Differential diagnosis of neurodegenerative diseases using structural MRI data

MRI-Based Multi-Class Relevance Vector Machine Classification of Neurodegenerative Diseases

Machine learning algorithms are a promising automated candidate that can help mitigate the growing need for dementia experts. Despite the substantial development in MRI-based machine learning analyses, case misclassification is a universal finding, yet the reasons behind misclassification are poorly understood. We implemented a multi-class classification approach that uses relevance vector machine and logistic classification to classify research participants based on their whole-brain T1-weighted MRI scans. A total of 468 participants from seven diagnostic classes were included: 144 healthy controls, 84 Alzheimer's disease, 108 behavioral variant frontotemporal dementia (bvFTD), 30 semantic variant primary progressive aphasia (svPPA), 30 non-fluent variant primary progressive aphasia (nfvPPA), 30 corticobasal syndrome (CBS), and 42 progressive supranuclear palsy syndrome (PSPS). We compared the algorithm's diagnostic accuracy against the clinical, pathological, genetic, and quantitative imaging data. The exact neurodegenerative syndrome was predicted in 71% of the cases, the neurodegenerative disease spectrum was predicted in 80% of the cases, and the algorithm distinguished controls from any dementia in 85% of the cases. The algorithm showed high performance in diagnosing healthy controls, moderate performance in diagnosing AD, bvFTD, and svPPA, and low performance in diagnosing CBS, nfvPPA, and PSPS. Based on the quantitative imaging data, most of the misclassified neurodegenerative cases had minimal atrophy and brain volumes comparable to healthy controls. In AD, early-onset AD cases with minimal brain atrophy represented most of the misclassified cases. In bvFTD, FTD genetic mutation carriers (predominantly C9orf72 repeat expansion), FTD phenocopy, patients meeting only possible bvFTD criteria represented most misclassified cases. Case misclassification in machine learning studies in neurodegenerative diseases results from neurodegenerative disease heterogeneity and the limitations of structural MRI's ability to capture the whole gamut of biological changes. Larger and more inclusive datasets that are representative of population biologic heterogeneity are needed to train better machine learning techniques, and a margin of error is expected and should be acceptable, like the uncertainty of a clinical diagnosis by a dementia expert.

Cerebrovascular and Alzheimer's disease biomarkers in dementia with Lewy bodies and other dementias

Co-pathologies are common in dementia with Lewy bodies and other dementia disorders. We investigated cerebrovascular and Alzheimer's disease co-pathologies in patients with dementia with Lewy bodies in comparison with patients with mild cognitive impairment, Alzheimer's disease, mixed dementia, vascular dementia or Parkinson's disease with dementia and cognitively unimpaired participants. We assessed the association of biomarkers of cerebrovascular and Alzheimer's disease co-pathologies with medial temporal atrophy and global cognitive performance. Additionally, we evaluated whether the findings were specific to dementia with Lewy bodies. We gathered a multi-cohort dataset of 4549 participants (dementia with Lewy bodies = 331, cognitively unimpaired = 1505, mild cognitive impairment = 1489, Alzheimer's disease = 708, mixed dementia = 268, vascular dementia = 148, Parkinson's disease with dementia = 120) from the MemClin Study, Karolinska Imaging in Dementia Study, Gothenburg H70 Birth Cohort Studies and the European DLB Consortium. Cerebrovascular co-pathology was assessed with visual ratings of white matter hyperintensities using the Fazekas scale through structural imaging. Alzheimer's disease biomarkers of β-amyloid and phosphorylated tau were assessed in the cerebrospinal fluid for a subsample (N = 2191). Medial temporal atrophy was assessed with visual ratings and global cognition with the mini-mental state examination. Differences and associations were assessed through regression models, including interaction terms. In dementia with Lewy bodies, 43% had a high white matter hyperintensity load, which was significantly higher than that in cognitively unimpaired (14%), mild cognitive impairment (26%) and Alzheimer's disease (27%), but lower than that in vascular dementia (62%). In dementia with Lewy bodies, white matter hyperintensities were associated with medial temporal atrophy, and the interaction term showed that this association was stronger than that in cognitively unimpaired and mixed dementia. However, the association between white matter hyperintensities and medial temporal atrophy was non-significant when β-amyloid was included in the model. Instead, β-amyloid predicted medial temporal atrophy in dementia with Lewy bodies, in contrast to the findings in mild cognitive impairment where medial temporal atrophy scores were independent of β-amyloid. Dementia with Lewy bodies had the lowest performance on global cognition, but this was not associated with white matter hyperintensities. In Alzheimer's disease, global cognitive performance was lower in patients with more white matter hyperintensities. We conclude that white matter hyperintensities are common in dementia with Lewy bodies and are associated with more atrophy in medial temporal lobes, but this association depended on β-amyloid-related pathology in our cohort. The associations between biomarkers were overall stronger in dementia with Lewy bodies than in some of the other diagnostic groups.

Frontal white and gray matter abnormality in gambling disorder: A multimodal MRI study

Background

Changes in brain structural connections appear to be important in the pathophysiology of substance use disorders, but their role in behavioral addictions, such as gambling disorder (GD), is unclear. GD also offers a model to study addiction mechanisms without pharmacological confounding factors. Here, we used multimodal MRI data to examine the integrity of white matter connections in individuals with GD. We hypothesized that the affected areas would be in the fronto-striatal-thalamic circuit.

Methods

Twenty individuals with GD (mean age: 64 years, GD duration: 15.7 years) and 40 age- and sex-matched healthy controls (HCs) underwent detailed clinical examinations together with brain 3T MRI scans (T1, T2, FLAIR and DWI). White matter (WM) analysis involved fractional anisotropy and lesion load, while gray matter (GM) analysis included voxel- and surface-based morphometry. These measures were compared between groups, and correlations with GD-related behavioral characteristics were examined.

Results

Individuals with GD showed reduced WM integrity in the left and right frontal parts of the corona radiata and corpus callosum (pFWE < 0.05). WM gambling symptom severity (SOGS score) was negatively associated to WM integrity in these areas within the left hemisphere (p < 0.05). Individuals with GD also exhibited higher WM lesion load in the left anterior corona radiata (pFWE < 0.05). GM volume in the left thalamus and GM thickness in the left orbitofrontal cortex were reduced in the GD group (pFWE < 0.05).

Conclusions

Similar to substance addictions, the fronto-striatal-thalamic circuit is also affected in GD, suggesting that this circuitry may have a crucial role in addictions, independent of pharmacological substances.

MicroRNA biomarkers as next-generation diagnostic tools for neurodegenerative diseases: a comprehensive review

Neurodegenerative diseases (NDs) are characterized by abnormalities within neurons of the brain or spinal cord that gradually lose function, eventually leading to cell death. Upon examination of affected tissue, pathological changes reveal a loss of synapses, misfolded proteins, and activation of immune cells-all indicative of disease progression-before severe clinical symptoms become apparent. Early detection of NDs is crucial for potentially administering targeted medications that may delay disease advancement. Given their complex pathophysiological features and diverse clinical symptoms, there is a pressing need for sensitive and effective diagnostic methods for NDs. Biomarkers such as microRNAs (miRNAs) have been identified as potential tools for detecting these diseases. We explore the pivotal role of miRNAs in the context of NDs, focusing on Alzheimer's disease, Parkinson's disease, Multiple sclerosis, Huntington's disease, and Amyotrophic Lateral Sclerosis. The review delves into the intricate relationship between aging and NDs, highlighting structural and functional alterations in the aging brain and their implications for disease development. It elucidates how miRNAs and RNA-binding proteins are implicated in the pathogenesis of NDs and underscores the importance of investigating their expression and function in aging. Significantly, miRNAs exert substantial influence on post-translational modifications (PTMs), impacting not just the nervous system but a wide array of tissues and cell types as well. Specific miRNAs have been found to target proteins involved in ubiquitination or de-ubiquitination processes, which play a significant role in regulating protein function and stability. We discuss the link between miRNA, PTM, and NDs. Additionally, the review discusses the significance of miRNAs as biomarkers for early disease detection, offering insights into diagnostic strategies.

