MRI-based studies

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Regular voluntary exercise has been shown to increase waste transport through the glymphatic system in mice. Here, we investigate the impact of physical exercise on both upstream and downstream brain waste clearance in healthy volunteers via noninvasive MR imaging. Putative glymphatic influx, evaluated using intravenous contrast-enhanced dynamic T1 mapping, increases significantly at the putamen after 12 weeks of long-term exercise using a cycle ergometer. The putative meningeal lymphatic vessel size and flow, measured by intravenous contrast-enhanced black-blood imaging and IR-ALADDIN technique, increase significantly after long-term exercise. Plasma proteomics reveals significant changes in inflammation-related and immune-related proteins (down-regulated: S100A8, S100A9, PSMA3, and DEFA1A3; up-regulated: J chain) after long-term exercise, which correlate with putative glymphatic influx or mLV flow. Our results suggest that increased glymphatic and mLV flow may be the potential mechanism underlying the neuroprotective effects of exercise on cognition, highlighting the importance of long-term, regular exercise.

Background: Blood-brain barrier (BBB) permeability change is a possible pathologic mechanism of autoimmune encephalitis.

Purpose: To evaluate the change in BBB permeability in patients with autoimmune encephalitis as compared with healthy controls by using dynamic contrast-enhanced (DCE) MRI and to explore its predictive value for treatment response in patients.

Materials and Methods: This single-center retrospective study included consecutive patients with probable or possible autoimmune encephalitis and healthy controls who underwent DCE MRI between April 2020 and May 2021. Automatic volumetric segmentation was performed on three-dimensional T1-weighted images, and volume transfer constant (Ktrans) values were calculated at encephalitis-associated brain regions. Ktrans values were compared between the patients and controls, with adjustment for age and sex with use of a nonparametric approach. The Wilcoxon rank sum test was performed to compare Ktrans values of the good (improvement in modified Rankin Scale [mRS] score of at least two points or achievement of an mRS score of ≤2) and poor (improvement in mRS score of less than two points and achievement of an mRS score >2) treatment response groups among the patients.

Results: Thirty-eight patients with autoimmune encephalitis (median age, 38 years [IQR, 29-59 years]; 20 [53%] female) and 17 controls (median age, 71 years [IQR, 63-77 years]; 12 [71%] female) were included. All brain regions showed higher Ktrans values in patients as compared with controls (P < .001). The median difference in Ktrans between the patients and controls was largest in the right parahippocampal gyrus (25.1 × 10-4 min-1 [95% CI: 17.6, 43.4]). Among patients, the poor treatment response group had higher baseline Ktrans values in both cerebellar cortices (P = .03), the left cerebellar cortex (P = .02), right cerebellar cortex (P = .045), left cerebral cortex (P = .045), and left postcentral gyrus (P = .03) than the good treatment response group. 

Conclusion: DCE MRI demonstrated that BBB permeability was increased in all brain regions in patients with autoimmune encephalitis as compared with controls, and baseline Ktrans values were higher in patients with poor treatment response in the cerebellar cortex, left cerebral cortex, and left postcentral gyrus as compared with the good response group.

The glymphatic system plays a crucial role in brain waste clearance, with glymphatic magnetic resonance imaging (MRI) techniques highlighting its significance in understanding neurodegenerative diseases. This review emphasizes the intricate relationship between sleep, the glymphatic system, and the onset of conditions such as Alzheimer's disease (AD), idiopathic normal pressure hydrocephalus (iNPH), Parkinson's disease (PD), and other neurological diseases. Key findings revealed that sleep disruptions can impair the glymphatic system and potentially accelerate the progression of neurodegenerative diseases. In AD, amyloid β plaque accumulation correlates with glymphatic dysfunction, while in iNPH, impaired glymphatic functionality may result in waste accumulation, such as amyloid-beta accumulation in AD. Research on PD has underscored the potential role of the glymphatic system in α-synuclein clearance. In conclusion, as we delve into the glymphatic system using MRI techniques, we anticipate a richer understanding of neurodegenerative diseases, offering prospects for innovative therapeutic interventions.

