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Fil: Fernández, Gerardo. ViewMind Inc; Estados Unidos Fil: Fernández, Gerardo. Universidad Nacional del Sur. Instituto de Investigaciones en Ingeniería Eléctrica. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina Fil: Fernández, Gerardo. Carrera del Investigador Científico y Tecnológico; Argentina Fil: González, Ana Paula. Instituto de Neurociencias. Fundación para la Lucha contra las Enfermedades Neurológicas de la Infancia. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina Fil: González, Ana Paula. Grupo de Investigación en Neurociencias Aplicadas a las Alteraciones de la conducta; Argentina Fil: Abulafia, Carolina Andrea. Instituto de Neurociencias. Fundación para la Lucha contra las Enfermedades Neurológicas de la Infancia. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina Fil: Abulafia, Carolina Andrea. Grupo de Investigación en Neurociencias Aplicadas a las Alteraciones de la conducta; Argentina Fil: Abulafia, Carolina Andrea. Fundación para la Lucha contra las Enfermedades Neurológicas de la Infancia. Departamento de Neurología. Servicio de Psiquiatría; Argentina Fil: Abulafia, Carolina Andrea. Pontificia Universidad Católica Argentina. Instituto de Investigaciones Biomédicas; Argetnina Fil: Abulafia, Carolina Andrea. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina Fil: Fiorentini, Leticia. Grupo de Investigación en Neurociencias Aplicadas a las Alteraciones de la conducta; Argentina Fil: Fiorentini, Leticia. Instituto de Neurociencias. Fundación para la Lucha contra las Enfermedades Neurológicas de la Infancia. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina Fil: Fiorentini, Leticia. Fundación para la Lucha contra las Enfermedades Neurológicas de la Infancia. Departamento de Neurología. Servicio de Psiquiatría; Argentina Fil: Agamennoni, Osvaldo. Universidad Nacional del Sur. Instituto de Investigaciones en Ingeniería Eléctrica. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina Fil: Agamennoni, Osvaldo. Carrera del Investigador Científico y Tecnológico; Argentina Fil: Guinjoan, Salvador M. Universidad de Buenos Aires. Facultad de Medicina. Departamento de Fisiología; Argentina Fil: Guinjoan, Salvador M. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina Fil: Guinjoan, Salvador M. Laureate Institute for Brain Research; Estados Unidos Abstract: Introduction: Eye movement patterns during reading are well defined and documented. Each eye movement ends up in a fixation point, which allows the brain to process the incoming information and program the following saccade. In this work, we investigated whether eye movement alterations during a reading task might be already present in middle-aged, cognitively normal offspring of late-onset Alzheimer’s disease (O-LOAD). Methods: 18 O-LOAD and 18 age-matched healthy individuals with no family history of LOAD participated in the study. Participants were seated in front of a 20-inch LCD monitor, and single sentences were presented on it. Eye movements were recorded with an eye tracker with a sampling rate of 1000 Hz. Results: Analysis of eye movements during reading revealed that O-LOAD displayed more fixations, shorter saccades, and shorter fixation durations than controls. Conclusion: The present study shows that O-LOAD experienced alterations in their eye movements during reading. O-LOAD eye movement behavior could be considered an initial sign of oculomotor impairment. Hence, the evaluation of eye movement during reading might be a useful tool for monitoring well-defined cognitive resources.