Computerized decision support is an effective approach to select memory clinic patients for amyloid-PET

BACKGROUND

The use of amyloid-PET in dementia workup is upcoming. At the same time, amyloid-PET is costly and limitedly available. While the appropriate use criteria (AUC) aim for optimal use of amyloid-PET, their limited sensitivity hinders the translation to clinical practice. Therefore, there is a need for tools that guide selection of patients for whom amyloid-PET has the most clinical utility. We aimed to develop a computerized decision support approach to select patients for amyloid-PET.

METHODS

We included 286 subjects (135 controls, 108 Alzheimer's disease dementia, 33 frontotemporal lobe dementia, and 10 vascular dementia) from the Amsterdam Dementia Cohort, with available neuropsychology, APOE, MRI and [18F]florbetaben amyloid-PET. In our computerized decision support approach, using supervised machine learning based on the DSI classifier, we first classified the subjects using only neuropsychology, APOE, and quantified MRI. Then, for subjects with uncertain classification (probability of correct class (PCC) < 0.75) we enriched classification by adding (hypothetical) amyloid positive (AD-like) and negative (normal) PET visual read results and assessed whether the diagnosis became more certain in at least one scenario (PPC≥0.75). If this was the case, the actual visual read result was used in the final classification. We compared the proportion of PET scans and patients diagnosed with sufficient certainty in the computerized approach with three scenarios: 1) without amyloid-PET, 2) amyloid-PET according to the AUC, and 3) amyloid-PET for all patients.

RESULTS

The computerized approach advised PET in n = 60(21%) patients, leading to a diagnosis with sufficient certainty in n = 188(66%) patients. This approach was more efficient than the other three scenarios: 1) without amyloid-PET, diagnostic classification was obtained in n = 155(54%), 2) applying the AUC resulted in amyloid-PET in n = 113(40%) and diagnostic classification in n = 156(55%), and 3) performing amyloid-PET in all resulted in diagnostic classification in n = 154(54%).

CONCLUSION

Our computerized data-driven approach selected 21% of memory clinic patients for amyloid-PET, without compromising diagnostic performance. Our work contributes to a cost-effective implementation and could support clinicians in making a balanced decision in ordering additional amyloid PET during the dementia workup.

Blood biomarkers of neurodegeneration associate differently with amyloid deposition, medial temporal atrophy, and cerebrovascular changes in APOE ε4-enriched cognitively unimpaired elderly

BACKGROUND

Alzheimer's disease (AD) is characterized by the accumulation of amyloid-β (Aβ) plaques, neurofibrillary tau tangles, and neurodegeneration in the brain parenchyma. Here, we aimed to (i) assess differences in blood and imaging biomarkers used to evaluate neurodegeneration among cognitively unimpaired APOE ε4 homozygotes, heterozygotes, and non-carriers with varying risk for sporadic AD, and (ii) to determine how different cerebral pathologies (i.e., Aβ deposition, medial temporal atrophy, and cerebrovascular pathology) contribute to blood biomarker concentrations in this sample.

METHODS

Sixty APOE ε4 homozygotes (n = 19), heterozygotes (n = 21), and non-carriers (n = 20) ranging from 60 to 75 years, were recruited in collaboration with Auria biobank (Turku, Finland). Participants underwent Aβ-PET ([11C]PiB), structural brain MRI including T1-weighted and T2-FLAIR sequences, and blood sampling for measuring serum neurofilament light chain (NfL), plasma total tau (t-tau), plasma N-terminal tau fragments (NTA-tau) and plasma glial fibrillary acidic protein (GFAP). [11C]PiB standardized uptake value ratio was calculated for regions typical for Aβ accumulation in AD. MRI images were analysed for regional volumes, atrophy scores, and volumes of white matter hyperintensities. Differences in biomarker levels and associations between blood and imaging biomarkers were tested using uni- and multivariable linear models (unadjusted and adjusted for age and sex).

RESULTS

Serum NfL concentration was increased in APOE ε4 homozygotes compared with non-carriers (mean 21.4 pg/ml (SD 9.5) vs. 15.5 pg/ml (3.8), p = 0.013), whereas other blood biomarkers did not differ between the groups (p > 0.077 for all). From imaging biomarkers, hippocampal volume was significantly decreased in APOE ε4 homozygotes compared with non-carriers (6.71 ml (0.86) vs. 7.2 ml (0.7), p = 0.029). In the whole sample, blood biomarker levels were differently predicted by the three measured cerebral pathologies; serum NfL concentration was associated with cerebrovascular pathology and medial temporal atrophy, while plasma NTA-tau associated with medial temporal atrophy. Plasma GFAP showed significant association with both medial temporal atrophy and Aβ pathology. Plasma t-tau concentration did not associate with any of the measured pathologies.

CONCLUSIONS

Only increased serum NfL concentrations and decreased hippocampal volume was observed in cognitively unimpaired APOEε4 homozygotes compared to non-carriers. In the whole population the concentrations of blood biomarkers were affected in distinct ways by different pathologies.

Deep Learning-based Approach for Brainstem and Ventricular MR Planimetry: Application in Patients with Progressive Supranuclear Palsy

Purpose To develop a fast and fully automated deep learning (DL)-based method for the MRI planimetric segmentation and measurement of the brainstem and ventricular structures most affected in patients with progressive supranuclear palsy (PSP). Materials and Methods In this retrospective study, T1-weighted MR images in healthy controls (n = 84) were used to train DL models for segmenting the midbrain, pons, middle cerebellar peduncle (MCP), superior cerebellar peduncle (SCP), third ventricle, and frontal horns (FHs). Internal, external, and clinical test datasets (n = 305) were used to assess segmentation model reliability. DL masks from test datasets were used to automatically extract midbrain and pons areas and the width of MCP, SCP, third ventricle, and FHs. Automated measurements were compared with those manually performed by an expert radiologist. Finally, these measures were combined to calculate the midbrain to pons area ratio, MR parkinsonism index (MRPI), and MRPI 2.0, which were used to differentiate patients with PSP (n = 71) from those with Parkinson disease (PD) (n = 129). Results Dice coefficients above 0.85 were found for all brain regions when comparing manual and DL-based segmentations. A strong correlation was observed between automated and manual measurements (Spearman ρ > 0.80, P < .001). DL-based measurements showed excellent performance in differentiating patients with PSP from those with PD, with an area under the receiver operating characteristic curve above 0.92. Conclusion The automated approach successfully segmented and measured the brainstem and ventricular structures. DL-based models may represent a useful approach to support the diagnosis of PSP and potentially other conditions associated with brainstem and ventricular alterations. Keywords: MR Imaging, Brain/Brain Stem, Segmentation, Quantification, Diagnosis, Convolutional Neural Network Supplemental material is available for this article. © RSNA, 2024 See also the commentary by Mohajer in this issue.