The glymphatic system plays a crucial role in brain waste clearance, with glymphatic magnetic resonance imaging (MRI) techniques highlighting its significance in understanding neurodegenerative diseases. This review emphasizes the intricate relationship between sleep, the glymphatic system, and the onset of conditions such as Alzheimer's disease (AD), idiopathic normal pressure hydrocephalus (iNPH), Parkinson's disease (PD), and other neurological diseases. Key findings revealed that sleep disruptions can impair the glymphatic system and potentially accelerate the progression of neurodegenerative diseases. In AD, amyloid β plaque accumulation correlates with glymphatic dysfunction, while in iNPH, impaired glymphatic functionality may result in waste accumulation, such as amyloid-beta accumulation in AD. Research on PD has underscored the potential role of the glymphatic system in α-synuclein clearance. In conclusion, as we delve into the glymphatic system using MRI techniques, we anticipate a richer understanding of neurodegenerative diseases, offering prospects for innovative therapeutic interventions.

Adult-type diffuse glioma (grade 4) has infiltrating nature, and therefore local progression is likely to occur within surrounding non-enhancing T2 hyperintense areas even after gross total resection of contrast-enhancing lesions. Cerebral blood volume (CBV) obtained from dynamic susceptibility contrast perfusion-weighted imaging (DSC-PWI) is a parameter that is well-known to be a surrogate marker of both histologic and angiographic vascularity in tumors. We built two nnU-Net deep learning models for prediction of early local progression in adult-type diffuse glioma (grade 4), one using conventional MRI alone and one using multiparametric MRI, including conventional MRI and DSC-PWI. Local progression areas were annotated in a non-enhancing T2 hyperintense lesion on preoperative T2 FLAIR images, using the follow-up contrast-enhanced (CE) T1-weighted (T1W) images as the reference standard. The sensitivity was doubled with the addition of nCBV (80% vs. 40%, P = 0.02) while the specificity was decreased nonsignificantly (29% vs. 48%, P = 0.39), suggesting that fewer cases of early local progression would be missed with the addition of nCBV. While the diagnostic performance of CBV model is still poor and needs improving, the multiparametric deep learning model, which presumably learned from the subtle difference in vascularity between early local progression and non-progression voxels within perilesional T2 hyperintensity, may facilitate risk-adapted radiotherapy planning in adult-type diffuse glioma (grade 4) patients.

Background: Evaluation of the glymphatic system with intrathecal contrast material injection has limited clinical use.

Purpose: To investigate the feasibility of using serial intravenous contrast-enhanced T1 mapping in the quantitative evaluation of putative dynamic glymphatic activity in various brain regions and to demonstrate the effect of sleep on glymphatic activity in humans.

Materials and Methods: In this prospective study from May 2019 to February 2020, 25 healthy participants (mean age, 25 years ± 2 [standard deviation]; 15 men) underwent two cycles of MRI (day and night cycles). For each cycle, T1 maps were acquired at baseline and 0.5, 1, 1.5, 2, and 12 hours after intravenous contrast material injection. For the night cycle, participants had a normal night of sleep between 2 and 12 hours. The time (tmin) to reach the minimum T1 value (T1min), the absolute difference between baseline T1 and T1min (peak ΔT1), and the slope between two measurements at 2 and 12 hours (slope[2h-12h]) were determined from T1 value-time curves in cerebral gray matter (GM), cerebral white matter (WM), cerebellar GM, cerebellar WM, and putamen. Mixed-model analysis of variance (ANOVA), Friedman test, and repeated-measures ANOVA were used to assess the effect of sleep on slope(2h-12h) and to compare tmin and peak ΔT1 among different regions.

Results: The slope(2h-12h) increased from the day to night cycles in cerebral GM, cerebellar GM, and putamen (geometric mean ratio [night/day] = 1.4 [95% CI: 1.2, 1.7], 1.3 [95% CI: 1.1, 1.4], and 2.4 [95% CI: 1.6, 3.6], respectively; P = .001, P < .001, and P < .001, respectively). Median tmin values were 0.5 hour in cerebral and cerebellar GM and putamen for both cycles. Cerebellar GM had the highest mean peak ΔT1, followed by cerebral GM and putamen in both day (159 msec ± 6, 99 msec ± 4, and 62 msec ± 5, respectively) and night (152 msec ± 6, 104 msec ± 6, and 58 msec ± 4, respectively) cycles. 

Conclusion: Clearance of a gadolinium-based contrast agent was greater after sleep compared with daytime wakefulness. These results suggest that sleep was associated with greater glymphatic clearance compared with wakefulness. 

