Purpose
Parkinson’s disease (PD) is primarily defined by motor symptoms and is associated with alterations of sensorimotor areas. Evidence for network changes of the sensorimotor network (SMN) in PD ...is inconsistent and a systematic evaluation of SMN in PD yet missing. We investigate functional connectivity changes of the SMN in PD, both, within the network, and to other large-scale connectivity networks.
Methods
Resting-state fMRI was assessed in 38 PD patients under long-term dopaminergic treatment and 43 matched healthy controls (HC). Independent component analysis (ICA) into 20 components was conducted and the SMN was identified within the resulting networks. Functional connectivity within the SMN was analyzed using a dual regression approach. Connectivity between the SMN and the other networks from group ICA was investigated with FSLNets. We investigated for functional connectivity changes between patients and controls as well as between medication states (OFF vs. ON) in PD and for correlations with clinical parameters.
Results
There was decreased functional connectivity within the SMN in left inferior parietal and primary somatosensory cortex in PD OFF. Across networks, connectivity between SMN and two motor networks as well as two visual networks was diminished in PD OFF. All connectivity decreases partially normalized in PD ON.
Conclusion
PD is accompanied by functional connectivity losses of the SMN, both, within the network and in interaction to other networks. The connectivity changes in short- and long-range connections are probably related to impaired sensory integration for motor function in PD. SMN decoupling can be partially compensated by dopaminergic therapy.
Abstract This study aimed to (1) replicate a deep-learning-based model for cerebral aneurysm segmentation in TOF-MRAs, (2) improve the approach by testing various fully automatic pre-processing ...pipelines, and (3) rigorously validate the model’s transferability on independent, external test-datasets. A convolutional neural network was trained on 235 TOF-MRAs acquired on local scanners from a single vendor to segment intracranial aneurysms. Different pre-processing pipelines including bias field correction, resampling, cropping and intensity-normalization were compared regarding their effect on model performance. The models were tested on independent, external same-vendor and other-vendor test-datasets, each comprised of 70 TOF-MRAs, including patients with and without aneurysms. The best-performing model achieved excellent results on the external same-vendor test-dataset, surpassing the results of the previous publication with an improved sensitivity (0.97 vs. ~ 0.86), a higher Dice score coefficient (DSC, 0.60 ± 0.25 vs. 0.53 ± 0.31), and an improved false-positive rate (0.87 ± 1.35 vs. ~ 2.7 FPs/case). The model further showed excellent performance in the external other-vendor test-datasets (DSC 0.65 ± 0.26; sensitivity 0.92, 0.96 ± 2.38 FPs/case). Specificity was 0.38 and 0.53, respectively. Raising the voxel-size from 0.5 × 0.5×0.5 mm to 1 × 1×1 mm reduced the false-positive rate seven-fold. This study successfully replicated core principles of a previous approach for detecting and segmenting cerebral aneurysms in TOF-MRAs with a robust, fully automatable pre-processing pipeline. The model demonstrated robust transferability on two independent external datasets using TOF-MRAs from the same scanner vendor as the training dataset and from other vendors. These findings are very encouraging regarding the clinical application of such an approach.
Background
Defacing has become mandatory for anonymization of brain MRI scans; however, concerns regarding data integrity were raised. Thus, we systematically evaluated the effect of different ...defacing procedures on automated brain atrophy estimation.
Methods
In total, 268 Alzheimer’s disease patients were included from ADNI, which included unaccelerated (
n
= 154), within-session unaccelerated repeat (
n
= 67) and accelerated 3D T1 imaging (
n
= 114).
Atrophy maps were computed using the open-source software veganbagel for every original, unmodified scan and after defacing using afni_refacer, fsl_deface, mri_deface, mri_reface, PyDeface or spm_deface, and the root-mean-square error (RMSE) between z-scores was calculated.
RMSE values derived from unaccelerated and unaccelerated repeat imaging served as a benchmark. Outliers were defined as RMSE > 75th percentile and by using Grubbs’s test.
Results
Benchmark RMSE was 0.28 ± 0.1 (range 0.12–0.58, 75th percentile 0.33).
Outliers were found for unaccelerated and accelerated T1 imaging using the 75th percentile cutoff: afni_refacer (unaccelerated: 18, accelerated: 16), fsl_deface (unaccelerated: 4, accelerated: 18), mri_deface (unaccelerated: 0, accelerated: 15), mri_reface (unaccelerated: 0, accelerated: 2) and spm_deface (unaccelerated: 0, accelerated: 7). PyDeface performed best with no outliers (unaccelerated mean RMSE 0.08 ± 0.05, accelerated mean RMSE 0.07 ± 0.05).