Integrating Demographics and Imaging Features for Various Stages of Dementia Classification: Feed Forward Neural Network Multi-Class Approach

BACKGROUND

MRI magnetization-prepared rapid acquisition (MPRAGE) is an easily available imaging modality for dementia diagnosis. Previous studies suggested that volumetric analysis plays a crucial role in various stages of dementia classification. In this study, volumetry, radiomics and demographics were integrated as inputs to develop an artificial intelligence model for various stages, including Alzheimer's disease (AD), mild cognitive decline (MCI) and cognitive normal (CN) dementia classifications.

METHOD

The Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset was separated into training and testing groups, and the Open Access Series of Imaging Studies (OASIS) dataset was used as the second testing group. The MRI MPRAGE image was reoriented via statistical parametric mapping (SPM12). Freesurfer was employed for brain segmentation, and 45 regional brain volumes were retrieved. The 3D Slicer software was employed for 107 radiomics feature extractions from within the whole brain. Data on patient demographics were collected from the datasets. The feed-forward neural network (FFNN) and the other most common artificial intelligence algorithms, including support vector machine (SVM), ensemble classifier (EC) and decision tree (DT), were used to build the models using various features.

RESULTS

The integration of brain regional volumes, radiomics and patient demographics attained the highest overall accuracy at 76.57% and 73.14% in ADNI and OASIS testing, respectively. The subclass accuracies in MCI, AD and CN were 78.29%, 89.71% and 85.14%, respectively, in ADNI testing, as well as 74.86%, 88% and 83.43% in OASIS testing. Balanced sensitivity and specificity were obtained for all subclass classifications in MCI, AD and CN.

CONCLUSION

The FFNN yielded good overall accuracy for MCI, AD and CN categorization, with balanced subclass accuracy, sensitivity and specificity. The proposed FFNN model is simple, and it may support the triage of patients for further confirmation of the diagnosis.

Use of a digital tool to support the diagnostic process in memory clinics-a usability study

BACKGROUND

Both memory clinic professionals and patients see value in digital tools, yet these hardly find their way to clinical practice. We explored the usability of a digital tool to support the diagnostic work-up in daily memory clinic practice. We evaluated four modules that integrate multi-modal patient data (1.cognitive test; cCOG, and 2. MRI quantification; cMRI) into useful diagnostic information for clinicians (3. cDSI) and understandable and personalized information for patients (4. patient report).

METHODS

We conducted a mixed-methods study in five Dutch memory clinics. Fourteen clinicians (11 geriatric specialists/residents, two neurologists, one nurse practitioner) were invited to integrate the tool into routine care with 43 new memory clinic patients. We evaluated usability and user experiences through quantitative data from questionnaires (patients, care partners, clinicians), enriched with thematically analyzed qualitative data from interviews (clinicians).

RESULTS

We observed wide variation in tool use among clinicians. Our core findings were that clinicians: 1) were mainly positive about the patient report, since it contributes to patient-centered and personalized communication. This was endorsed by patients and care partners, who indicated that the patient report was useful and understandable and helped them to better understand their diagnosis, 2) considered the tool acceptable in addition to their own clinical competence, 3) indicated that the usefulness of the tool depended on the patient population and purpose of the diagnostic process, 4) addressed facilitators (ease of use, practice makes perfect) and barriers (high workload, lack of experience, data unavailability).

CONCLUSION

This multicenter usability study revealed a willingness to adopt a digital tool to support the diagnostic process in memory clinics. Clinicians, patients, and care partners appreciated the personalized diagnostic report. More attention to education and training of clinicians is needed to utilize the full functionality of the tool and foster implementation in actual daily practice. These findings provide an important step towards a lasting adoption of digital tools in memory clinic practice.

Contrast-enhanced to non-contrast-enhanced image translation to exploit a clinical data warehouse of T1-weighted brain MRI

BACKGROUND

Clinical data warehouses provide access to massive amounts of medical images, but these images are often heterogeneous. They can for instance include images acquired both with or without the injection of a gadolinium-based contrast agent. Harmonizing such data sets is thus fundamental to guarantee unbiased results, for example when performing differential diagnosis. Furthermore, classical neuroimaging software tools for feature extraction are typically applied only to images without gadolinium. The objective of this work is to evaluate how image translation can be useful to exploit a highly heterogeneous data set containing both contrast-enhanced and non-contrast-enhanced images from a clinical data warehouse.

METHODS

We propose and compare different 3D U-Net and conditional GAN models to convert contrast-enhanced T1-weighted (T1ce) into non-contrast-enhanced (T1nce) brain MRI. These models were trained using 230 image pairs and tested on 77 image pairs from the clinical data warehouse of the Greater Paris area.

RESULTS

Validation using standard image similarity measures demonstrated that the similarity between real and synthetic T1nce images was higher than between real T1nce and T1ce images for all the models compared. The best performing models were further validated on a segmentation task. We showed that tissue volumes extracted from synthetic T1nce images were closer to those of real T1nce images than volumes extracted from T1ce images.

CONCLUSION

We showed that deep learning models initially developed with research quality data could synthesize T1nce from T1ce images of clinical quality and that reliable features could be extracted from the synthetic images, thus demonstrating the ability of such methods to help exploit a data set coming from a clinical data warehouse.

Cognitively healthy APOE4/4 carriers show white matter impairment associated with serum NfL and amyloid-PET

Except for aging, carrying the APOE ε4 allele (APOE4) is the most important risk factor for sporadic Alzheimer's disease. APOE4 carriers may have reduced capacity to recycle lipids, resulting in white matter microstructural abnormalities. In this study, we evaluated whether white matter impairment measured by diffusion tensor imaging (DTI) differs between healthy individuals with a different number of APOE4 alleles, and whether white matter impairment associates with brain beta-amyloid (Aβ) load and serum levels of neurofilament light chain (NfL). We studied 96 participants (APOE3/3, N = 37; APOE3/4, N = 39; APOE4/4, N = 20; mean age 70.7 (SD 5.22) years, 63% females) with a brain MRI including a DTI sequence (N = 96), Aβ-PET (N = 89) and a venous blood sample for the serum NfL concentration measurement (N = 88). Fractional anisotropy (FA), mean diffusivity (MD), radial diffusivity (RD) and axial diffusivity (AxD) in six a priori-selected white matter regions-of-interest (ROIs) were compared between the groups using ANCOVA, with sex and age as covariates. A voxel-weighted average of FA, MD, RD and AxD was calculated for each subject, and correlations with Aβ-PET and NfL levels were evaluated. APOE4/4 carriers exhibited a higher MD and a higher RD in the body of corpus callosum than APOE3/4 (p = 0.0053 and p = 0.0049, respectively) and APOE3/3 (p = 0.026 and p = 0.042). APOE4/4 carriers had a higher AxD than APOE3/4 (p = 0.012) and APOE3/3 (p = 0.040) in the right cingulum adjacent to cingulate cortex. In the total sample, composite MD, RD and AxD positively correlated with the cortical Aβ load (r = 0.26 to 0.33, p < 0.013 for all) and with serum NfL concentrations (r = 0.31 to 0.36, p < 0.0028 for all). In conclusion, increased local diffusivity was detected in cognitively unimpaired APOE4/4 homozygotes compared to APOE3/4 and APOE3/3 carriers, and increased diffusivity correlated with biomarkers of Alzheimer's disease and neurodegeneration. White matter impairment seems to be an early phenomenon in the Alzheimer's disease pathologic process in APOE4/4 homozygotes.

Regional MRI volumetry using NeuroQuant versus visual rating scales in patients with cognitive impairment and dementia

BACKGROUND AND PURPOSE

The aims were to compare the novel regional brain volumetric measures derived by the automatic software NeuroQuant (NQ) with clinically used visual rating scales of medial temporal lobe atrophy (MTA), global cortical atrophy-frontal (GCA-f), and posterior atrophy (PA) brain regions, assessing their diagnostic validity, and to explore if combining automatic and visual methods would increase diagnostic prediction accuracy.

