is task-dependent, and is low when distinct cognitive states, as captured by different fMRI tasks, are compared. Here we propose an embedding framework, GEFF (Graph Embedding for Functional Fingerprinting), based on group-level decomposition of FCs into eigenvectors. GEFF creates an eigenspace representation of a group of subjects using one or more task FCs (Learning Stage). In the Identification Stage, we compare new instances of FCs from the Learning subjects within this eigenspace (validation dataset). The validation dataset contains FCs either from the same tasks as the Learning dataset or from the remaining tasks that were not included in Learning. Assessment of validation FCs within the eigenspace results in significantly increased subject-identification rates for all fMRI tasks tested and potentially task-independent fingerprinting process. It is noteworthy that combining resting-state with one fMRI task for GEFF Learning Stage covers most of the cognitive space for subject identification. Thus, while designing an experiment, one could choose a task fMRI to ask a specific question and combine it with resting-state fMRI to extract maximum subject differentiability using GEFF. In addition to subject-identification, GEFF was also used for identification of cognitive states, i. e. to identify the task associated to a given FC, regardless of the subject being already in the Learning dataset or not (subject-independent task-identification). In addition, we also show that eigenvectors from the Learning Stage can be characterized as task- and subject-dominant, subject-dominant or neither, using two-way ANOVA of their corresponding loadings, providing a deeper insight into the extent of variance in functional connectivity across individuals and cognitive states.

Details DOI

YNIMG Journal 2020 Journal Article

The disengaging brain: Dynamic transitions from cognitive engagement and alcoholism risk

Enrico Amico
Mario Dzemidzic
Brandon G. Oberlin
Claire R. Carron
Jaroslaw Harezlak
Joaquín Goñi
David A. Kareken

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.

Details DOI

YNICL Journal 2019 Journal Article

Resting state network modularity along the prodromal late onset Alzheimer's disease continuum

Joey A. Contreras
Andrea Avena-Koenigsberger
Shannon L. Risacher
John D. West
Eileen Tallman
Brenna C. McDonald
Martin R. Farlow
Liana G. Apostolova

Alzheimer's disease is considered a disconnection syndrome, motivating the use of brain network measures to detect changes in whole-brain resting state functional connectivity (FC). We investigated changes in FC within and among resting state networks (RSN) across four different stages in the Alzheimer's disease continuum. FC changes were examined in two independent cohorts of individuals (84 and 58 individuals, respectively) each comprising control, subjective cognitive decline, mild cognitive impairment and Alzheimer's dementia groups. For each participant, FC was computed as a matrix of Pearson correlations between pairs of time series from 278 gray matter brain regions. We determined significant differences in FC modular organization with two distinct approaches, network contingency analysis and multiresolution consensus clustering. Network contingency analysis identified RSN sub-blocks that differed significantly across clinical groups. Multiresolution consensus clustering identified differences in the stability of modules across multiple spatial scales. Significant modules were further tested for statistical association with memory and executive function cognitive domain scores. Across both analytic approaches and in both participant cohorts, the findings converged on a pattern of FC that varied systematically with diagnosis within the frontoparietal network (FP) and between the FP network and default mode network (DMN). Disturbances of modular organization were manifest as greater internal coherence of the FP network and stronger coupling between FP and DMN, resulting in less segregation of these two networks. Our findings suggest that the pattern of interactions within and between specific RSNs offers new insight into the functional disruption that occurs across the Alzheimer's disease spectrum.

Details DOI

YNIMG Journal 2019 Journal Article

Uncovering multi-site identifiability based on resting-state functional connectomes

Sumra Bari
Enrico Amico
Nicole Vike
Thomas M. Talavage
Joaquín Goñi

Multi-site studies are becoming important to increase statistical power, enhance generalizability, and to improve the likelihood of pooling relevant subgroups together—activities which are otherwise limited by the availability of subjects or funds at a single site. Even with harmonized imaging sequences, site-dependent variability can mask the advantages of these multi-site studies. The aim of this study was to assess multi-site reproducibility in resting-state functional connectivity “fingerprints”, and to improve identifiability of functional connectomes. The individual fingerprinting of functional connectivity profiles is promising due to its potential as a robust neuroimaging biomarker with which to draw single-subject inferences. We evaluated, on two independent multi-site datasets, individual fingerprints in test-retest visit pairs within and across two sites and present a generalized framework based on principal component analysis to improve identifiability. Those principal components that maximized differential identifiability of a training dataset were used as an orthogonal connectivity basis to reconstruct the individual functional connectomes of training and validation sets. The optimally reconstructed functional connectomes showed a substantial improvement in individual fingerprinting of the subjects within and across the two sites and test-retest visit pairs relative to the original data. A notable increase in ICC values for functional edges and resting-state networks were also observed for reconstructed functional connectomes. Improvements in identifiability were not found to be affected by global signal regression. Post-hoc analyses assessed the effect of the number of fMRI volumes on identifiability and showed that multi-site differential identifiability was for all cases maximized after optimal reconstruction. Finally, the generalizability of the optimal set of orthogonal basis of each dataset was evaluated through a leave-one-out procedure. Overall, results demonstrate that the data-driven framework presented in this study systematically improves identifiability in resting-state functional connectomes in multi-site studies.