The following outliers were found according to Grubbs’s test: afni_refacer (unaccelerated: 16, accelerated: 13), fsl_deface (unaccelerated: 10, accelerated: 21), mri_deface (unaccelerated: 7, accelerated: 20), mri_reface (unaccelerated: 7, accelerated: 6), PyDeface (unaccelerated: 5, accelerated: 8) and spm_deface (unaccelerated: 10, accelerated: 12).
Conclusion
Most defacing approaches have an impact on atrophy estimation, especially in accelerated 3D T1 imaging. Only PyDeface showed good results with negligible impact on atrophy estimation.
Different imaging sequences (T1 etc.) depict different aspects of a brain tumor. As clinical MRI examinations of the brain might be terminated prematurely, not all sequences may be acquired, ...decreasing the performance of automated tumor segmentation. We attempt to optimize the order of sequences, to maximize information gain in case of incomplete examination.
For segmentation we used the winner algorithm of the Brain Tumor Segmentation challenge 2018, trained on the BraTS 2020 dataset, with the objective to segment necrotic core, peritumoral edema, and enhancing tumor. We compared the segmentation performance for all combinations of sequences, using the Dice score (DS) as the primary metric. We compare the results with those which would be obtained by attempting to follow the consensus recommendations for brain tumor imaging T1, FLAIR, T2, T1CE.
The average segmentation accuracy varies between 0.476 for T1 only and 0.751 for the full set of sequences. T1CE has a high information content, even regarding peritumoral edema and information of T2 and FLAIR were highly redundant. The optimal order of sequences appears to be T1, T2, T1CE, FLAIR. Comparing segmentation accuracy after each fully acquired sequence, the first sequence (T1) is the same for both, DS for T1, T2 (proposed) is 6.2% higher than T1, FLAIR (aborted recommendations), and T1, T2, T1CE (proposed) is 34.8% higher than T1, FLAIR, T2 (aborted recommendations).
For the purpose of optimal deep-learning-based segmentation purposes in potentially incomplete MRI examinations, the T1CE sequence should be acquired as early as possible.
Background:
Normative brain volume reports (NBVR) are becoming more available in the work-up of patients with suspected dementia disorders, potentially leveraging the value of structural MRI in ...clinical settings. The present study aims to investigate the impact of NBVRs on the diagnosis of neurodegenerative dementia disorders in real-world clinical practice.
Methods:
We retrospectively analyzed data of 112 memory clinic patients, who were consecutively referred for MRI and 18F-fluorodeoxyglucose (FDG) positron emission tomography (PET) during a 12-month period. Structural MRI was assessed by two residents with 2 and 3 years of neuroimaging experience. Statements and diagnostic confidence regarding the presence of a neurodegenerative disorder in general (first level) and Alzheimer’s disease (AD) pattern in particular (second level) were recorded without and with NBVR information. FDG-PET served as the reference standard.
Results:
Overall, despite a trend towards increased accuracy, the impact of NBVRs on diagnostic accuracy was low and non-significant. We found a significant drop of sensitivity (0.75–0.58;
p
< 0.001) and increase of specificity (0.62–0.85;
p
< 0.001) for rater 1 at identifying patients with neurodegenerative dementia disorders. Diagnostic confidence increased for rater 2 (
p
< 0.001).
Conclusions:
Overall, NBVRs had a limited impact on diagnostic accuracy in real-world clinical practice. Potentially, NBVR might increase diagnostic specificity and confidence of neuroradiology residents. To this end, a well-defined framework for integration of NBVR in the diagnostic process and improved algorithms of NBVR generation are essential.
Age-related cognitive decline varies greatly in healthy older adults, which may partly be explained by differences in the functional architecture of brain networks. Resting-state functional ...connectivity (RSFC) derived network parameters as widely used markers describing this architecture have even been successfully used to support diagnosis of neurodegenerative diseases. The current study aimed at examining whether these parameters may also be useful in classifying and predicting cognitive performance differences in the normally aging brain by using machine learning (ML). Classifiability and predictability of global and domain-specific cognitive performance differences from nodal and network-level RSFC strength measures were examined in healthy older adults from the 1000BRAINS study (age range: 55–85 years). ML performance was systematically evaluated across different analytic choices in a robust cross-validation scheme. Across these analyses, classification performance did not exceed 60% accuracy for global and domain-specific cognition. Prediction performance was equally low with high mean absolute errors (
s ≥ 0.75) and low to none explained variance (
≤ 0.07) for different cognitive targets, feature sets, and pipeline configurations. Current results highlight limited potential of functional network parameters to serve as sole biomarker for cognitive aging and emphasize that predicting cognition from functional network patterns may be challenging.
In recent years, new insights into brain network communication related to cognitive performance differences in older age have been gained. Simultaneously, an increasing number of studies has turned to machine learning (ML) approaches for the development of biomarkers in health and disease. Given the increasing aging population and the impact cognition has on the quality of life of older adults, automated markers for cognitive aging gain importance. This study addressed the classification and prediction power of resting-state functional connectivity (RSFC) strength measures for cognitive performance in healthy older adults using a battery of standard ML approaches. Classifiability and predictability of cognitive abilities was found to be low across analytic choices. Results emphasize limited potential of these metrics as sole biomarker for cognitive aging.
