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1.
Netw Neurosci ; 8(2): 395-417, 2024.
Artículo en Inglés | MEDLINE | ID: mdl-38952809

RESUMEN

Functional brain networks have preserved architectures in rest and task; nevertheless, previous work consistently demonstrated task-related brain functional reorganization. Efficient rest-to-task functional network reconfiguration is associated with better cognition in young adults. However, aging and cognitive load effects, as well as contributions of intra- and internetwork reconfiguration, remain unclear. We assessed age-related and load-dependent effects on global and network-specific functional reconfiguration between rest and a spatial working memory (SWM) task in young and older adults, then investigated associations between functional reconfiguration and SWM across loads and age groups. Overall, global and network-level functional reconfiguration between rest and task increased with age and load. Importantly, more efficient functional reconfiguration associated with better performance across age groups. However, older adults relied more on internetwork reconfiguration of higher cognitive and task-relevant networks. These reflect the consistent importance of efficient network updating despite recruitment of additional functional networks to offset reduction in neural resources and a change in brain functional topology in older adults. Our findings generalize the association between efficient functional reconfiguration and cognition to aging and demonstrate distinct brain functional reconfiguration patterns associated with SWM in aging, highlighting the importance of combining rest and task measures to study aging cognition.


Brain networks identified by functional connectivity (FC) have preserved architectures from rest to task and across task demands. Higher similarity, implying more efficient network reconfiguration, was associated with better cognition and task performance in young adults. To examine how it may be influenced by aging, we compared whole-brain and network-level FC similarities between resting-state and spatial working memory fMRI in young and older adults. At whole-brain level and higher order cognitive networks, older adults evidenced less efficient network reconfiguration from rest to task than young adults. Importantly, more efficient reconfiguration was associated with better accuracy. This relationship relied more on internetwork connections in older adults. Despite reduced neural resources compared to young, maintaining efficient network updating still contributes to better cognition at older age.

2.
J Alzheimers Dis ; 99(3): 965-980, 2024.
Artículo en Inglés | MEDLINE | ID: mdl-38759005

RESUMEN

Background: Alzheimer's disease (AD) and behavioral variant frontotemporal dementia (bvFTD) show differential vulnerability to large-scale brain functional networks. Plasma neurofilament light (NfL), a promising biomarker of neurodegeneration, has been linked in AD patients to glucose metabolism changes in AD-related regions. However, it is unknown whether plasma NfL would be similarly associated with disease-specific functional connectivity changes in AD and bvFTD. Objective: Our study examined the associations between plasma NfL and functional connectivity of the default mode and salience networks in patients with AD and bvFTD. Methods: Plasma NfL and neuroimaging data from patients with bvFTD (n = 16) and AD or mild cognitive impairment (n = 38; AD + MCI) were analyzed. Seed-based functional connectivity maps of key regions within the default mode and salience networks were obtained and associated with plasma NfL in these patients. RESULTS: We demonstrated divergent associations between NfL and functional connectivity in AD + MCI and bvFTD patients. Specifically, AD + MCI patients showed lower default mode network functional connectivity with higher plasma NfL, while bvFTD patients showed lower salience network functional connectivity with higher plasma NfL. Further, lower NfL-related default mode network connectivity in AD + MCI patients was associated with lower Montreal Cognitive Assessment scores and higher Clinical Dementia Rating sum-of-boxes scores, although NfL-related salience network connectivity in bvFTD patients was not associated with Neuropsychiatric Inventory Questionnaire scores. CONCLUSIONS: Our findings indicate that plasma NfL is differentially associated with brain functional connectivity changes in AD and bvFTD.


Asunto(s)
Enfermedad de Alzheimer , Biomarcadores , Demencia Frontotemporal , Imagen por Resonancia Magnética , Proteínas de Neurofilamentos , Humanos , Enfermedad de Alzheimer/sangre , Enfermedad de Alzheimer/fisiopatología , Enfermedad de Alzheimer/diagnóstico por imagen , Femenino , Demencia Frontotemporal/sangre , Demencia Frontotemporal/fisiopatología , Demencia Frontotemporal/diagnóstico por imagen , Masculino , Anciano , Proteínas de Neurofilamentos/sangre , Persona de Mediana Edad , Biomarcadores/sangre , Disfunción Cognitiva/sangre , Disfunción Cognitiva/fisiopatología , Disfunción Cognitiva/diagnóstico por imagen , Encéfalo/diagnóstico por imagen , Encéfalo/fisiopatología , Red Nerviosa/diagnóstico por imagen , Red Nerviosa/fisiopatología , Red en Modo Predeterminado/fisiopatología , Red en Modo Predeterminado/diagnóstico por imagen
3.
Transl Psychiatry ; 13(1): 345, 2023 Nov 11.
Artículo en Inglés | MEDLINE | ID: mdl-37951943