Details DOI

YNIMG Journal 2017 Journal Article

Mapping the functional connectome traits of levels of consciousness

Enrico Amico
Daniele Marinazzo
Carol Di Perri
Lizette Heine
Jitka Annen
Charlotte Martial
Mario Dzemidzic
Murielle Kirsch

Examining task-free functional connectivity (FC) in the human brain offers insights on how spontaneous integration and segregation of information relate to human cognition, and how this organization may be altered in different conditions, and neurological disorders. This is particularly relevant for patients in disorders of consciousness (DOC) following severe acquired brain damage and coma, one of the most devastating conditions in modern medical care. We present a novel data-driven methodology, connICA, which implements Independent Component Analysis (ICA) for the extraction of robust independent FC patterns (FC-traits) from a set of individual functional connectomes, without imposing any a priori data stratification into groups. We here apply connICA to investigate associations between network traits derived from task-free FC and cognitive/clinical features that define levels of consciousness. Three main independent FC-traits were identified and linked to consciousness-related clinical features. The first one represents the functional configuration of a “resting” human brain, and it is associated to a sedative (sevoflurane), the overall effect of the pathology and the level of arousal. The second FC-trait reflects the disconnection of the visual and sensory-motor connectivity patterns. It also relates to the time since the insult and to the ability of communicating with the external environment. The third FC-trait isolates the connectivity pattern encompassing the fronto-parietal and the default-mode network areas as well as the interaction between left and right hemispheres, which are also associated to the awareness of the self and its surroundings. Each FC-trait represents a distinct functional process with a role in the degradation of conscious states of functional brain networks, shedding further light on the functional sub-circuits that get disrupted in severe brain-damage.

Details DOI

YNIMG Journal 2016 Journal Article

Generative models of the human connectome

Richard F. Betzel
Andrea Avena-Koenigsberger
Joaquín Goñi
Ye He
Marcel A. de Reus
Alessandra Griffa
Petra E. Vértes
Bratislav Mišic

The human connectome represents a network map of the brain's wiring diagram and the pattern into which its connections are organized is thought to play an important role in cognitive function. The generative rules that shape the topology of the human connectome remain incompletely understood. Earlier work in model organisms has suggested that wiring rules based on geometric relationships (distance) can account for many but likely not all topological features. Here we systematically explore a family of generative models of the human connectome that yield synthetic networks designed according to different wiring rules combining geometric and a broad range of topological factors. We find that a combination of geometric constraints with a homophilic attachment mechanism can create synthetic networks that closely match many topological characteristics of individual human connectomes, including features that were not included in the optimization of the generative model itself. We use these models to investigate a lifespan dataset and show that, with age, the model parameters undergo progressive changes, suggesting a rebalancing of the generative factors underlying the connectome across the lifespan.

Details DOI

YNIMG Journal 2014 Journal Article

Changes in structural and functional connectivity among resting-state networks across the human lifespan

Richard F. Betzel
Lisa Byrge
Ye He
Joaquín Goñi
Xi-Nian Zuo
Olaf Sporns

At rest, the brain's sensorimotor and higher cognitive systems engage in organized patterns of correlated activity forming resting-state networks. An important empirical question is how functional connectivity and structural connectivity within and between resting-state networks change with age. In this study we use network modeling techniques to identify significant changes in network organization across the human lifespan. The results of this study demonstrate that whole-brain functional and structural connectivity both exhibit reorganization with age. On average, functional connections within resting-state networks weaken in magnitude while connections between resting-state networks tend to increase. These changes can be localized to a small subset of functional connections that exhibit systematic changes across the lifespan. Collectively, changes in functional connectivity are also manifest at a system-wide level, as components of the control, default mode, saliency/ventral attention, dorsal attention, and visual networks become less functionally cohesive, as evidenced by decreased component modularity. Paralleling this functional reorganization is a decrease in the density and weight of anatomical white-matter connections. Hub regions are particularly affected by these changes, and the capacity of those regions to communicate with other regions exhibits a lifelong pattern of decline. Finally, the relationship between functional connectivity and structural connectivity also appears to change with age; functional connectivity along multi-step structural paths tends to be stronger in older subjects than in younger subjects. Overall, our analysis points to age-related changes in inter-regional communication unfolding within and between resting-state networks.