METHODS

Brain magnetic resonance imaging (MRI) examinations from 86 patients with subjective and mild cognitive impairment (i.e., non-dementia, n = 41) and dementia (n = 45) from the Memory Clinic at Oslo University Hospital were assessed using NQ volumetry and with visual rating scales. Correlations, receiver operating characteristic analyses calculating area under the curves (AUCs) for diagnostic accuracy, and logistic regression analyses were performed.

RESULTS

The correlations between NQ volumetrics and visual ratings of corresponding regions were generally high between NQ hippocampi/temporal volumes and MTA (r = -0.72/-0.65) and between NQ frontal volume and GCA-f (r = -0.62) but lower between NQ parietal/occipital volumes and PA (r = -0.49/-0.37). AUCs of each region, separating non-dementia from dementia, were generally comparable between the two methods, except that NQ hippocampi volume did substantially better than visual MTA (AUC = 0.80 vs. 0.69). Combining both MRI methods increased only the explained variance of the diagnostic prediction substantially regarding the posterior brain region.

CONCLUSIONS

The findings of this study encourage the use of regional automatic volumetry in locations lacking neuroradiologists with experience in the rating of atrophy typical of neurodegenerative diseases, and in primary care settings.

Joint diffusional kurtosis magnetic resonance imaging analysis of white matter and the thalamus to identify subcortical ischemic vascular disease

Identifying subcortical ischemic vascular disease (SIVD) in older adults is important but challenging. Growing evidence suggests that diffusional kurtosis imaging (DKI) can detect SIVD-relevant microstructural pathology, and a systematic assessment of the discriminant power of DKI metrics in various brain tissue microstructures is urgently needed. Therefore, the current study aimed to explore the value of DKI and diffusion tensor imaging (DTI) metrics in detecting early-stage SIVD by combining multiple diffusion metrics, analysis strategies, and clinical-radiological constraints. This prospective study compared DKI with diffusivity and macroscopic imaging evaluations across the aging spectrum including SIVD, Alzheimer's disease (AD), and cognitively normal (NC) groups. Using a white matter atlas and segregated thalamus analysis with considerations of the pre-identified macroscopic pathology, the most effective diffusion metrics were selected and then examined using multiple clinical-radiological constraints in a two-group or three-group paradigm. A total of 122 participants (mean age, 74.6 ± 7.38 years, 72 women) including 42 with SIVD, 50 with AD, and 30 NC were evaluated. Fractional anisotropy, mean kurtosis, and radial kurtosis were critical metrics in detecting early-stage SIVD. The optimal selection of diffusion metrics showed 84.4-100% correct classification of the results in a three-group paradigm, with an area under the curve of .909-.987 in a two-group paradigm related to SIVD detection (all P < .001). We therefore concluded that greatly resilient to the effect of pre-identified macroscopic pathology, the combination of DKI/DTI metrics showed preferable performance in identifying early-stage SIVD among adults across the aging spectrum.

Polydopamine Nanostructure-Enhanced Water Interaction with pH-Responsive Manganese Sulfide Nanoclusters for Tumor Magnetic Resonance Contrast Enhancement and Synergistic Ferroptosis-Photothermal Therapy

Rational structure design benefits the development of efficient nanoplatforms for tumor theranostic application. In this work, a multifunctional polydopamine (PDA)-coated manganese sulfide (MnS) nanocluster was prepared. The polyhydroxy structure of PDA enhanced the water interaction with pH-responsive MnS nanoclusters via hydrogen bonds. At pH 5.5 conditions, the spin-lattice relaxation rate of MnS nanoclusters dramatically increased from 5.76 to 19.33 mM-1·s-1 after the PDA coating, which can be beneficial for efficient tumor magnetic resonance imaging. In addition, PDA endowed MnS nanoclusters with excellent biocompatibility and good photothermal conversion efficiency, which can be used for efficient tumor photothermal therapy (PTT). Furthermore, MnS nanoclusters possess the ability to release H2S in the acidic tumor microenvironment, effectively inhibiting mitochondrial respiration and adenosine triphosphate production. As a result, the expression of heat shock protein was obviously reduced, which can reduce the resistance of tumor cells to photothermal stimulation and enhance the efficacy of PTT. The released Mn2+ also displayed efficient peroxidase and glutathione oxidase-like activity, effectively inducing tumor cell ferroptosis and apoptosis at the same time. Therefore, this nanoplatform could be a potential nanotheranostic for magnetic resonance contrast enhancement and synergistic ferroptosis-PTT of tumors.

Evaluation of a Fully Automated Method for Ventricular Volume Segmentation Before and After Shunt Surgery in Idiopathic Normal Pressure Hydrocephalus

BACKGROUND

Determination of the ventricle size in idiopathic normal pressure hydrocephalus (iNPH) is essential for diagnosis and follow-up of shunt results. Fully automated segmentation methods are anticipated to optimize the accuracy and time efficiency of ventricular volume measurements. We evaluated the accuracy of preoperative and postoperative ventricular volume measurements in iNPH by a magnetic resonance imaging (MRI)-based licensed software for fully automated quantitative assessment.

METHODS

Forty-eight patients diagnosed with iNPH were retrospectively analyzed. All patients received a ventriculoperitoneal shunt and had symptom grading and routine MRI preoperatively and 3-6 months postoperatively. Ventricular volumes, generated by fully automated T1-weighted imaging volume sequence segmentation, were compared with semiautomatic measurements and routine radiologic reports. The relation of postoperative ventricular size change to clinical response was evaluated.

RESULTS

Fully automated segmentation was achieved in 95% of the MRIs, but showed various rates of 8 minor segmentation errors. The correlation between both segmentation methods was very strong (r >0.9) and the agreement very good using Bland-Altman analyses. The ventricular volumes differed significantly between semiautomated and fully automated segmentations and between preoperative and postoperative MRI. The fully automated method systematically overestimated the ventricles by a median 15 mL preoperatively and 14 mL postoperatively; hence, the magnitudes of volume changes were equivalent. Routine radiologic reports of ventricular size changes were inaccurate in 51% and lacked association with treatment response. Objectively measured ventricular volume changes correlated moderately with postoperative clinical improvement.

CONCLUSIONS

A fully automated volumetric method permits reliable evaluation of preoperative ventriculomegaly and postoperative ventricular volume change in idiopathic normal pressure hydrocephalus.

Artificial intelligence for diagnostic and prognostic neuroimaging in dementia: A systematic review

INTRODUCTION

Artificial intelligence (AI) and neuroimaging offer new opportunities for diagnosis and prognosis of dementia.

METHODS

We systematically reviewed studies reporting AI for neuroimaging in diagnosis and/or prognosis of cognitive neurodegenerative diseases.

RESULTS

A total of 255 studies were identified. Most studies relied on the Alzheimer's Disease Neuroimaging Initiative dataset. Algorithmic classifiers were the most commonly used AI method (48%) and discriminative models performed best for differentiating Alzheimer's disease from controls. The accuracy of algorithms varied with the patient cohort, imaging modalities, and stratifiers used. Few studies performed validation in an independent cohort.

DISCUSSION

The literature has several methodological limitations including lack of sufficient algorithm development descriptions and standard definitions. We make recommendations to improve model validation including addressing key clinical questions, providing sufficient description of AI methods and validating findings in independent datasets. Collaborative approaches between experts in AI and medicine will help achieve the promising potential of AI tools in practice.