RESUMEN

Mindfulness-based interventions are showing increasing promise as a treatment for psychological disorders, with improvements in cognition and emotion regulation after intervention. Understanding the changes in functional brain activity and neural plasticity that underlie these benefits from mindfulness interventions is thus of interest in current neuroimaging research. Previous studies have found functional brain changes during resting and task states to be associated with mindfulness both cross-sectionally and longitudinally, particularly in the executive control, default mode and salience networks. However, limited research has combined information from rest and task to study mindfulness-related functional changes in the brain, particularly in the context of intervention studies with active controls. Recent work has found that the reconfiguration efficiency of brain activity patterns between rest and task states is behaviorally relevant in healthy young adults. Thus, we applied this measure to investigate how mindfulness intervention changed functional reconfiguration between rest and a breath-counting task in elderly participants with self-reported sleep difficulties. Improving on previous longitudinal designs, we compared the intervention effects of a mindfulness-based therapy to an active control (sleep hygiene) intervention. We found that mindfulness intervention improved self-reported mindfulness measures and brain functional reconfiguration efficiency in the executive control, default mode and salience networks, though the brain and behavioral changes were not associated with each other. Our findings suggest that neuroplasticity may be induced through regular mindfulness practice, thus bringing the intrinsic functional configuration in participants' brains closer to a state required for mindful awareness.


Asunto(s)
Atención Plena , Adulto Joven , Humanos , Anciano , Atención Plena/métodos , Encéfalo , Cognición/fisiología , Función Ejecutiva/fisiología , Mapeo Encefálico/métodos , Imagen por Resonancia Magnética
4.
Rheumatology (Oxford) ; 61(3): 1166-1174, 2022 03 02.
Artículo en Inglés | MEDLINE | ID: mdl-34156469

RESUMEN

OBJECTIVES: Brain white matter (WM) microstructural changes evaluated by diffusion MRI are well documented in patients with SLE. Yet, the conventional diffusion tensor imaging technique fails to differentiate WM changes that originate from tissue alterations from those due to increased extracellular free water (FW) related to neuroinflammation, microvascular disruption, atrophy, or other extracellular processes. Here, we sought to delineate changes in WM tissue microstructure and extracellular FW volume and examine their relationships with neurocognitive function in SLE patients. METHODS: Twenty SLE patients [16 females, aged 36.0 (10.6)] without clinically overt neuropsychiatric manifestation and 61 healthy controls (HCs) [29 females, aged 29.2 (9.4)] underwent diffusion MRI and computerized neuropsychological assessments cross-sectionally. The FW imaging method was applied to compare microstructural tissue changes and extracellular FW volume of the brain WM between SLE patients and HCs. Association between extracellular FW changes and neurocognitive performance was studied. RESULTS: SLE patients had higher WM extracellular FW compared with HCs (family-wise-error-corrected P < 0.05), while no group difference was found in FW-corrected tissue compartment and structural connectivity metrics. Extracellular FW increases in SLE patients were associated with poorer neurocognitive performance that probed sustained attention (P = 0.022) and higher cumulative glucocorticoid dose (P = 0.0041). Such findings remained robust after controlling for age, gender, intelligence quotient, and total WM volume. CONCLUSION: The association between WM extracellular FW increases and reduced neurocognitive performance suggest possible microvascular degradation and/or neuroinflammation in SLE patients with clinically inactive disease. The mechanistic impact of cumulative glucocorticoids on WM FW deserves further evaluation.


Asunto(s)
Disfunción Cognitiva/diagnóstico por imagen , Disfunción Cognitiva/etiología , Imagen de Difusión Tensora , Espacio Extracelular/diagnóstico por imagen , Lupus Eritematoso Sistémico/complicaciones , Lupus Eritematoso Sistémico/diagnóstico por imagen , Sustancia Blanca/diagnóstico por imagen , Adulto , Agua Corporal/metabolismo , Femenino , Humanos , Masculino , Persona de Mediana Edad , Pruebas Neuropsicológicas , Adulto Joven
5.
Schizophr Res ; 243: 330-341, 2022 05.
Artículo en Inglés | MEDLINE | ID: mdl-34210562

RESUMEN

The ability of automatic feature learning makes Convolutional Neural Network (CNN) potentially suitable to uncover the complex and widespread brain changes in schizophrenia. Despite that, limited studies have been done on schizophrenia identification using interpretable deep learning approaches on multimodal neuroimaging data. Here, we developed a deep feature approach based on pre-trained 2D CNN and naive 3D CNN models trained from scratch for schizophrenia classification by integrating 3D structural and diffusion magnetic resonance imaging (MRI) data. We found that the naive 3D CNN models outperformed the pretrained 2D CNN models and the handcrafted feature-based machine learning approach using support vector machine during both cross-validation and testing on an independent dataset. Multimodal neuroimaging-based models accomplished performance superior to models based on a single modality. Furthermore, we identified brain grey matter and white matter regions critical for illness classification at the individual- and group-level which supported the salience network and striatal dysfunction hypotheses in schizophrenia. Our findings underscore the potential of CNN not only to automatically uncover and integrate multimodal 3D brain imaging features for schizophrenia identification, but also to provide relevant neurobiological interpretations which are crucial for developing objective and interpretable imaging-based probes for prognosis and diagnosis in psychiatric disorders.


Asunto(s)
Esquizofrenia , Imagen de Difusión por Resonancia Magnética , Humanos , Imagen por Resonancia Magnética/métodos , Redes Neurales de la Computación , Neuroimagen , Esquizofrenia/diagnóstico por imagen
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