Biology subjects

Goya-Maldonado, R.

Publications and source records attributed to Goya-Maldonado, R..

6 recordsLinked to original sources

bioRxiv2025

Dissecting heterogeneity in cortical thickness abnormalities in major depressive disorder: a large-scale ENIGMA MDD normative modelling study

ImportanceMajor depressive disorder (MDD) is highly heterogeneous, with marked individual differences in clinical presentation and neurobiology, which may obscure identification of structural brain abnormalities in MDD. To explore this, we used normative modeling to index regional patterns of variability in cortical thickness (CT) across individual patients. ObjectiveTo use normative modeling in a large dataset from the ENIGMA MDD consortium to obtain individualised CT deviations from the norm (relative to age, sex and site) and examine the relationship between these deviations and clinical characteristics. Design, setting, and participantsA normative model adjusting for age, sex and site effects was trained on 35 CT measures from FreeSurfer parcellation of 3,181 healthy controls (HC) from 34 sites (40 scanners). Individualised z-score deviations from this norm for each CT measure were calculated for a test set of 2,119 HC and 3,645 individuals with MDD. For each individual, each CT z-score was classified as being within the normal range (95% of individuals) or within the extreme range (2.5% of individuals with the thinnest or thickest cortices). Main outcome measuresZ-score deviations of CT measures of MDD individuals as estimated from a normative model based on HC. ResultsZ-score distributions of CT measures were largely overlapping between MDD and HC (minimum 92%, range 92-98%), with overall thinner cortices in MDD. 34.5% of MDD individuals, and 30% of HC individuals, showed an extreme deviation in at least one region, and these deviations were widely distributed across the brain. There was high heterogeneity in the spatial location of CT deviations across individuals with MDD: a maximum of 12% of individuals with MDD showed an extreme deviation in the same location. Extreme negative CT deviations were associated with having an earlier onset of depression and more severe depressive symptoms in the MDD group, and with higher BMI across MDD and HC groups. Extreme positive deviations were associated with being remitted, of not taking antidepressants and less severe symptoms. Conclusions and relevanceOur study illustrates a large heterogeneity in the spatial location of CT abnormalities across patients with MDD and confirms a substantial overlap of CT measures with HC. We also demonstrate that individualised extreme deviations can identify protective factors and individuals with a more severe clinical picture. Key points QuestionCan z-scores derived from normative modelling shed light on the heterogeneous group-level findings of cortical thickness abnormalities in major depression and what characterises individuals at the extreme ends of cortical thickness abnormalities? FindingWe confirmed a large overlap in z-score distributions between depressed individuals and healthy controls and a heterogeneous spatial distribution of extreme z-deviations across brain regions across individual patients. Lower z-scores for cortical thickness were related to more severe clinical characteristics. MeaningOur findings confirm the heterogeneity in individual variation in the location and extent of CT abnormalities across patients with MDD and stress the importance of individualised predictions when examining cortical thickness abnormalities.

neuroscience↗

bioRxiv2022

A Large-Scale ENIGMA Multisite Replication Study of Brain Age in Depression

BackgroundSeveral studies have evaluated whether depressed persons have older appearing brains than their nondepressed peers. However, the estimated neuroimaging-derived "brain age gap" has varied from study to study, likely driven by differences in training and testing sample (size), age range, and used modality/features. To validate our previously developed ENIGMA brain age model and the identified brain age gap, we aim to replicate the presence and effect size estimate previously found in the largest study in depression to date (N=2,126 controls & N=2,675 cases; +1.08 years [SE 0.22], Cohens d=0.14, 95% CI: 0.08-0.20), in independent cohorts that were not part of the original study. MethodsA previously trained brain age model (www.photon-ai.com/enigma_brainage) based on 77 FreeSurfer brain regions of interest was used to obtain unbiased brain age predictions in 751 controls and 766 persons with depression (18-75 years) from 13 new cohorts collected from 20 different scanners. ResultsOur ENIGMA MDD brain age model generalized reasonably well to controls from the new cohorts (predicted age vs. age: r = 0.73, R2=0.47, MAE=7.50 years), although the performance varied from cohort to cohort. In these new cohorts, on average, depressed persons showed a significantly higher brain age gap of +1 year (SE 0.35) (Cohens d{square}={square}{square}.15, 95% CI: 0.05-0.25) compared with controls, highly similar to our previous finding. ConclusionsThis study further validates our previously developed ENIGMA brain age algorithm. Importantly, we replicated the brain age gap in depression with a comparable effect size. Thus, two large-scale independent mega-analyses across in total 32 cohorts and >3,400 patients and >2,800 controls worldwide show reliable but subtle effects of brain aging in adult depression.

neuroscience↗

bioRxiv2021

High frequency rTMS modulates functional connectivity nodes and boundaries differently in the human brain