HIGHLIGHTS

There has been a rapid expansion in the use of machine learning for diagnosis and prognosis in neurodegenerative disease Most studies (71%) relied on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset with no other individual dataset used more than five times There has been a recent rise in the use of more complex discriminative models (e.g., neural networks) that performed better than other classifiers for classification of AD vs healthy controls We make recommendations to address methodological considerations, addressing key clinical questions, and validation We also make recommendations for the field more broadly to standardize outcome measures, address gaps in the literature, and monitor sources of bias.

Differential diagnosis of neurodegenerative dementias with the explainable MRI based machine learning algorithm MUQUBIA

Biomarker-based differential diagnosis of the most common forms of dementia is becoming increasingly important. Machine learning (ML) may be able to address this challenge. The aim of this study was to develop and interpret a ML algorithm capable of differentiating Alzheimer's dementia, frontotemporal dementia, dementia with Lewy bodies and cognitively normal control subjects based on sociodemographic, clinical, and magnetic resonance imaging (MRI) variables. 506 subjects from 5 databases were included. MRI images were processed with FreeSurfer, LPA, and TRACULA to obtain brain volumes and thicknesses, white matter lesions and diffusion metrics. MRI metrics were used in conjunction with clinical and demographic data to perform differential diagnosis based on a Support Vector Machine model called MUQUBIA (Multimodal Quantification of Brain whIte matter biomArkers). Age, gender, Clinical Dementia Rating (CDR) Dementia Staging Instrument, and 19 imaging features formed the best set of discriminative features. The predictive model performed with an overall Area Under the Curve of 98%, high overall precision (88%), recall (88%), and F1 scores (88%) in the test group, and good Label Ranking Average Precision score (0.95) in a subset of neuropathologically assessed patients. The results of MUQUBIA were explained by the SHapley Additive exPlanations (SHAP) method. The MUQUBIA algorithm successfully classified various dementias with good performance using cost-effective clinical and MRI information, and with independent validation, has the potential to assist physicians in their clinical diagnosis.

Evaluation of MRI-based machine learning approaches for computer-aided diagnosis of dementia in a clinical data warehouse

A variety of algorithms have been proposed for computer-aided diagnosis of dementia from anatomical brain MRI. These approaches achieve high accuracy when applied to research data sets but their performance on real-life clinical routine data has not been evaluated yet. The aim of this work was to study the performance of such approaches on clinical routine data, based on a hospital data warehouse, and to compare the results to those obtained on a research data set. The clinical data set was extracted from the hospital data warehouse of the Greater Paris area, which includes 39 different hospitals. The research set was composed of data from the Alzheimer's Disease Neuroimaging Initiative data set. In the clinical set, the population of interest was identified by exploiting the diagnostic codes from the 10th revision of the International Classification of Diseases that are assigned to each patient. We studied how the imbalance of the training sets, in terms of contrast agent injection and image quality, may bias the results. We demonstrated that computer-aided diagnosis performance was strongly biased upwards (over 17 percent points of balanced accuracy) by the confounders of image quality and contrast agent injection, a phenomenon known as the Clever Hans effect or shortcut learning. When these biases were removed, the performance was very poor. In any case, the performance was considerably lower than on the research data set. Our study highlights that there are still considerable challenges for translating dementia computer-aided diagnosis systems to clinical routine.

The use of neuroimaging techniques in the early and differential diagnosis of dementia

Dementia is a leading cause of disability and death worldwide. At present there is no disease modifying treatment for any of the most common types of dementia such as Alzheimer's disease (AD), Vascular dementia, Lewy Body Dementia (LBD) and Frontotemporal dementia (FTD). Early and accurate diagnosis of dementia subtype is critical to improving clinical care and developing better treatments. Structural and molecular imaging has contributed to a better understanding of the pathophysiology of neurodegenerative dementias and is increasingly being adopted into clinical practice for early and accurate diagnosis. In this review we summarise the contribution imaging has made with particular focus on multimodal magnetic resonance imaging (MRI) and positron emission tomography imaging (PET). Structural MRI is widely used in clinical practice and can help exclude reversible causes of memory problems but has relatively low sensitivity for the early and differential diagnosis of dementia subtypes. 18F-fluorodeoxyglucose PET has high sensitivity and specificity for AD and FTD, while PET with ligands for amyloid and tau can improve the differential diagnosis of AD and non-AD dementias, including recognition at prodromal stages. Dopaminergic imaging can assist with the diagnosis of LBD. The lack of a validated tracer for α-synuclein or TAR DNA-binding protein 43 (TDP-43) imaging remain notable gaps, though work is ongoing. Emerging PET tracers such as 11C-UCB-J for synaptic imaging may be sensitive early markers but overall larger longitudinal multi-centre cross diagnostic imaging studies are needed.

Subjective vs informant-reported cognitive complaints have differential clinical significance in covert cerebral small vessel disease

Objective

Subjective cognitive complaints are common in patients with cerebral small vessel disease (cSVD), yet their relationship with informant evaluations, objective cognitive functions and severity of brain changes are poorly understood. We studied the associations of subjective and informant reports with findings from comprehensive neuropsychological assessment and brain MRI.

Method

In the Helsinki SVD Study, 152 older adults with varying degrees of white matter hyperintensities (WMH) but without stroke or dementia were classified as having normal cognition or mild cognitive impairment (MCI) based on neuropsychological criteria. The measures also included continuous domain scores for memory and executive functions. Cognitive complaints were evaluated with the subjective and informant versions of the Prospective and Retrospective Memory Questionnaire (PRMQ) and Dysexecutive Questionnaire (DEX); functional abilities with the Amsterdam Instrumental Activities of Daily Living Questionnaire (A-IADL); and depressive symptoms with the Geriatric Depression Scale (GDS-15).

Results

Subjective cognitive complaints correlated significantly with informant reports (r=0.40-0.50, p<0.001). After controlling for demographics, subjective and informant DEX and PRMQ were not related to MCI, memory or executive functions. Instead, subjective DEX and PRMQ significantly associated with GDS-15 and informant DEX and PRMQ with WMH volume and A-IADL.

Conclusions

Neither subjective nor informant-reported cognitive complaints associated with objective cognitive performance. Informant-evaluations were related to functional impairment and more severe WMH, whereas subjective complaints only associated with mild depressive symptoms. These findings suggest that awareness of cognitive impairment may be limited in early-stage cSVD and highlight the value of informant assessments in the identification of patients with functional impairment.

Precision medicine in neurodegeneration: the IHI-PROMINENT project

Neurodegenerative diseases are one of the most important contributors to morbidity and mortality in the elderly. In Europe, over 14 million people are currently living with dementia, at a cost of over 400 billion EUR annually. Recent advances in diagnostics and approval for new pharmaceutical treatments for Alzheimer's disease (AD), the most common etiology of dementia, heralds the beginning of precision medicine in this field. However, their implementation will challenge an already over-burdened healthcare systems. There is a need for innovative digital solutions that can drive the related clinical pathways and optimize and personalize care delivery. Public-private partnerships are ideal vehicles to tackle these challenges. Here we describe the Innovative Health Initiative (IHI) public-private partnership project PROMINENT that has been initiated by connecting leading dementia researchers, medical professionals, dementia patients and their care partners with the latest innovative health technologies using a precision medicine based digital platform. The project builds upon the knowledge and already implemented digital tools from several collaborative initiatives that address new models for early detection, diagnosis, and monitoring of AD and other neurodegenerative disorders. The project aims to provide support to improvement efforts to each aspect of the care pathway including diagnosis, prognosis, treatment, and data collection for real world evidence and cost effectiveness studies. Ultimately the PROMINENT project is expected to lead to cost-effective care and improved health outcomes.

