ArticleJournal of visualized experiments : JoVE2013
Identification of disease-related spatial covariance patterns using neuroimaging data.
Article in Journal of visualized experiments : JoVE, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 47 papers.
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Who cites it
47 citing papers in PubMed, 72 citations in OpenAlex.
- Predictive Value ofJournal of nuclear medicine : official publication, Society of Nuclear Medicine · 2020Trial
- Abnormal metabolic pattern associated with cognitive impairment in Parkinson's disease: a validation study.Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism · 2015Trial
- Network modulation following sham surgery in Parkinson's disease.The Journal of clinical investigation · 2014Trial
- Alterations in relative perfusion and synaptic patterns in Parkinson's disease using dynamic SV2A PET.medRxiv : the preprint server for health sciences · 2026Article
- Investigating AD and non-AD [European journal of nuclear medicine and molecular imaging · 2026Article
- Different brain spatial metabolic patterns characterizing different subtypes of multiple system atrophy.Journal of Parkinson's disease · 2026Article
- A data-driven SSM/PCA analysis approach for differential diagnosis of parkinsonism usingNeuroImage. Clinical · 2026Article
- Positron Emission Tomography Imaging in Clinical Trials for Parkinson's Disease: Applications of Metabolic Brain Network Approach.Movement disorders : official journal of the Movement Disorder Society · 2025Review
- Cerebral perfusion correlates with amyloid deposition in patients with mild cognitive impairment due to Alzheimer's disease.The journal of prevention of Alzheimer's disease · 2025Article
- Molecular connectivity studies in neurotransmission: a scoping review.Imaging neuroscience (Cambridge, Mass.) · 2025Article
- Whole-brain glucose metabolic pattern differentiates minimally conscious state from unresponsive wakefulness syndrome.CNS neuroscience & therapeutics · 2024Article
- Article
- The metabolic spatial covariance pattern of definite idiopathic normal pressure hydrocephalus: an FDG PET study with principal components analysis.Alzheimer's research & therapy · 2023Article
- ＜Editors' Choice＞ Pattern of THK 5351 retention in normal aging involves core regions of resting state networks associated with higher cognitive function.Nagoya journal of medical science · 2023Article
- Longitudinal evolution of cortical thickness signature reflecting Lewy body dementia in isolated REM sleep behavior disorder: a prospective cohort study.Translational neurodegeneration · 2023Article
- The identification and cognitive correlation of perfusion patterns measured with arterial spin labeling MRI in Alzheimer's disease.Alzheimer's research & therapy · 2023Article
- Parkinson's disease progression: Increasing expression of an invariant common core subnetwork.NeuroImage. Clinical · 2023Article
- Parkinson's Disease-Related Brain Metabolic Pattern Is Expressed in Schizophrenia Patients during Neuroleptic Drug-Induced Parkinsonism.Diagnostics (Basel, Switzerland) · 2022Article
- Widespread white matter oedema in subacute COVID-19 patients with neurological symptoms.Brain : a journal of neurology · 2022Article
- Discussion of Research Priorities for Gait Disorders in Parkinson's Disease.Movement disorders : official journal of the Movement Disorder Society · 2022Review
Corrections and comments
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Authors and funding
7 authors at 2 institutions in 1 country.
Funding
Abstract
The scaled subprofile model (SSM)(1-4) is a multivariate PCA-based algorithm that identifies major sources of variation in patient and control group brain image data while rejecting lesser components (Figure 1). Applied directly to voxel-by-voxel covariance data of steady-state multimodality images, an entire group image set can be reduced to a few significant linearly independent covariance patterns and corresponding subject scores. Each pattern, termed a group invariant subprofile (GIS), is an orthogonal principal component that represents a spatially distributed network of functionally interrelated brain regions. Large global mean scalar effects that can obscure smaller network-specific contributions are removed by the inherent logarithmic conversion and mean centering of the data(2,5,6). Subjects express each of these patterns to a variable degree represented by a simple scalar score that can correlate with independent clinical or psychometric descriptors(7,8). Using logistic regression analysis of subject scores (i.e. pattern expression values), linear coefficients can be derived to combine multiple principal components into single disease-related spatial covariance patterns, i.e. composite networks with improved discrimination of patients from healthy control subjects(5,6). Cross-validation within the derivation set can be performed using bootstrap resampling techniques(9). Forward validation is easily confirmed by direct score evaluation of the derived patterns in prospective datasets(10). Once validated, disease-related patterns can be used to score individual patients with respect to a fixed reference sample, often the set of healthy subjects that was used (with the disease group) in the original pattern derivation(11). These standardized values can in turn be used to assist in differential diagnosis(12,13) and to assess disease progression and treatment effects at the network level(7,14-16). We present an example of the application of this methodology to FDG PET data of Parkinson's Disease patients and normal controls using our in-house software to derive a characteristic covariance pattern biomarker of disease.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.