Repetitive transcranial magnetic stimulation (rTMS) has gained considerable importance in the treatment of neuropsychiatric disorders, including major depression. However, it is not yet understood how rTMS alters brains functional connectivity. Here we report changes in functional connectivity captured by resting state functional magnetic resonance imaging (rsfMRI) within the first hour after 10Hz rTMS. We apply subject-specific parcellation schemes to detect changes (1) in network nodes, where the strongest functional connectivity of regions is observed, and (2) in network boundaries, where functional transitions between regions occur. We use support vector machines (SVM), a widely used machine learning algorithm that is robust and effective for the classification and characterization of time intervals of changes in node and boundary maps. Our results reveal that changes in connectivity at the boundaries are slower and more complex than in those observed in the nodes, but of similar magnitude according to accuracy confidence intervals. These results were strongest in the posterior cingulate cortex and precuneus. As network boundaries are indeed under-investigated in comparison to nodes in connectomics research, our results highlight their contribution to functional adjustments to rTMS.

neuroscience↗

bioRxiv2020

Regional expression profiles of risk genes for depression are associated with brain activation patterns in emotion and reward tasks

The spatial relationship between gene expression profiles and neural response patterns known to be altered in neuropsychiatric disorders, e.g. depression, can guide the development of more targeted therapies. Here, we estimated the correlation between human transcriptome data and two different brain activation maps measured with functional magnetic resonance imaging (fMRI) in healthy subjects. Whole-brain activation patterns evoked during an emotional face recognition task were associated with topological mRNA expression of genes involved in cellular transport. In contrast, fMRI activation patterns related to the acceptance of monetary rewards were associated with genes implicated in neuronal development, synapse regulation, and gene transcription. An overlap of these genes with risk genes from major depressive disorder genome-wide associations studies revealed the involvement of the master regulators TCF4 and MEF2C. These results were further validated by means of meta-analytic fMRI data. Overall, the identification of stable relationships between spatial gene expression profiles and fMRI data may reshape the prospects for imaging transcriptomics studies.

neuroscience↗

bioRxiv2019

Intermittent theta burst stimulation at personalized targets reduces the functional connectivity of the default mode network in healthy subjects

Understanding the mechanisms by which transcranial magnetic stimulation protocols exert changes in the default mode network (DMN) is paramount to develop therapeutically more effective approaches in the future. A full session (3000 pulses) of 10 Hz repetitive transcranial magnetic stimulation (HF-rTMS) reduces the functional connectivity (FC) of the DMN and the subgenual anterior cingulate cortex but current understanding of the effects of a single session of intermittent theta burst stimulation (iTBS) on the DMN in healthy subjects is limited. To reduce the effects of inter-individual variability in functional architectures, we used a novel personalized target selection approach based on each subjects resting state fMRI for an unprecedented investigation into the effects of a single session (1800 pulses) of iTBS over the DMN in healthy controls. 26 healthy subjects participated in a double-blind, crossover, sham-controlled study. After iTBS to the personalized left dorsolateral prefrontal cortex (DLPFC) targets, we investigated the time lapse of effects in the DMN and its relationship to the harm avoidance (HA) personality trait measure (Temperament and Character Inventory/TCI). Approx. 25-30 minutes after stimulation, we observed reduced FC between the DMN and the rostral anterior cingulate cortex (rACC). About 45 minutes after stimulation the FC of rACC strongly decreased further, as did the FC of right anterior insula (rAI) with the DMN. We also report a positive correlation between the FC decrease in the rACC and the HA domain of TCI. Our results show how iTBS at personalized left-DLPFC targets reduces the FC between DMN and the rACC and rAI, regions typically described as nodes of the salience network. We find that HA scores can potentially predict iTBS response, as has been observed for HF-rTMS.

neuroscience↗

bioRxiv2018

Resting state fMRI based target selection for personalized rTMS: stimulation over the left DLPFC temporarily alters the default mode network in healthy subjects

High frequency repetitive TMS (HF-rTMS) to the left dorsolateral prefrontal cortex (DLPFC) is an approved option for the treatment of depression, but there is also much variability in the resulting antidepressant response. This variability is believed to arise from various factors, an important one of which being the method by which rTMS is targeted to the optimal stimulation site in the left DLPFC. To more accurately target HF-rTMS at left DLPFC, we present a new method for target selection based on individual RS-fMRI data. We show in 23 healthy subjects that the new proposed method of target selection is reproducible and yields left DLPFC targets whose functional connectivity correlates more negatively with subgenual anterior cingulate cortex (sgACC) than targets based on standard MNI coordinates. Since previous work has highlighted higher negative connectivity with the sgACC as an important feature of targets for higher antidepressant effect of HF-rTMS, the targets selected by the new method can be expected to lead to a higher therapeutic response. Additionally, the mechanism of action of an entire single session of HF-rTMS (3000 pulses) in healthy subjects has not been reported. We show significant decreases in functional connectivity of the default mode network in sgACC and ventral striatum (vStr) regions, peaking at 27-32 minutes after stimulation. Also, we report a negative correlation between the magnitude of this decrease in the right sgACC and the harm avoidance domain measure from the Temperament and Character Inventory (TCI). This finding points towards the possibility of using the harm avoidance measure as a predictor of HF-rTMS response. In addition, the decreased functional connectivity of the default mode network in right nucleus accumbens (NAcc) correlates with a short-term decrease in self-rated negative emotions from the Positive and Negative Affect Schedule (PANAS) i.e. the lower the functional connectivity of right NAcc with the default mode network, the lower the reported perception of negative mood by the subjects. This suggests a mechanism by which changes induced by rTMS influence the perception of negative mood in recipients.

neuroscience↗