Imputation Strategy for Reliable Regional MRI Morphological Measurements

Linking maternal disrupted interaction and infant limbic volumes: The role of infant cortisol output

Despite a large animal literature documenting the role of low maternal nurturance and elevated glucocorticoid production on offspring limbic development, these pathways have not yet been assessed during human infancy. Informed by animal models, the present study examined whether 1) maternal disrupted interaction is related to infant cortisol levels, 2) infant cortisol levels are associated with infant limbic volumes, and 3) infant cortisol levels mediate associations between maternal disrupted interaction and infant limbic volumes. Participants included 57 mother-infant dyads. Infant saliva was measured at one time point before and two time points after the Still-Face Paradigm (SFP) at age 4 months. Five aspects of maternal disrupted interaction were coded during the SFP reunion episode. Between 4 and 25 months (M age = 11.74 months, SD = 6.12), under natural sleep, infants completed an MRI. Amygdala and hippocampal volumes were calculated via automated segmentation. Results indicated that 1) maternal disrupted interaction, and specifically disoriented interaction, with the infant was associated with higher infant salivary cortisol (AUCg) levels during the SFP, 2) higher infant AUCg was related to enlarged bilateral amygdala and hippocampal volumes, and 3) infant AUCg mediated the relation between maternal disrupted interaction and infant amygdala and hippocampal volumes. Findings are consistent with controlled animal studies and provide evidence of a link between increased cortisol levels and enlarged limbic volumes in human infants. Results further suggest that established interventions to decrease maternal disrupted interaction could impact both infant cortisol levels and infant limbic volumes.

Dementia-related user-based collaborative filtering for imputing missing data and generating a reliability scale on clinical test scores

Medical doctors may struggle to diagnose dementia, particularly when clinical test scores are missing or incorrect. In case of any doubts, both morphometrics and demographics are crucial when examining dementia in medicine. This study aims to impute and verify clinical test scores with brain MRI analysis and additional demographics, thereby proposing a decision support system that improves diagnosis and prognosis in an easy-to-understand manner. Therefore, we impute the missing clinical test score values by unsupervised dementia-related user-based collaborative filtering to minimize errors. By analyzing succession rates, we propose a reliability scale that can be utilized for the consistency of existing clinical test scores. The complete base of 816 ADNI1-screening samples was processed, and a hybrid set of 603 features was handled. Moreover, the detailed parameters in use, such as the best neighborhood and input features were evaluated for further comparative analysis. Overall, certain collaborative filtering configurations outperformed alternative state-of-the-art imputation techniques. The imputation system and reliability scale based on the proposed methodology are promising for supporting the clinical tests.

Three-dimensional self-attention conditional GAN with spectral normalization for multimodal neuroimaging synthesis

PURPOSE

To develop a new 3D generative adversarial network that is designed and optimized for the application of multimodal 3D neuroimaging synthesis.

METHODS

We present a 3D conditional generative adversarial network (GAN) that uses spectral normalization and feature matching to stabilize the training process and ensure optimization convergence (called SC-GAN). A self-attention module was also added to model the relationships between widely separated image voxels. The performance of the network was evaluated on the data set from ADNI-3, in which the proposed network was used to predict PET images, fractional anisotropy, and mean diffusivity maps from multimodal MRI. Then, SC-GAN was applied on a multidimensional diffusion MRI experiment for superresolution application. Experiment results were evaluated by normalized RMS error, peak SNR, and structural similarity.

RESULTS

In general, SC-GAN outperformed other state-of-the-art GAN networks including 3D conditional GAN in all three tasks across all evaluation metrics. Prediction error of the SC-GAN was 18%, 24% and 29% lower compared to 2D conditional GAN for fractional anisotropy, PET and mean diffusivity tasks, respectively. The ablation experiment showed that the major contributors to the improved performance of SC-GAN are the adversarial learning and the self-attention module, followed by the spectral normalization module. In the superresolution multidimensional diffusion experiment, SC-GAN provided superior predication in comparison to 3D Unet and 3D conditional GAN.

CONCLUSION

In this work, an efficient end-to-end framework for multimodal 3D medical image synthesis (SC-GAN) is presented. The source code is also made available at https://github.com/Haoyulance/SC-GAN.