Background: Pharmacokinetic (PK) parameters obtained from dynamic contrast agent-enhanced (DCE) MRI evaluates the microcirculation permeability of astrocytomas, but the unreliability from arterial input function (AIF) remains a challenge.

Purpose: To develop a deep learning model that improves the reliability of AIF for DCE MRI and to validate the reliability and diagnostic performance of PK parameters by using improved AIF in grading astrocytomas.

Materials and Methods: This retrospective study included 386 patients (mean age, 52 years ± 16 [standard deviation]; 226 men) with astrocytomas diagnosed with histopathologic analysis who underwent dynamic susceptibility contrast (DSC)-enhanced and DCE MRI preoperatively from April 2010 to January 2018. The AIF was obtained from each sequence: AIF obtained from DSC-enhanced MRI (AIFDSC) and AIF measured at DCE MRI (AIFDCE). The model was trained to translate AIFDCE into AIFDSC, and after training, outputted neural-network-generated AIF (AIFgenerated DSC) with input AIFDCE. By using the three different AIFs, volume transfer constant (Ktrans), fractional volume of extravascular extracellular space (Ve), and vascular plasma space (Vp) were averaged from the tumor areas in the DCE MRI. To validate the model, intraclass correlation coefficients and areas under the receiver operating characteristic curve (AUCs) of the PK parameters in grading astrocytomas were compared by using different AIFs.

Results: The AIF-generated, DSC-derived PK parameters showed higher AUCs in grading astrocytomas than those derived from AIFDCE (mean Ktrans, 0.88 [95% confidence interval {CI}: 0.81, 0.93] vs 0.72 [95% CI: 0.63, 0.79], P = .04; mean Ve, 0.87 [95% CI: 0.79, 0.92] vs 0.70 [95% CI: 0.61, 0.77], P = .049, respectively). Ktrans and Ve showed higher intraclass correlation coefficients for AIFgenerated DSC than for AIFDCE (0.91 vs 0.38, P < .001; and 0.86 vs 0.60, P < .001, respectively). In AIF analysis, baseline signal intensity (SI), maximal SI, and wash-in slope showed higher intraclass correlation coefficients with AIFgenerated DSC than AIFDCE (0.77 vs 0.29, P < .001; 0.68 vs 0.42, P = .003; and 0.66 vs 0.45, P = .01, respectively.

Conclusion: A deep learning algorithm improved both reliability and diagnostic performance of MRI pharmacokinetic parameters for differentiating astrocytoma grades.  

Background: Recent studies showed the possible association between inflammation-induced blood-brain barrier (BBB) structural changes followed by greater permeability of the BBB and chronic pain. Thus, measurement of BBB breakdown would be a valuable aid in the diagnosis in migraine. Dynamic contrast material-enhanced (DCE) MRI can determine perfusion and permeability properties related to the BBB.

Purpose: To evaluate the relationship between permeability of the BBB in migraine-associated brain regions by using DCE MRI.

Materials and Methods: In this prospective study, from September 2016 to December 2017, 56 study participants underwent DCE MRI after gadobutrol administration and were classified into migraine (n = 35) and healthy control (n = 21) groups. Automatic volumetric segmentation was performed on the pre-contrast-enhanced T1-weighted images by using FreeSurfer, and migraine-associated brain region masks were extracted by using the software NordicICE. The corresponding maps for pharmacokinetic parameters Ktrans (the volume transfer constant) and Vp (the fractional plasma volume) were coregistered with the region-of-interest masks, and their mean values of corresponding total volume of interest were calculated. For comparison analyses, the Mann-Whitney tests were used. Receiver operating characteristic curve analysis and Spearman rank correlation tests were used to identify correlations between clinical characteristics and the aforementioned perfusion parameters.

Results: Mean age was younger in the migraine group (mean ± standard deviation, 57 years ± 12) than in the healthy control group (mean, 71 years ± 8) (P < .001). In the migraine group, the mean value of Vp in the left amygdala (median, 0.27 mL/100 g) was lower than that in the healthy control group (median, 0.39 mL/100 g) (P = .04). The mean value of Vp in the left amygdala was correlated with the intensity of headache attack in participants with migraine (correlation coefficient, -0.34; P = .04).

Conclusion: Lower fractional plasma volume in the left amygdala was observed in participants with migraine than in healthy participants. 