Unraveling Lifelong Brain Morphometric Dynamics: A Protocol for Systematic Review and Meta-Analysis in Healthy Neurodevelopment and Ageing

A high incidence and prevalence of neurodegenerative diseases and neurodevelopmental disorders justify the necessity of well-defined criteria for diagnosing these pathologies from brain imaging findings. No easy-to-apply quantitative markers of abnormal brain development and ageing are available. We aim to find the characteristic features of non-pathological development and degeneration in distinct brain structures and to work out a precise descriptive model of brain morphometry in age groups. We will use four biomedical databases to acquire original peer-reviewed publications on brain structural changes occurring throughout the human life-span. Selected publications will be uploaded to Covidence systematic review software for automatic deduplication and blinded screening. Afterwards, we will manually review the titles, abstracts, and full texts to identify the papers matching eligibility criteria. The relevant data will be extracted to a 'Summary of findings' table. This will allow us to calculate the annual rate of change in the volume or thickness of brain structures and to model the lifelong dynamics in the morphometry data. Finally, we will adjust the loss of weight/thickness in specific brain areas to the total intracranial volume. The systematic review will synthesise knowledge on structural brain change across the life-span.

APOE ε4 gene dose effect on imaging and blood biomarkers of neuroinflammation and beta-amyloid in cognitively unimpaired elderly

BACKGROUND

Neuroinflammation, characterized by increased reactivity of microglia and astrocytes in the brain, is known to be present at various stages of the Alzheimer's disease (AD) continuum. However, its presence and relationship with amyloid pathology in cognitively normal at-risk individuals is less clear. Here, we used positron emission tomography (PET) and blood biomarker measurements to examine differences in neuroinflammation and beta-amyloid (Aβ) and their association in cognitively unimpaired homozygotes, heterozygotes, or non-carriers of the APOE ε4 allele, the strongest genetic risk for sporadic AD.

METHODS

Sixty 60-75-year-old APOE ε4 homozygotes (n = 19), heterozygotes (n = 21), and non-carriers (n = 20) were recruited in collaboration with the local Auria biobank. The participants underwent 11C-PK11195 PET (targeting 18-kDa translocator protein, TSPO), 11C-PiB PET (targeting Aβ), brain MRI, and neuropsychological testing including a preclinical cognitive composite (APCC). 11C-PK11195 distribution volume ratios and 11C-PiB standardized uptake value ratios (SUVRs) were calculated for regions typical for early Aβ accumulation in AD. Blood samples were drawn for measuring plasma glial fibrillary acidic protein (GFAP) and plasma Aβ1-42/1.40.

RESULTS

In our cognitively unimpaired sample, cortical 11C-PiB-binding increased according to APOE ε4 gene dose (median composite SUVR 1.47 (range 1.38-1.66) in non-carriers, 1.55 (1.43-2.02) in heterozygotes, and 2.13 (1.61-2.83) in homozygotes, P = 0.002). In contrast, cortical composite 11C-PK11195-binding did not differ between the APOE ε4 gene doses (P = 0.27) or between Aβ-positive and Aβ-negative individuals (P = 0.81) and associated with higher Aβ burden only in APOE ε4 homozygotes (Rho = 0.47, P = 0.043). Plasma GFAP concentration correlated with cortical 11C-PiB (Rho = 0.35, P = 0.040), but not 11C-PK11195-binding (Rho = 0.13, P = 0.47) in Aβ-positive individuals. In the total cognitively unimpaired population, both higher composite 11C-PK11195-binding and plasma GFAP were associated with lower hippocampal volume, whereas elevated 11C-PiB-binding was associated with lower APCC scores.

CONCLUSIONS

Only Aβ burden measured by PET, but not markers of neuroinflammation, differed among cognitively unimpaired elderly with different APOE ε4 gene dose. However, APOE ε4 gene dose seemed to modulate the association between neuroinflammation and Aβ.

Molecular Biomarkers of Neuronal Injury in Epilepsy Shared with Neurodegenerative Diseases

In neurodegenerative diseases, changes in neuronal proteins in the cerebrospinal fluid and blood are viewed as potential biomarkers of the primary pathology in the central nervous system (CNS). Recent reports suggest, however, that level of neuronal proteins in fluids also alters in several types of epilepsy in various age groups, including children. With increasing evidence supporting clinical and sub-clinical seizures in Alzheimer's disease, Lewy body dementia, Parkinson's disease, and in other less common neurodegenerative conditions, these findings call into question the specificity of neuronal protein response to neurodegenerative process and urge analysis of the effects of concomitant epilepsy and other comorbidities. In this article, we revisit the evidence for alterations in neuronal proteins in the blood and cerebrospinal fluid associated with epilepsy with and without neurodegenerative diseases. We discuss shared and distinctive characteristics of changes in neuronal markers, review their neurobiological mechanisms, and consider the emerging opportunities and challenges for their future research and diagnostic use.

Neurofilament light level correlates with brain atrophy, and cognitive and motor performance

Background

The usefulness of neurofilament light (NfL) as a biomarker for small vessel disease has not been established. We examined the relationship between NfL, neuroimaging changes, and clinical findings in subjects with varying degrees of white matter hyperintensity (WMH).

Methods

A subgroup of participants (n = 35) in the Helsinki Small Vessel Disease Study underwent an analysis of NfL in cerebrospinal fluid (CSF) as well as brain magnetic resonance imaging (MRI) and neuropsychological and motor performance assessments. WMH and structural brain volumes were obtained with automatic segmentation.

Results

CSF NfL did not correlate significantly with total WMH volume (r = 0.278, p = 0.105). However, strong correlations were observed between CSF NfL and volumes of cerebral grey matter (r = -0.569, p < 0.001), cerebral cortex (r = -0.563, p < 0.001), and hippocampi (r = -0.492, p = 0.003). CSF NfL also correlated with composite measures of global cognition (r = -0.403, p = 0.016), executive functions (r = -0.402, p = 0.017), memory (r = -0.463, p = 0.005), and processing speed (r = -0.386, p = 0.022). Regarding motor performance, CSF NfL was correlated with Timed Up and Go (TUG) test (r = 0.531, p = 0.001), and gait speed (r = -0.450, p = 0.007), but not with single-leg stance. After adjusting for age, associations with volumes in MRI, functional mobility (TUG), and gait speed remained significant, whereas associations with cognitive performance attenuated below the significance level despite medium to large effect sizes.

Conclusion

NfL was strongly related to global gray matter and hippocampal atrophy, but not to WMH severity. NfL was also associated with motor performance. Our results suggest that NfL is independently associated with brain atrophy and functional mobility, but is not a reliable marker for cerebral small vessel disease.

Dementia with Lewy bodies: Impact of co-pathologies and implications for clinical trial design

Dementia with Lewy bodies (DLB) is clinically defined by the presence of visual hallucinations, fluctuations, rapid eye movement (REM) sleep behavioral disorder, and parkinsonism. Neuropathologically, it is characterized by the presence of Lewy pathology. However, neuropathological studies have demonstrated the high prevalence of coexistent Alzheimer's disease, TAR DNA-binding protein 43 (TDP-43), and cerebrovascular pathologic cases. Due to their high prevalence and clinical impact on DLB individuals, clinical trials should account for these co-pathologies in their design and selection and the interpretation of biomarkers values and outcomes. Here we discuss the frequency of the different co-pathologies in DLB and their cross-sectional and longitudinal clinical impact. We then evaluate the utility and possible applications of disease-specific and disease-nonspecific biomarkers and how co-pathologies can impact these biomarkers. We propose a framework for integrating multi-modal biomarker fingerprints and step-wise selection and assessment of DLB individuals for clinical trials, monitoring target engagement, and interpreting outcomes in the setting of co-pathologies.

