Reconceptualizing Consciousness for Neuropsychiatric Conditions
Findings of my ADNI study (To be presented July 2025) (Under construction!)
Preliminary findings of my upcoming ADNI study (Presented April 2023)
A pilot study exploring emotional intensity in English and French Bilinguals (Presented April 2023)
An argument exploring how emotional experiences can work in a cognitive theory without representations (Presented May 2022)
The study of consciousness in Alzheimer’s disease (AD) has predominantly relied on hierarchical models that categorize consciousness into discrete levels, often based on unidimensional markers of arousal or increasing awareness complexity. However, these models fail to capture the dynamic and multifaceted nature of consciousness, particularly with regard to the global state of consciousness (GSC). A novel concept in consciousness science, GSC refers to a dispositional state of being conscious, grounded in cognitive, functional, and behavioral dimensions. Existing frameworks overlook the complexities of this state. This study critiques these models and proposes a multidimensional framework for GSC in AD.
This work critically examines hierarchical models of consciousness, particularly their limitations in capturing the complexities of GSC in AD. A review of contemporary theoretical and empirical studies reveals how disruptions across cognitive and subjective dimensions—such as sensory content, memory, attention, social cognition, and the sense of self—manifest in altered states. Based on these insights, a multidimensional framework for GSC in AD is developed, focusing on the interplay between these dimensions.
A key finding is that GSC in AD is not a static or uniform state but rather a dynamic one that modulates across various phenomenological dimensions. This modulation is critical to understanding the heterogeneity of consciousness in AD, even in severe stages. Disruptions in sensory processing, memory, executive function, and the sense of self can interact in ways that influence the overall GSC, leading to varying patterns of consciousness across individuals. These variations explain why some individuals exhibit fluctuations in awareness or experience periods of clarity despite disease progression. By integrating these dimensions, the proposed framework offers a more nuanced understanding of how consciousness manifests and changes over time, addressing complexities that hierarchical models fail to capture.
This study introduces the global state of consciousness as a novel perspective in AD, emphasizing its dispositional nature and proposing a multidimensional framework for exploring the dynamic interplay of cognitive and subjective dimensions. By integrating theoretical and empirical findings, this approach provides a more comprehensive understanding of consciousness in AD, with implications for research and clinical interventions addressing cognitive and existential challenges.
The hippocampus is central to Alzheimer’s disease (AD), characterized by atrophy and cognitive decline. While volume loss is well-documented, surface-based morphometric (SBM) features—curvature, gyrification, and thickness—remain less explored. Using T1-weighted MRI data from the Alzheimer’s Disease Neuroimaging Initiative (4,617 timepoints; CN: 475, MCI: 673, AD: 269), hippocampal subfields were analyzed with HippUnfold. Linear mixed effects models examined volume and SBM changes, tracking cognitive trajectories in stable (n = 1017) and progressing (n = 301) individuals.
Focusing on CA1, the stable AD group showed significant volume reductions (β = −1.01, p < .001) compared to CN (β = 6.19, p < .001). SBM metrics significantly increased over time across subregions (e.g., curvature: CN: β = −0.72, p < .001; AD: β = 0.006, p < .001), though gyrification did not reach significance in bilateral CA1, CA3, CA4, and subiculum. Time-dependent interactions indicated progressive volume loss and SBM increases across groups (all p’s < .001) except for Stable MCI. Notably, SBM metrics predicted cognitive improvements in AD.
While volume loss remains a key AD marker, it may not capture early morphometric changes. SBM features provide additional insights, underscoring the need for integrated volumetric and surface-based analyses to refine disease detection and therapeutic strategies.