Purpose: To explore the role of histogram analysis of apparent diffusion coefficient (ADC) maps obtained at standard- and high-b-value (1000 and 3000 sec/mm2, respectively) diffusion-weighted (DW) imaging in the differentiation of true progression from pseudoprogression in glioblastoma treated with radiation therapy and concomitant temozolomide.

Materials and Methods: This retrospective study was approved by the institutional review board of Seoul National University Hospital, and informed consent requirement was waived. Thirty patients with histopathologically proved glioblastoma who had undergone concurrent chemotherapy and radiation therapy (CCRT) with temozolomide underwent diffusion-weighted MR imaging with b values of 1000 and 3000 sec/mm2, and corresponding ADC maps were calculated from entire newly developed or enlarged enhancing lesions after completion of CCRT. Histogram parameters of each ADC map between true progression (n = 15) and pseudoprogression (n = 15) groups were compared by using the unpaired Student t test. Receiver operating characteristic analysis was used to determine the best cutoff values for predictors in the differentiation of true progression from pseudoprogression. Results were validated in an independent test set of nine patients by using the best cutoff value to predict differentiation of true progression from pseudoprogression. The accuracy of the selected best cutoff value in the independent test set was then calculated.

Results: In terms of cumulative histograms, the fifth percentile of both ADC at b value of 1000 sec/mm2 (ADC1000) and the ADC at b value of 3000 sec/mm2 (ADC3000) were significantly lower in the true progression group than in the pseudoprogression group (P = .049 and P < .001, respectively). In contrast, neither the mean ADC1000 nor the mean ADC3000 was significantly different between the two groups. The diagnostic values of the parameters derived from ADC1000 and ADC3000 were compared, and a significant difference (0.224, P = .016) was found between the area under the receiver operating characteristic curve of the fifth percentile for ADC1000 and that for ADC3000. The accuracies were 66.7% (six of nine patients) and 88.9% (eight of nine patients) based on the fifth percentile of both ADC1000 and ADC3000 in the independent test set, respectively.

Conclusion: The fifth percentile of the cumulative ADC histogram obtained at a high b value was the most promising parameter in the differentiation of true progression from pseudoprogression of the newly developed or enlarged enhancing lesions after CCRT with temozolomide for glioblastoma treatment.

Purpose: To explore the role of histogram analysis of apparent diffusion coefficient (ADC) maps based on entire tumor volume data in determining glioma grade and to evaluate the diagnostic performance of ADC maps at standard (1000 sec/mm2) and high (3000 sec/mm2) b values.

Materials and Methods: This retrospective study was approved by the institutional review board, and informed consent was waived. Twenty-seven patients with astrocytic tumors underwent diffusion-weighted magnetic resonance imaging with b values of 1000 and 3000 sec/mm2, and the corresponding ADC maps were calculated (ADC1000 and ADC3000, respectively). Regions of interest containing the lesion were drawn on every section of the ADC map containing the tumor and were summated to derive volume-based data of the entire tumor. Histogram parameters were correlated with tumor grade by using repeated measurements analysis of variance, the Tukey-Kramer test for post hoc comparisons, and an unpaired Student t test. Receiver operating characteristic (ROC) curves were constructed to determine the optimum threshold for each histogram parameter, and sensitivity and specificity were assessed.

Results: Minimum ADC1000 and ADC3000 both decreased with increasing tumor grade. The 50th and 75th percentiles of cumulative ADC1000 histograms showed significant differences between grades (P = .015 and .001, respectively), while the fifth and 75th percentiles of cumulative ADC3000 histograms showed such differences (P = .015 and .014, respectively). Minimum ADC and the fifth percentile for both ADC1000 (P < .001 and P = .024, respectively) and ADC3000 (P < .001 and P = .001, respectively) proved to be significant histogram parameters for differentiating high- from low-grade gliomas. The diagnostic value of the parameters derived from ADC1000 and ADC3000 were compared, and a significant difference (0.202, P = .014) was found between the areas under the ROC curve of the fifth percentiles for ADC1000 and ADC3000.

Conclusion: Histogram analysis of ADC maps based on entire tumor volume can be a useful tool for grading gliomas. The fifth percentile of the cumulative ADC histogram obtained at a high b value was the most promising parameter for differentiating high- from low-grade gliomas.