Global and Regional Changes in Perivascular Space in Idiopathic and Familial Parkinson's Disease

BACKGROUND

The glymphatic system, including the perivascular space (PVS), plays a critical role in brain homeostasis. Although mounting evidence from Alzheimer's disease has supported the potential role of PVS in neurodegenerative disorders, its contribution in Parkinson's disease (PD) has not been fully elucidated. Although idiopathic (IPD) and familial PD (FPD) share similar pathophysiology in terms of protein aggregation, the differential impact of PVS on PD subtypes remains unknown. Our objective was to examine the differences in PVS volume fraction in IPD and FPD compared to healthy controls (HCs) and nonmanifest carriers (NMCs).

METHODS

A total of 470 individuals were analyzed from the Parkinson's Progression Markers Initiative database, including (1) IPD (n = 179), (2) FPD (LRRK2 [leucine-rich repeat kinase 2], glucocerebrosidase, or α-synuclein) (n = 67), (3) NMC (n = 101), and (4) HCs (n = 84). Total PVS volume fraction (%) was compared using parcellation and quantitation within greater white matter volume at global and regional levels in all cortical and subcortical white matter.

RESULTS

There was a significant increase in global and regional PVS volume fraction in PD versus non-PD, particularly in FPD versus NMC and LRRK2 FPD versus NMC. Regionally, FPD and NMC differed in the medial orbitofrontal region, as did LRRK2 FPD versus NMC. Non-PD and PD differed in the medial orbitofrontal region and the banks of the superior temporal regions. IPD and FPD differed in the cuneus and lateral occipital regions.

CONCLUSIONS

Our findings support the role of PVS in PD and highlight a potentially significant contribution of PVS to the pathophysiology of FPD, particularly LRRK2. © 2021 International Parkinson and Movement Disorder Society.

Volumetric distribution of perivascular space in relation to mild cognitive impairment

Vascular contributions to early cognitive decline are increasingly recognized, prompting further investigation into the nature of related changes in perivascular spaces (PVS). Using magnetic resonance imaging, we show that, compared to a cognitively normal sample, individuals with early cognitive dysfunction have altered PVS presence and distribution, irrespective of Amyloid-β. Surprisingly, we noted lower PVS presence in the anterosuperior medial temporal lobe (asMTL) (1.29 times lower PVS volume fraction in cognitively impaired individuals, p < 0.0001), which was associated with entorhinal neurofibrillary tau tangle deposition (beta (standard error) = -0.98 (0.4); p = 0.014), one of the hallmarks of early Alzheimer's disease pathology. We also observed higher PVS volume fraction in centrum semi-ovale of the white matter, but only in female participants (1.47 times higher PVS volume fraction in cognitively impaired individuals, p = 0.0011). We also observed PVS changes in participants with history of hypertension (higher in the white matter and lower in the asMTL). Our results suggest that anatomically specific alteration of the PVS is an early neuroimaging feature of cognitive impairment in aging adults, which is differentially manifested in female.

Investigating the Added Value of FreeSurfer's Manual Editing Procedure for the Study of the Reading Network in a Pediatric Population

Insights into brain anatomy are important for the early detection of neurodevelopmental disorders, such as dyslexia. FreeSurfer is one of the most frequently applied automatized software tools to study brain morphology. However, quality control of the outcomes provided by FreeSurfer is often ignored and could lead to wrong statistical inferences. Additional manual editing of the data may be a solution, although not without a cost in time and resources. Past research in adults on comparing the automatized method of FreeSurfer with and without additional manual editing indicated that although editing may lead to significant differences in morphological measures between the methods in some regions, it does not substantially change the sensitivity to detect clinical differences. Given that automated approaches are more likely to fail in pediatric-and inherently more noisy-data, we investigated in the current study whether FreeSurfer can be applied fully automatically or additional manual edits of T1-images are needed in a pediatric sample. Specifically, cortical thickness and surface area measures with and without additional manual edits were compared in six regions of interest (ROIs) of the reading network in 5-to-6-year-old children with and without dyslexia. Results revealed that additional editing leads to statistical differences in the morphological measures, but that these differences are consistent across subjects and that the sensitivity to reveal statistical differences in the morphological measures between children with and without dyslexia is not affected, even though conclusions of marginally significant findings can differ depending on the method used. Thereby, our results indicate that additional manual editing of reading-related regions in FreeSurfer has limited gain for pediatric samples.