Multiclass prediction of different dementia syndromes based on multi-centric volumetric MRI imaging

INTRODUCTION

Dementia syndromes can be difficult to diagnose. We aimed at building a classifier for multiple dementia syndromes using magnetic resonance imaging (MRI).

METHODS

Atlas-based volumetry was performed on T1-weighted MRI data of 426 patients and 51 controls from the multi-centric German Research Consortium of Frontotemporal Lobar Degeneration including patients with behavioral variant frontotemporal dementia, Alzheimer's disease, the three subtypes of primary progressive aphasia, i.e., semantic, logopenic and nonfluent-agrammatic variant, and the atypical parkinsonian syndromes progressive supranuclear palsy and corticobasal syndrome. Support vector machine classification was used to classify each patient group against controls (binary classification) and all seven diagnostic groups against each other in a multi-syndrome classifier (multiclass classification).

RESULTS

The binary classification models reached high prediction accuracies between 71 and 95% with a chance level of 50%. Feature importance reflected disease-specific atrophy patterns. The multi-syndrome model reached accuracies of more than three times higher than chance level but was far from 100%. Multi-syndrome model performance was not homogenous across dementia syndromes, with better performance in syndromes characterized by regionally specific atrophy patterns. Whereas diseases generally could be classified vs controls more correctly with increasing severity and duration, differentiation between diseases was optimal in disease-specific windows of severity and duration.

DISCUSSION

Results suggest that automated methods applied to MR imaging data can support physicians in diagnosis of dementia syndromes. It is particularly relevant for orphan diseases beside frequent syndromes such as Alzheimer's disease.

Selection of single domain anti-transferrin receptor antibodies for blood-brain barrier transcytosis using a neurotensin based assay and histological assessment of target engagement in a mouse model of Alzheimer's related amyloid-beta pathology

The blood-brain barrier (BBB) presents a major obstacle in developing specific diagnostic imaging agents for many neurological disorders. In this study we aimed to generate single domain anti-mouse transferrin receptor antibodies (anti-mTfR VHHs) to mediate BBB transcytosis as components of novel MRI molecular contrast imaging agents. Anti-mTfR VHHs were produced by immunizing a llama with mTfR, generation of a VHH phage display library, immunopanning, and in vitro characterization of candidates. Site directed mutagenesis was used to generate additional variants. VHH fusions with neurotensin (NT) allowed rapid, hypothermia-based screening for VHH-mediated BBB transcytosis in wild-type mice. One anti-mTfR VHH variant was fused with an anti-amyloid-beta (Aβ) VHH dimer and labeled with fluorescent dye for direct assessment of in vivo target engagement in a mouse model of AD-related Aβ plaque pathology. An anti-mTfR VHH called M1 and variants had binding affinities to mTfR of <1nM to 1.52nM. The affinity of the VHH binding to mTfR correlated with the efficiency of the VHH-NT induced hypothermia effects after intravenous injection of 600 nmol/kg body weight, ranging from undetectable for nonbinding mutants to -6°C for the best mutants. The anti-mTfR VHH variant M1P96H with the strongest hypothermia effect was fused to the anti-Aβ VHH dimer and labeled with Alexa647; the dye-labeled VHH fusion construct still bound both mTfR and Aβ plaques at concentrations as low as 0.22 nM. However, after intravenous injection at 600 nmol/kg body weight into APP/PS1 transgenic mice, there was no detectible labeling of plaques above control levels. Thus, NT-induced hypothermia did not correlate with direct target engagement in cortex, likely because the concentration required for NT-induced hypothermia was lower than the concentration required to produce in situ labeling. These findings reveal an important dissociation between NT-induced hypothermia, presumably mediated by hypothalamus, and direct engagement with Aβ-plaques in cortex. Additional methods to assess anti-mTfR VHH BBB transcytosis will need to be developed for anti-mTfR VHH screening and the development of novel MRI molecular contrast agents.

Iron oxide nanoparticle targeting mechanism and its application in tumor magnetic resonance imaging and therapy

Iron oxide nanoparticles (IONPs) can be applied to targeted drug delivery, targeted diagnosis and treatment of tumors due to their easy preparation, good biocompatibility, low biotoxicity, high imaging quality, high magnetothermal sensitivity and stable targeting after certain surface modifications. However, the complexity of the mechanism of action and their properties has led to there being few clinical applications of IONPs. This review first describes the targeting mechanisms of IONPs and their toxicity issues, then discusses the applications of IONP targeting studies in tumor MRI. Finally, the applications of IONP targeting in tumor therapy are listed. The authors show the advantages of targeting IONPs and hope that the review will increase the possibility of converting IONPs from biomedical applications to clinical applications.

Neuropsychiatric symptoms are associated with exacerbated cognitive impairment in covert cerebral small vessel disease

OBJECTIVES

Neuropsychiatric symptoms are related to disease progression and cognitive decline over time in cerebral small vessel disease (SVD) but their significance is poorly understood in covert SVD. We investigated neuropsychiatric symptoms and their relationships between cognitive and functional abilities in subjects with varying degrees of white matter hyperintensities (WMH), but without clinical diagnosis of stroke, dementia or significant disability.

METHODS

The Helsinki Small Vessel Disease Study consisted of 152 subjects, who underwent brain magnetic resonance imaging (MRI) and comprehensive neuropsychological evaluation of global cognition, processing speed, executive functions, and memory. Neuropsychiatric symptoms were evaluated with the Neuropsychiatric Inventory Questionnaire (NPI-Q, n = 134) and functional abilities with the Amsterdam Instrumental Activities of Daily Living questionnaire (A-IADL, n = 132), both filled in by a close informant.

RESULTS

NPI-Q total score correlated significantly with WMH volume (r_s = 0.20, p = 0.019) and inversely with A-IADL score (r_s = -0.41, p < 0.001). In total, 38% of the subjects had one or more informant-evaluated neuropsychiatric symptom. Linear regressions adjusted for age, sex, and education revealed no direct associations between neuropsychiatric symptoms and cognitive performance. However, there were significant synergistic interactions between neuropsychiatric symptoms and WMH volume on cognitive outcomes. Neuropsychiatric symptoms were also associated with A-IADL score irrespective of WMH volume.

CONCLUSIONS

Neuropsychiatric symptoms are associated with an accelerated relationship between WMH and cognitive impairment. Furthermore, the presence of neuropsychiatric symptoms is related to worse functional abilities. Neuropsychiatric symptoms should be routinely assessed in covert SVD as they are related to worse cognitive and functional outcomes.

Grey matter atrophy in patients with benign multiple sclerosis

BACKGROUND

Brain atrophy appears during the progression of multiple sclerosis (MS) and is associated with the disability caused by the disease.

METHODS

We investigated global and regional grey matter (GM) and white matter (WM) volumes, WM lesion load, and corpus callosum index (CCI), in benign relapsing-remitting MS (BRRMS, n = 35) with and without any treatment and compared those to aggressive relapsing-remitting MS (ARRMS, n = 46). Structures were analyzed by using an automated MRI quantification tool (cNeuro®).

