James Benedict Rowe

The paper introduces a longitudinal neuroimaging dataset of Alzheimer's disease pathology, including multimodal data from patients and controls, to identify and validate novel biomarkers.

This paper translates group-level neurophysiological biomarkers into individual-level phenotyping to identify mechanisms of neurodegenerative diseases using MEG data.

This paper applies Bayesian hierarchical modeling to assess regional brain atrophy in rare neurodegenerative disorders, finding significant volume loss in specific brain regions across disease groups.

The paper identifies the CSF YWHAG:NPTX2 ratio as a biomarker for predicting clinical severity and phenoconversion in FTLD, showing strong correlation with disease progression and improved trial design potential.

This paper uses machine learning to stratify genetic frontotemporal dementia patients into subtypes and provides progression scores that improve trial enrichment efficiency.

The paper identifies disease-specific molecular profiles of small tau aggregates across different tauopathies using single-molecule assays.

This study conducts a GWAS on Pick's disease and identifies two SNPs associated with risk, though not at genome-wide significance, suggesting potential genetic factors beyond MAPT mutations.

The paper develops an MRI-based machine learning classifier to identify presymptomatic C9orf72 carriers at high risk of disease onset within two years.

This study investigates apathy as a reduction in prior precision of action outcome beliefs, finding a negative correlation between prior precision and apathy in healthy adults.

The paper shows that white matter hyperintensities (WMHs) appear before other biomarkers in GRN-related frontotemporal dementia, suggesting they are early indicators of disease progression.

The paper identifies a cellular tipping point between aggregate accumulation and removal as a universal feature of neurodegeneration, validated through live-cell assays and super-resolution imaging.

This study shows iPSC-derived astrocytes from AD patients reflect key pathophysiological features, offering a new model for AD research and therapeutic screening.

This study shows that cognitive function in older adults depends on the alignment between functional and structural brain networks, with this dependency being strongest in high-order and subcortical networks.

This paper develops a Bayesian framework integrating MEG and 7T-MRS to infer synaptic connectivity influenced by neurotransmitter concentrations, confirming GABA and glutamate effects on specific cortical connections.

This study investigates the association between cerebellar and subcortical atrophy and neuropsychiatric symptoms in genetic frontotemporal dementia, finding significant differences across genetic mutations.

The paper uses induced pluripotent stem cell-derived cortical neurons from early Alzheimer's patients to show that cellular vulnerability to amyloid-beta correlates with clinical vulnerability.

The paper introduces an information decomposition framework to identify synergistic and redundant neural interactions, showing synergistic activity underlies human complex cognition and evolutionary brain expansion.

This study investigates how locus coeruleus noradrenergic integrity influences inhibitory control in older adults through prefrontal cortical connectivity.

The paper demonstrates that the right anterior dorsolateral and ventrolateral prefrontal cortex dynamically shift inhibitory connectivity to suppress both actions and thoughts, supporting a domain-general inhibitory control mechanism.

This study uses linked ICA to analyze multimodal neuroimaging data from healthy ageing subjects, finding correlations between brain structure, vascular dynamics, and fluid intelligence.

This study shows that baseline cerebral blood flow predicts multiple-demand network activity and fluid intelligence across the lifespan, with non-neuronal CBF effects confounding BOLD signals.

This study shows that atrophy patterns in bvFTD are shaped by both global brain network structure and local transcriptomic vulnerability from disease-related genes.

This study identifies brain regions consistently activated during intentional decisions and categorizes decision types using meta-analyses of fMRI/PET studies.

This study shows that age-related changes in resting-state BOLD signal variability are fully explained by cardiovascular and cerebrovascular factors, not neural activity or atrophy.

This study creates a probabilistic atlas of the human locus coeruleus using ultra-high field MRI, revealing spatial gradients and providing a resource for future research.

This study evaluates the reproducibility of 7T quantitative susceptibility mapping (QSM) and R2* at multiple sites using a harmonized protocol, finding improved consistency compared to 3T.

This study investigates how Tau pathology in early Alzheimer's disease affects neurophysiological network dynamics using MEG and PET scans, finding frequency-specific correlations between Tau levels and network efficiency and segregation.

This study investigates the neural mechanisms of visuomotor decisions by combining accumulation-to-threshold models with EEG, revealing simultaneous forward and backward information flow between sensory and motor regions.

The DPUK Data Portal provides secure access to cohort data for dementia research, supporting multi-modal analyses and collaborations across multiple institutions.

This study links apathy to reduced precision of prior beliefs about action outcomes using a Bayesian framework and a visuomotor task.