The concept of early brain injury (EBI) is based on the assumption of a global reduction in brain perfusion following aneurysmal subarachnoid hemorrhage (aSAH). However, the heterogeneity of computed ...tomography perfusion (CTP) imaging in EBI has not yet been investigated. In contrast, increased mean transit time (MTT) heterogeneity, a possible marker of microvascular perfusion heterogeneity, in the delayed cerebral ischemia (DCI) phase has recently been associated with a poor neurological outcome after aSAH. Therefore, in this study, we investigated whether the heterogeneity of early CTP imaging in the EBI phase is an independent predictor of the neurological outcome after aSAH. We retrospectively analyzed the heterogeneity of the MTT using the coefficient of variation (cvMTT) in early CTP scans (within 24 h after ictus) of 124 aSAH patients. Both linear and logistic regression were used to model the mRS outcome, which were treated as numerical and dichotomized values, respectively. Linear regression was used to investigate the linear dependency between the variables. No significant difference in cvMTT between the patients with and those without EVD could be observed (
= 0.69). We found no correlation between cvMTT in early CTP imaging and initial modified Fisher (
= 0.07) and WFNS grades (
= 0.23). The cvMTT in early perfusion imaging did not correlate significantly with the 6-month mRS for the entire study population (
= 0.15) or for any of the subgroups (without EVD:
= 0.21; with EVD:
= 0.3). In conclusion, microvascular perfusion heterogeneity, assessed by the heterogeneity of MTT in early CTP imaging, does not appear to be an independent predictor of the neurological outcome 6 months after aSAH.
Patients suffering from Parkinson's disease (PD) often show impairments in executive function (EF) like decision-making and action control. The right dorsolateral prefrontal cortex (dlPFC) has been ...strongly implicated in EF in healthy subjects and has repeatedly been reported to show alterations related to EF impairment in PD. Recently, two key regions for cognitive action control have been identified within the right dlPFC by co-activation based parcellation. While the posterior region is engaged in rather basal EF like stimulus integration and working memory, the anterior region has a more abstract, supervisory function. To investigate whether these functionally distinct subdivisions of right dlPFC are differentially affected in PD, we analyzed resting-state functional connectivity (FC) in 39 PD patients and 44 age- and gender-matched healthy controls. Patients were examined both after at least 12 h withdrawal of dopaminergic drugs (OFF) and under their regular dopaminergic medication (ON). We found that only the posterior right dlPFC subdivision shows FC alterations in PD, while the anterior part remains unaffected. PD-related decreased FC with posterior right dlPFC was found in the bilateral medial posterior parietal cortex (mPPC) and left dorsal premotor region (PMd) in the OFF state. In the medical ON, FC with left PMd normalized, while decoupling with bilateral mPPC remained. Furthermore, we observed increased FC between posterior right dlPFC and the bilateral dorsomedial prefrontal cortex (dmPFC) in PD in the ON state. Our findings point to differential disturbances of right dlPFC connectivity in PD, which relate to its hierarchical organization of EF processing by stronger affecting the functionally basal posterior aspect than the hierarchically higher anterior part.
General microvascular perfusion and its heterogeneity are pathophysiological features of delayed cerebral ischemia (DCI) that are gaining increasing attention. Recently, CT perfusion (CTP) imaging ...has made it possible to evaluate them radiologically using mean transit time (MTT) and its heterogeneity (measured by cvMTT). This study evaluates the effect of multimodal rescue therapy (intra-arterial nimodipine administration and elevation of blood pressure) on MTT and cvMTT during DCI in aneurysmal subarachnoid haemorrhage (aSAH) patients. A total of seventy-nine aSAH patients who underwent multimodal rescue therapy between May 2012 and December 2019 were retrospectively included in this study. CTP-based perfusion impairment (MTT and cvMTT) on the day of DCI diagnosis was compared with follow-up CTP after initiation of combined multimodal therapy. The mean MTT was significantly reduced in the follow-up CTP compared to the first CTP (3.7 ± 0.7 s vs. 3.3 ± 0.6 s; p < 0.0001). However, no significant reduction of cvMTT was observed (0.16 ± 0.06 vs. 0.15 ± 0.06; p = 0.44). Mean arterial pressure was significantly increased between follow-up and first CTP (98 ± 17 mmHg vs. 104 ± 15 mmHg; p < 0.0001). The combined multimodal rescue therapy was effective in addressing the general microvascular perfusion impairment but did not affect the mechanisms underlying microvascular perfusion heterogeneity. This highlights the need for research into new therapeutic approaches that also target these pathophysiological mechanisms of DCI.
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