Details DOI

YNIMG Journal 2013 Journal Article

Robust estimation of fractal measures for characterizing the structural complexity of the human brain: Optimization and reproducibility

Joaquín Goñi
Olaf Sporns
Hu Cheng
Maite Aznárez-Sanado
Yang Wang
Santiago Josa
Gonzalo Arrondo
Vincent P. Mathews

High-resolution isotropic three-dimensional reconstructions of human brain gray and white matter structures can be characterized to quantify aspects of their shape, volume and topological complexity. In particular, methods based on fractal analysis have been applied in neuroimaging studies to quantify the structural complexity of the brain in both healthy and impaired conditions. The usefulness of such measures for characterizing individual differences in brain structure critically depends on their within-subject reproducibility in order to allow the robust detection of between-subject differences. This study analyzes key analytic parameters of three fractal-based methods that rely on the box-counting algorithm with the aim to maximize within-subject reproducibility of the fractal characterizations of different brain objects, including the pial surface, the cortical ribbon volume, the white matter volume and the gray matter/white matter boundary. Two separate datasets originating from different imaging centers were analyzed, comprising 50 subjects with three and 24 subjects with four successive scanning sessions per subject, respectively. The reproducibility of fractal measures was statistically assessed by computing their intra-class correlations. Results reveal differences between different fractal estimators and allow the identification of several parameters that are critical for high reproducibility. Highest reproducibility with intra-class correlations in the range of 0. 9–0. 95 is achieved with the correlation dimension. Further analyses of the fractal dimensions of parcellated cortical and subcortical gray matter regions suggest robustly estimated and region-specific patterns of individual variability. These results are valuable for defining appropriate parameter configurations when studying changes in fractal descriptors of human brain structure, for instance in studies of neurological diseases that do not allow repeated measurements or for disease-course longitudinal studies.

Details DOI

YNIMG Journal 2009 Journal Article

Brain pathways of verbal working memory

Jorge Sepulcre
Joseph C. Masdeu
Maria A. Pastor
Joaquín Goñi
Carla Barbosa
Bartolomé Bejarano
Pablo Villoslada

Working memory relies on information processing by several well-identified gray matter regions. However, the white matter regions and pathways involved in this cognitive process remain unknown. An attractive and underexplored approach to study white matter connectivity in cognitive functions is through the use of non-aprioristic models, which specifically search disrupted white matter pathways. For this purpose, we used voxel-based lesion–function mapping to correlate white matter lesions on the magnetic resonance images of 54 multiple sclerosis patients with their performance on a verbal working memory task. With this approach, we have identified critical white matter regions involved in verbal working memory in humans. They are located in the cingulum, parieto-frontal pathways and thalamo-cortical projections, with a left-sided predominance, as well as the right cerebellar white matter. Our study provides direct evidence on the white matter pathways subserving verbal working memory in the human brain.

Details DOI

YNIMG Journal 2008 Journal Article

Mapping the brain pathways of declarative verbal memory: Evidence from white matter lesions in the living human brain

Jorge Sepulcre
Joseph C. Masdeu
Jaume Sastre-Garriga
Joaquín Goñi
Nieves Vélez-de-Mendizábal
Beatriz Duque
Maria A. Pastor
Bartolomé Bejarano

Understanding the contribution of the brain white matter pathways to declarative verbal memory processes has been hindered by the lack of an adequate model in humans. An attractive and underexplored approach to study white matter region functionality in the living human brain is through the use of non-aprioristic models which specifically search disrupted white matter pathways. For this purpose, we employed voxel-based lesion–function mapping to correlate white matter lesions on the magnetic resonance images of 46 multiple sclerosis patients with their performance on declarative verbal memory storage and retrieval. White matter correlating with storage was in the temporal lobe–particularly lateral to the hippocampus and in the anterior temporal stem–, in the thalamic region and in the anterior limb of the internal capsule, all on the left hemisphere, and also in the right anterior temporal stem. The same volumes were relevant for retrieval, but to them were added temporo-parieto-frontal paramedian bundles, particularly the cingulum and the fronto-occipital fasciculus. These 3D maps indicate the white matter regions most critically involved in declarative verbal memory in humans.

Details DOI

YNIMG Journal 2007 Journal Article

Fractal dimension and white matter changes in multiple sclerosis

Francisco J. Esteban
Jorge Sepulcre
Nieves Vélez de Mendizábal
Joaquín Goñi
Juan Navas
Juan Ruiz de Miras
Bartolome Bejarano
Jose C. Masdeu

The brain white matter (WM) in multiple sclerosis (MS) suffers visible and non-visible (normal-appearing WM (NAWM)) changes in conventional magnetic resonance (MR) images. The fractal dimension (FD) is a quantitative parameter that characterizes the morphometric variability of a complex object. Our aim was to assess the usefulness of FD analysis in the measurement of WM abnormalities in conventional MR images in patients with MS, particularly to detect NAWM changes. First, we took on a voxel-based morphometry approach optimized for MS to obtain the segmented brain. Then, the FD of the whole grey–white matter interface (WM border) and skeletonized WM was calculated in patients with MS and healthy controls. To assess the FD of the NAWM, we focused our analysis on single sections without lesions at the centrum semiovale level. We found that patients with MS had a significant decrease in the FD of the entire brain WM compared with healthy controls. Such a decrease of the FD was detected not only on MR image sections with MS lesions but also on single sections with NAWM. Taken together, the results showed that FD identifies changes in the brain of patients with MS, including in NAWM, even at an early phase of the disease. Thus, FD might become a useful marker of diffuse damage of the central nervous system in MS.

Details DOI