A Functional MRI and Magneto/Electro Source Imaging Procedure for Cognitive and Pre-surgical Evaluation

AbstractAnalysis of normal/pathological brain activity using neuroimaging methods is necessary to avoid operation risks, and the outcome serves as prior information for surgical neuronavigation. We present an fMRI/MEG/EEG-based methodology for tasks demanding mainly sensorimotor and visual/cognitive responses. This consists of carefully selected/designed stimulation paradigms and statistical parametric mapping methods that demonstrate the practicability of these techniques for clinical applications. The results replicate known findings in the brain-imaging field, with the improvement that our analyses are restricted to grey matter tissue. The latter enhance computations, which is advantageous for the massive data analyses that are typical of clinical and radiological functional brain “checkup” services

Cluster-permutation statistical analysis for high-dimensional brain-wide functional connectivity mapping

Brain functional connectivity (FC) analyses based on magnetoencephalographic (MEG) signals have yet to exploit the intrinsic high-dimensional information. Typically, these analyses are constrained to regions of interest to avoid the curse of dimensionality, which leads to conservative hypothesis testing. We removed such constraint by extending cluster-permutation statistics for high-dimensional MEG-FC analyses. We demonstrated the feasibility of this approach by identifying MEG-FC resting-state changes in mild cognitive impairment (MCI), a prodromal stage of Alzheimer’s disease. We found dense clusters of increased connectivity strength in MCI compared to healthy controls (hypersynchronization), in delta (1-4 Hz) and higher-theta (6-8 Hz) bands oscillations. These clusters mainly consisted of interactions between occipitofrontal and occipitotemporal regions in the left hemisphere and could potentially be used as neuromarkers of early progression in Alzheimer’s disease. Our novel approach can be used to generate high-resolution statistical FC maps for neuroimaging studies in general

Computational neurology: computational modeling approaches in Dementia

Dementia is a collection of symptoms associated with impaired cognition and impedes everyday normal functioning. Dementia, with Alzheimer’s disease constituting its most common type, is highly complex in terms of etiology and pathophysiology. A more quantitative or computational attitude towards dementia research, or more generally in neurology, is becoming necessary – Computational Neurology. We provide a focused review of some computational approaches that have been developed and applied to the study of dementia, particularly Alzheimer’s disease. Both mechanistic modeling and data-driven, including AI or machine learning, approaches are discussed. Linkage to clinical decision support systems for dementia diagnosis will also be discussed