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Paas Oliveros, L. K.

Publications and source records attributed to Paas Oliveros, L. K..

2 recordsLinked to original sources

Unveiling the Multifaceted Networks of the Left DLPFC for Precision TMS Targeting

The left dorsolateral prefrontal cortex (lDLPFC) is the standard transcranial magnetic stimulation (TMS) target for treatment-resistant depression (TRD), yet non-response rates remain high. TMS efficacy has been linked to the stimulation sites functional connectivity, particularly its anti-correlation with the subgenual cingulate cortex (SGC). While this pragmatic strategy has demonstrated clinical utility, it offers limited insight into how the lDLPFCs network interactions contribute to site-dependent variability in treatment response. Here, we used connectivity-based parcellation within a region of interest encompassing common TMS targets in the left prefrontal cortex (TMS-PFC) to delineate functionally distinct subregions and characterize their large-scale network connectivity and behavioral associations. Our results revealed a hierarchical organization: a coarse two-pole antagonism between anterior-central and superior-posterior subregions and a finer nine-cluster architecture exposing the heterogeneity along anterior-posterior and ventral-dorsal axes within TMS-PFC. Anterior-central areas were strongly anti-correlated with SGC and default-mode network, positively connected with salience, dorsal attention, and control networks, and associated with cognitive control. In contrast, superior-posterior subregions displayed the inverse pattern, while ventral clusters engaged somatomotor and visual networks, and language-related processes. Central and superior-anterior clusters showed differentiated profiles, including associations with inhibition, social cognition, and perceptual functions. To aid clinical translation, we derived a likelihood map integrating granularities, highlighting the anterior-central subregion as the strongest TMS candidate given its connectivity and behavioral relevance in depression, while indicating that neighboring subregions have distinct functions. These findings underscore the hierarchical and heterogeneous organization of TMS-PFC and provide a network-informed reference for developing individualized, symptom-specific TMS interventions. HighlightsO_LITMS targets within the left PFC encompass nine functionally distinct subregions C_LIO_LISubregions differ in SGC, DMN, salience, attention, and control network connectivity C_LIO_LIAnterior-central and superior-posterior subregions show antagonistic profiles C_LIO_LIA likelihood map identifies the anterior-central subregion as optimal TMS target C_LIO_LIFindings provide a network-informed reference for symptom-specific TMS targeting C_LI

neuroscience↗

Predicting Executive Functioning from Brain Networks: Modality Specificity and Age Effects

Healthy aging is associated with structural and functional network changes in the brain, which have been linked to deterioration in executive functioning (EF), while their neural implementation at the individual level remains unclear. As the biomarker potential of individual resting-state functional connectivity (RSFC) patterns has been questioned, we investigated to what degree individual EF abilities can be predicted from gray-matter volume (GMV), regional homogeneity, fractional amplitude of low-frequency fluctuations (fALFF), and RSFC within EF-related, perceptuo-motor, and whole-brain networks in young and old adults. We examined whether differences in out-of-sample prediction accuracy were modality-specific and depended on age or task-demand levels. Both uni- and multivariate analysis frameworks revealed overall low prediction accuracies and moderate to weak brain-behavior associations (R2 < .07, r < .28), further challenging the idea of finding meaningful markers for individual EF performance with the metrics used. Regional GMV, well linked to overall atrophy, carried the strongest information about individual EF differences in older adults, whereas fALFF, measuring functional variability, did so for younger adults. Our study calls for future research analyzing more global properties of the brain, different task-states and applying adaptive behavioral testing to result in sensitive predictors for young and older adults, respectively.

neuroscience↗