Biology subjects

Giovanello, K. S.

Publications and source records attributed to Giovanello, K. S..

4 recordsLinked to original sources

bioRxiv2024

Cortico-hippocampal interactions underlie schema-supported memory encoding in older adults

Although episodic memory is typically impaired in older adults (OAs) compared to young adults (YAs), this deficit is attenuated when OAs can leverage their rich semantic knowledge, such as their knowledge of schemas. Memory is better for items consistent with pre-existing schemas and this effect is larger in OAs. Neuroimaging studies have associated schema use with the ventromedial prefrontal cortex (vmPFC) and hippocampus (HPC), but most of this research has been limited to YAs. This fMRI study investigated the neural mechanisms underlying how schemas boost episodic memory in OAs. Participants encoded scene-object pairs with varying congruency, and memory for the objects was tested the following day. Congruency with schemas enhanced object memory for YAs and, more substantially, for OAs. FMRI analyses examined how cortical modulation of HPC predicted subsequent memory. Congruency-related vmPFC modulation of left HPC enhanced subsequent memory in both age groups, while congruency-related modulation from angular gyrus (AG) boosted subsequent memory only in OAs. Individual differences in cortico-hippocampal modulations indicated that OAs preferentially used their semantic knowledge to facilitate encoding via an AG-HPC interaction, suggesting a compensatory mechanism. Collectively, our findings illustrate age-related differences in how schemas influence episodic memory encoding via distinct routes of cortico-hippocampal interactions.

neuroscience↗

bioRxiv2023

Enabling brain-wide mapping of directed functional connectivity at 3T via layer-dependent fMRI with draining-vein suppression

Layer-dependent functional magnetic resonance imaging (fMRI) is a promising yet challenging approach for investigating layer-specific functional connectivity (FC). Achieving a brain-wide mapping of layer-specific FC requires several technical advancements, including sub-millimeter spatial resolution, sufficient temporal resolution, functional sensitivity, global brain coverage, and high spatial specificity. Although gradient echo (GE)-based echo planar imaging (EPI) is commonly used for rapid fMRI acquisition, it faces significant challenges due to the draining-vein contamination. In this study, we addressed these limitations by integrating velocity-nulling (VN) gradients into a GE-BOLD fMRI sequence to suppress vascular signals from the vessels with fast-flowing velocity. The extravascular contamination from pial veins was mitigated using a GE-EPI sequence at 3T rather than 7T, combined with phase regression methods. Additionally, we incorporated advanced techniques, including simultaneous multi-slice (SMS) acceleration and NOise Reduction with DIstribution Corrected principal component analysis (NORDIC PCA) denoising, to improve temporal resolution, spatial coverage, and signal sensitivity. This resulted in a VN fMRI sequence with 0.9-mm isotropic spatial resolution, a repetition time (TR) of 4 seconds, and brain-wide coverage. The VN gradient strength was determined based on results from a button-pressing task. Using resting-state data, we validated layer-specific FC through seed-based analyses, identifying distinct connectivity patterns in the superficial and deep layers of the primary motor cortex (M1), with significant inter-layer differences. Further analyses with a seed in the primary sensory cortex (S1) demonstrated the reliability of the method. Brain-wide layer-dependent FC analyses yielded results consistent with prior literature, reinforcing the efficacy of VN fMRI in resolving layer-specific functional connectivity. Given the widespread availability of 3T scanners, this technical advancement has the potential for significant impact across multiple domains of neuroscience research.

neuroscience↗

bioRxiv2020

Brain-wide functional networks associated with anatomically- and functionally-defined hippocampal subfields using ultrahigh-resolution fMRI

The hippocampus is critical for learning and memory and may be separated into anatomically-defined hippocampal subfields (aHPSFs). Many studies have shown that aHPSFs, and their respective functional networks, are differentially vulnerable to a variety of disorders. Hippocampal functional networks, particularly during resting state, are generally analyzed using aHPSFs as seed regions, with the underlying assumption that the function within a subfield is homogeneous, yet heterogeneous between subfields. However, several prior studies that have utilized aHPSFs and assessed brain-wide cortical connectivity have observed similar resting-state functional connectivity profiles between aHPSFs. Alternatively, data-driven approaches offer a means to investigate hippocampal functional organization without a priori assumptions. However, insufficient spatial resolution may lead to partial volume effects at the boundaries of hippocampal subfields, resulting in a number of caveats concerning the reliability of the results. Hence, we developed a functional Magnetic Resonance Imaging (fMRI) sequence on a 7T MR scanner achieving 0.94 mm isotropic resolution with a TR of 2s and brain-wide coverage to 1) investigate whether hippocampal functional segmentation with ultrahigh-resolution data demonstrate similar anatomical, lamellar structures in the hippocampus, and 2) define and compare the brain-wide FC associated with fine-grained aHPSFs and functionally-defined hippocampal subfields (fHPSFs). Using a spatially restricted hippocampal Independent Component Analysis (ICA) approach, this study showed that fHPSFs were arranged along the longitudinal axis of the hippocampus that were not comparable to the lamellar structures of aHPSFs. Contrary to the anatomically defined hippocampal subfields which are bilaterally symmetrical, 13 out of 20 fHPSFs were unilateral. For brain-wide FC, the fHPSFs rather than aHPSFs revealed that a number of fHPSFs connected specifically with some of the functional networks. The visual and sensorimotor networks preferentially connected with different portions of CA1, CA3 and CA4/DG. The DMN was also found to connect more extensively with posterior subfields rather than anterior subfields. Finally, the frontoparietal network (FPN) was anticorrelated with the head portion of CA1. The investigation of functional networks associated with the fHPSFs may enhance the sensitivity of biomarkers for a range of neurological disorders, as network-based approaches take into account disease-related alterations in brain-wide interconnections rather than measuring the regional changes of hippocampus.

neuroscience↗

bioRxiv2020

The contribution of hippocampal subfields to the progression of neurodegeneration

Mild cognitive impairment (MCI) is often considered the precursor of Alzheimers disease. However, MCI is associated with substantially variable progression rates, which are not well understood. Attempts to identify the mechanisms that underlie MCI progression have often focused on the hippocampus, but have mostly overlooked its intricate structure and subdivisions. Here, we utilized deep learning to delineate the contribution of hippocampal subfields to MCI progression using a total sample of 1157 subjects (349 in the training set, 427 in a validation set and 381 in the testing set). We propose a dense convolutional neural network architecture that differentiates stable and progressive MCI based on hippocampal morphometry. The proposed deep learning model predicted MCI progression with an accuracy of 75.85%. A novel implementation of occlusion analysis revealed marked differences in the contribution of hippocampal subfields to the performance of the model, with presubiculum, CA1, subiculum, and molecular layer showing the most central role. Moreover, the analysis reveals that 10.5% of the volume of the hippocampus was redundant in the differentiation between stable and progressive MCI. Our predictive model uncovers pronounced differences in the contribution of hippocampal subfields to the progression of MCI. The results may reflect the sparing of hippocampal structure in individuals with a slower progression of neurodegeneration.

neuroscience↗