METHODS: Five APT quantification methods, including asymmetry analysis and its variants as well as two Lorentzian model-based methods, were applied to data acquired from six rats that underwent middle cerebral artery occlusion scanned at 9.4T. Diffusion and perfusion-weighted images, and water relaxation time maps were also acquired to study the relationship of these conventional imaging modalities with the different APT quantification methods.
RESULTS: The APT ischemic area estimates had varying sizes (Jaccard index: 0.544 ≤ J ≤ 0.971) and had varying correlations in their distributions (Pearson correlation coefficient: 0.104 ≤ r ≤ 0.995), revealing discrepancies in the quantified ischemic areas. The Lorentzian methods produced the highest contrast-to-noise ratios (CNRs; 1.427 ≤ CNR ≤ 2.002), but generated APT ischemic areas that were comparable in size to the cerebral blood flow (CBF) deficit areas; asymmetry analysis and its variants produced APT ischemic areas that were smaller than the CBF deficit areas but larger than the apparent diffusion coefficient deficit areas, though having lower CNRs (0.561 ≤ CNR ≤ 1.083).
CONCLUSION: There is a need to further investigate the accuracy and correlation of each quantification method with the pathophysiology using a larger scale multi-imaging modality and multi-time-point clinical study. Future studies should include the magnetization transfer ratio asymmetry results alongside the findings of the study to facilitate the comparison of results between different centers and also the published literature.
OBJECTIVE: The current review was aimed to present a comprehensive overview and critical appraisal of majorly employed neuroimaging techniques for rational diagnosis and effective monitoring of effectiveness of employed therapeutic intervention for NPH. Moreover, a critical overview of recent developments and utilization of pharmacological agents for treatment of hydrocephalus has also been appraised.
RESULTS: Considering the complications associated with the shunt-based surgical operations, consistent monitoring of shunting via neuroimaging techniques hold greater clinical significance. Despite having extensive applicability of MRI and CT scan, these conventional neuroimaging techniques are associated with misdiagnosis or several health risks to patients. Recent advances in MRI (i.e., Sagittal-MRI, coronal-MRI, Time-SLIP (time-spatial-labeling-inversion-pulse), PC-MRI and diffusion-tensor-imaging (DTI)) have shown promising applicability in diagnosis of NPH. Having associated with several adverse effects with surgical interventions, non-invasive approaches (pharmacological agents) have earned greater interest of scientists, medical professional, and healthcare providers. Amongst pharmacological agents, diuretics, isosorbide, osmotic agents, carbonic anhydrase inhibitors, glucocorticoids, NSAIDs, digoxin, and gold-198 have been employed for management of NPH and prevention of secondary sensory/intellectual complications.
CONCLUSION: Employment of rational diagnostic tool and therapeutic modalities avoids misleading diagnosis and sophisticated management of hydrocephalus by efficient reduction of cerebrospinal fluid (CSF) production, reduction of fibrotic and inflammatory cascades secondary to meningitis and hemorrhage, and protection of brain from further deterioration.
NEW METHOD: To overcome these limitations, we propose a new statistical model that smooths out the noise by exploiting the geometric structure of correlation matrices. The dynamic correlation matrix is modeled as a linear combination of symmetric positive-definite matrices combined with cosine series representation. The resulting smoothed dynamic correlation matrices are clustered into disjoint brain connectivity states using the k-means clustering algorithm.
RESULTS: The proposed model preserves the geometric structure of underlying physiological dynamic correlation, eliminates unwanted noise in connectivity and obtains more accurate state spaces. The difference in the estimated dynamic connectivity states between males and females is identified.
COMPARISON WITH EXISTING METHODS: We demonstrate that the proposed statistical model has less rapid state changes caused by noise and improves the accuracy in identifying and discriminating different states.
CONCLUSIONS: We propose a new regression model on dynamically changing correlation matrices that provides better performance over existing windowed correlation and is more reliable for the modeling of dynamic connectivity.