Disorders of the Nervous System - Neurodegenerative/Late Life

Frontotemporal dementia (FTD) is a neurodegenerative disorder affecting the frontal and temporal lobes (Grossman, 2023), with behavioral variant FTD (bvFTD) as the most common clinical presentation, characterized by changes in social behavior, personality, and executive function (Matarrubia, 2014). Staging dementia is critical for effective clinical management, as it helps tailor personalized care, and it provides a framework for documenting the impact of therapeutic interventions that may alter the course of the underlying disorder.
T1-weighted MRI is an important modality for diagnosis and clinical work-up, where visual inspection of cortical brain volume is part of the diagnostic criteria. Automated brain MRI segmentation methods are considered better than visual inspection alone, as they offer quantitative and repeatable measures of anatomical changes. However, volumetric analysis of structural MRI is limited to characterizing volume loss which is thought to occur later during the disease process. In contrast, texture analysis, which quantifies microstructural changes by examining relationships between signal intensities of neighboring voxels (Larroza, 2016), may detect physiological changes leading to neuronal loss.
We hypothesize that gray matter microstructural damage, quantified through texture analysis, can differentiate disease stages in bvFTD. Specifically, we focus on the sum average texture feature, which captures the relationship between radiolucent (dark, volume loss) and radiopaque (bright, dense) regions in MRI images. Neuron loss typically manifests as dark regions on T1-weighted MRI, with early changes often too subtle for visual detection. This study explores whether sum average can distinguish between mild and moderate dementia in bvFTD patients. 

Chemotherapy-induced cognitive impairment is a common problem in breast cancer patients, but its underlying mechanism remains unclear. Chemotherapy drugs can induce systemic inflammation, which may impact the nervous system by crossing the blood-brain barrier. The glymphatic system is recently found responsible for waste clearance in the brain, involving four main processes: cerebrospinal fluid (CSF) production, CSF influx into perivascular spaces, substance exchange in the white matter, and waste clearance. MRI techniques can assess these processes using four indicators: choroid plexus volume, volume fraction of perivascular space (PVSVF), volume fraction of free water in white matter (FW-WM), and diffusivity along the perivascular space (ALPS). This study aims to investigate the longitudinal changes in glymphatic function during neoadjuvant chemotherapy in breast cancer patients and explore their relationship with cognitive and inflammatory markers. 

Young-onset Alzheimer's Disease is a rare form of Alzheimer's Disease characterized by early symptom onset (< 65 years) and more aggressive clinical course (Mendez, 2019). Previous literature reported altered connectivity within the default-mode network in patients with young-onset Alzheimer's Disease (Gour et al., 2014; Lehmann et al., 2013; Singh et al., 2023), which is a large-scale brain network associated with episodic memory and self-awareness (Buckner et al., 2005). Despite the importance of the default-mode network in Alzheimer's Disease pathology and cognitive functioning, little is known about how the default-mode network interacts with other cognitive networks, such as salience and dorsal attention network, in patients with young-onset Alzheimer's Disease. 

Normative models (NMs) in neuroscience aim to characterize interindividual variability in brain phenotypes, establishing reference ranges-or brain charts-against which individual brains can be compared. NMs help identify individual-level abnormalities as potential biomarkers for neurological and psychiatric disorders [1,2,3]. However, conventional NMs face computational challenges in mapping charts at high spatial resolutions. State-of-the-art techniques [4,5] rely on exhaustive model fitting for each voxel or vertex, which is computationally burdensome and limits scalability for large neuroimaging datasets.

The high dimensionality of neuroimaging data complicates the development of high-resolution NMs. Identifying low-dimensional representations of cortical features can help overcome this challenge. Recent works suggest that brain eigenmodes may provide effective low-dimensional basis functions for reconstructing phenotypic variation on the cortical surface [6,7]. Here, we introduce spectral normative modeling (SNM), a novel approach that leverages spatial reconstructions via brain eigenmodes to generate normative brain charts at varying spatial scales and resolutions. By computing normative ranges of eigenmodes and accounting for cross-mode dependencies, SNM offers an efficient solution for high-resolution charting of brain phenotypes.

Alzheimer's disease (AD) is a neurodegenerative disorder marked by the accumulation of amyloid-beta plaques and tau tangles, contributing to neuronal loss and cortical atrophy, leading to progressive structural changes in brain regions critical for memory and cognition, differentiating AD brains from the normative trajectories of healthy aging [8]. We applied SNM to identify these structural deviations at the individual level, demonstrating its utility in characterizing neurological disorders and identifying potential prognostic biomarkers. 

Tau pathology is a hallmark of Alzheimer's disease (AD), spreading through the brain in specific patterns likely driven by brain connectivity (Liu et al., 2012; Walsh et al., 2016). Accumulating evidence suggests that certain brain regions are more susceptible to tau accumulation due to cellular composition, gene expression, receptor profiles, developmental patterns, or pathological conditions (Brettschneider et al., 2015; Mrdjen et al., 2019). However, most computational models simulating tau propagation focus on connectome-based spreading only, often constrained to specific connectivity types (Vogel et al., 2020; Yang et al., 2021), with limited exploration of regional vulnerability (Anand et al., 2022). We used the Susceptible-Infected-Removed (SIR) agent-based model (Zheng et al., 2019), a connectome-based spreading model integrating regional biological properties to modulate tau spread. Using diverse brain connectomes and regional biological measures, we aimed to investigate whether tau propagation is driven by connectome-based spreading, regional vulnerability, or their interplay. 

Alzheimer's disease (AD) is a progressive neurodegenerative disease that poses a significant challenge to global health, profoundly impacting individuals, families, and healthcare systems. Early detection of AD is crucial, as it allows for timely interventions that could slow disease progression and improve patient outcomes. The advent of recent novel immunotherapies has further heightened the need for cost-effective and time-efficient biomarkers for early diagnosis to enhance treatment effectiveness(Hansson, 2021). Deep learning methods has revolutionized MRI's potential towards earlier and more precise AD screening and facilitating timely medical treatment. In addition, interpretable models could enhance the confidence of physicians and patients in medical imaging models.