Major Depressive Disorder (MDD) poses a significant public health challenge due to its high prevalence and the substantial burden it places on individuals and healthcare systems. Real-time functional magnetic resonance imaging neurofeedback (rtfMRI-NF) shows promise as a treatment for this disorder, although its mechanisms of action remain unclear. This study investigated whole-brain response patterns during rtfMRI-NF training to explain interindividual variability in clinical efficacy in MDD. We analyzed data from 95 participants (67 active, 28 control) with MDD from previous rtfMRI-NF studies designed to increase left amygdala activation through positive autobiographical memory recall. Significant symptom reduction was observed in the active group (t=-4.404, d=-0.704, p<0.001) but not in the control group (t=-1.609, d=-0.430, p=0.111). However, left amygdala activation did not account for the variability in clinical efficacy. To elucidate the brain training process underlying the clinical effect, we examined whole-brain activation patterns during two critical phases of the neurofeedback procedure: activation during the self-regulation period, and transient responses to feedback signal presentations. Using a systematic process involving feature selection, manifold extraction, and clustering with cross-validation, we identified two subtypes of regulation activation and three subtypes of brain responses to feedback signals. These subtypes were significantly associated with the clinical effect (regulation subtype: F=8.735, p=0.005; feedback response subtype: F=5.326, p=0.008; subtypes' interaction: F=3.471, p=0.039). Subtypes associated with significant symptom reduction were characterized by selective increases in control regions, including lateral prefrontal areas, and decreases in regions associated with self-referential thinking, such as default mode areas. These findings suggest that large-scale brain activity during training is more critical for clinical efficacy than the level of activation in the neurofeedback target region itself. Tailoring neurofeedback training to incorporate these patterns could significantly enhance its therapeutic efficacy.

Background: Sensitivity to threat with dysregulation of fear learning is thought to contribute to the development of psychiatric disorders, including anxiety disorders (AD) and major depressive disorder (MDD). However, fewer studies have examined fear learning in MDD than in AD. Nearly half of individuals with MDD have an AD and the comorbid diagnosis has worse outcomes. The current study used propensity matching to examine the hypothesis that AD+MDD shows greater neural correlates of fear learning than MDD, suggesting that the co-occurrence of AD+MDD is exemplified by exaggerated defense related processes., Methods: 195 individuals with MDD (N = 65) or AD+MDD (N=130) were recruited from the community and completed multi-level assessments, including a Pavlovian fear learning task during functional imaging., Results: MDD and AD+MDD showed significantly different patterns of activation for [CSplus-CSminus] in the medial amygdala (ηp 2 =0.009), anterior insula (ηp 2 =0.01), dorsolateral prefrontal cortex (ηp 2 =0.002), dorsal anterior cingulate cortex (ηp 2 =0.01), mid-cingulate cortex (ηp 2 =0.01) and posterior cingulate cortex (ηp 2 =0.02). These differences were driven by greater activation to the CS+ in late conditioning phases in ADD+MDD relative to MDD., Conclusions: AD+MDD showed a pattern of increased sustained activation in regions identified with fear learning. Effects were consistently driven by the threat condition, further suggesting fear signaling as the emergent target process. Differences emerged in regions associated with salience processing, attentional orienting/conflict, and self-relevant processing.These findings help to elucidate the fear signaling mechanisms involved in the pathophysiology of comorbid anxiety and depression, thereby highlighting promising treatment targets for this prevalent treatment group.

Resting-state functional connectivity (RSFC) has been proposed as a potential indicator of repetitive negative thinking (RNT) in depression. However, identifying the specific functional process associated with RSFC alterations is challenging, and it remains unclear whether alterations in RSFC for depressed individuals are directly related to the RNT process or to individual characteristics distinct from the negative thinking process per se. To investigate the relationship between RSFC alterations and the RNT process in individuals with major depressive disorder (MDD), we compared RSFC with functional connectivity during an induced negative-thinking state (NTFC) in terms of their predictability of RNT traits and associated whole-brain connectivity patterns using connectome-based predictive modeling (CPM) and connectome-wide association (CWA) analyses. Thirty-six MDD participants and twenty-six healthy control participants underwent both resting state and induced negative thinking state fMRI scans. Both RSFC and NTFC distinguished between healthy and depressed individuals with CPM. However, trait RNT in depressed individuals, as measured by the Ruminative Responses Scale-Brooding subscale, was only predictable from NTFC, not from RSFC. CWA analysis revealed that negative thinking in depression was associated with higher functional connectivity between the default mode and executive control regions, which was not observed in RSFC. These findings suggest that RNT in depression involves an active mental process encompassing multiple brain regions across functional networks, which is not represented in the resting state. Although RSFC indicates brain functional alterations in MDD, they may not directly reflect the negative thinking process.