RESULTS

The total brain and cerebral WM volumes were larger in BRRMS than in ARRMS (p = .014, p = .017 respectively). In BRRMS, total brain volumes, regional GM volumes, and CCI were found similar whether or not disease-modifying treatment (DMT) was used. The total (p = .033), as well as subcortical (p = .046) and deep WM (p = .041) lesion load volumes were larger in BRRMS patients without DMT. Cortical GM volumes did not differ between BRRMS and ARRMS, but the volumes of total brain tissue (p = .014) and thalami (p = .003) were larger in patients with BRRMS compared to ARRMS. A positive correlation was found between CCI and whole-brain volume in both BRRMS (r = .73, p < .001) and ARRMS (r = .80, p < .01).

CONCLUSIONS

Thalamic volume is the most prominent measure to differentiate BRRMS and ARRMS. Validation of automated quantification of CCI provides an additional applicable MRI biomarker to detect brain atrophy in MS.

Brain signatures based on structural MRI: Classification for MCI, PMCI, and AD

Structural MRI (sMRI) provides valuable information for understanding neurodegenerative illnesses such as Alzheimer's Disease (AD) since it detects the brain's cerebral atrophy. The development of brain networks utilizing single imaging data-sMRI is an understudied area that has the potential to provide a network neuroscientific viewpoint on the brain. In this paper, we proposed a framework for constructing a brain network utilizing sMRI data, followed by the extraction of signature networks and important regions of interest (ROIs). To construct a brain network using sMRI, nodes are defined as regions described by the brain atlas, and edge weights are determined using a distance measure called the Sorensen distance between probability distributions of gray matter tissue probability maps. The brain signatures identified are based on the changes in the networks of disease and control subjects. To validate the proposed methodology, we first identified the brain signatures and critical ROIs associated with mild cognitive impairment (MCI), progressive MCI (PMCI), and Alzheimer's disease (AD) with 60 reference subjects (15 each of control, MCI, PMCI, and AD). Then, 200 examination subjects (50 each of control, MCI, PMCI, and AD) were selected to evaluate the identified signature patterns. Results demonstrate that the proposed framework is capable of extracting brain signatures and has a number of potential applications in the disciplines of brain mapping, brain communication, and brain network-based applications.

Functional Imaging for Neurodegenerative Diseases

Diagnosis and monitoring of neurodegenerative diseases has changed profoundly over the past twenty years. Biomarkers are now included in most diagnostic procedures as well as in clinical trials. Neuroimaging biomarkers provide access to brain structure and function over the course of neurodegenerative diseases. They have brought new insights into a wide range of neurodegenerative diseases and have made it possible to describe some of the imaging challenges in clinical populations. MRI mainly explores brain structure while molecular imaging, functional MRI and electro- and magnetoencephalography examine brain function. In this paper, we describe and analyse the current and potential contribution of MRI and molecular imaging in the field of neurodegenerative diseases.

White matter changes should not exclude patients with idiopathic normal pressure hydrocephalus from shunt surgery

Introduction

White matter changes (WMC) on brain imaging can be classified as deep white matter hyperintensities (DWMH) or periventricular hyperintensities (PVH) and are frequently seen in patients with idiopathic normal pressure hydrocephalus (iNPH). Contradictory results have been reported on whether preoperative WMC are associated with outcome after shunt surgery in iNPH patients. The aim of this study was to investigate any association between DWMH and PVH and shunt outcome in patients with iNPH, using magnetic resonance volumetry.

Methods

A total of 253 iNPH patients operated with shunt surgery and clinically assessed before and 12 months after surgery were included. All patients were investigated preoperatively with magnetic resonance imaging of the brain. The volumes of DWMH and PVH were quantified on fluid-attenuated inversion recovery images using an in-house semi-automatic volumetric segmentation software (SmartPaint). Shunt outcome was defined as the difference in symptom score between post- and preoperative investigations, measured on the iNPH scale, and shunt response was defined as improvement with ≥ 5 points.

Results

One year after shunt surgery, 51% of the patients were improved on the iNPH scale. When defining improvement as ≥ 5 points on the iNPH scale, there was no significant difference in preoperative volume of WMC between shunt responders and non-responders. If outcome was determined by a continuous variable, a larger volume of PVH was negatively associated with postoperative change in the total iNPH scale (p < 0.05) and negatively associated with improvement in gait (p < 0.01) after adjusting for age, sex, waiting time for surgery, preoperative level of symptoms, Evans’ index, and disproportionately enlarged subarachnoid space hydrocephalus. The volume of DWMH was not associated with shunt outcome.

Conclusions

An association between outcome after shunt surgery and volume of PVH was seen, but there was no difference between shunt responders and non-responders in the volumes of DWMH and PVH. We conclude that preoperative assessment of WMC should not be used to exclude patients with iNPH from shunt surgery.

Autoantibodies targeting neuronal proteins as biomarkers for neurodegenerative diseases

Neurodegenerative diseases (NDDs) are associated with the accumulation of a range of misfolded proteins across the central nervous system and related autoimmune responses, including the generation of antibodies and the activation of immune cells. Both innate and adaptive immunity become mobilized, leading to cellular and humoral effects. The role of humoral immunity in disease onset and progression remains to be elucidated with rising evidence suggestive of positive (protection, repair) and negative (injury, toxicity) outcomes. In this study, we review advances in research of neuron-targeting autoantibodies in the most prevalent NDDs. We discuss their biological origin, molecular diversity and changes in the course of diseases, consider their relevance to the initiation and progression of pathology as well as diagnostic and prognostic significance. It is suggested that the emerging autoimmune aspects of NDDs not only could facilitate the early detection but also might help to elucidate previously unknown facets of pathobiology with relevance to the development of precision medicine.

The changing landscape of neuroimaging in frontotemporal lobar degeneration: from group-level observations to single-subject data interpretation

INTRODUCTION

While the imaging signatures of frontotemporal lobar degeneration (FTLD) phenotypes and genotypes are well-characterised based on group-level descriptive analyses, the meaningful interpretation of single MRI scans remains challenging. Single-subject MRI classification frameworks rely on complex computational models and large training datasets to categorise individual patients into diagnostic subgroups based on distinguishing imaging features. Reliable individual subject data interpretation is hugely important in the clinical setting to expedite the diagnosis and classify individuals into relevant prognostic categories.

AREAS COVERED

This article reviews (1) the neuroimaging studies that propose single-subject MRI classification strategies in symptomatic and pre-symptomatic FTLD, (2) potential practical implications and (3) the limitations of current single-subject data interpretation models.

EXPERT OPINION

Classification studies in FTLD have demonstrated the feasibility of categorising individual subjects into diagnostic groups based on multiparametric imaging data. Preliminary data indicate that pre-symptomatic FTLD mutation carriers may also be reliably distinguished from controls. Despite momentous advances in the field, significant further improvements are needed before these models can be developed into viable clinical applications.

Serum Thioredoxin-80 is associated with age, ApoE4, and neuropathological biomarkers in Alzheimer's disease: a potential early sign of AD

Background

Thioredoxin-80 (Trx80) is a cleavage product from the redox-active protein Thioredoxin-1 and has been previously described as a pro-inflammatory cytokine secreted by immune cells. Previous studies in our group reported that Trx80 levels are depleted in Alzheimer’s disease (AD) brains. However, no studies so far have investigated peripheral Trx80 levels in the context of AD pathology and whether could be associated with the main known AD risk factors and biomarkers.

Methods

Trx80 was measured in serum samples from participants from two different cohorts: the observational memory clinic biobank (GEDOC) (N = 99) with AD CSF biomarker data was available and the population-based lifestyle multidomain intervention trial Finnish Geriatric Intervention Study to Prevent Cognitive Impairment and Disability (FINGER) (N = 47), with neuroimaging data and blood markers of inflammation available. The GEDOC cohort consists of participants diagnosed with subjective cognitive impairment (SCI), mild cognitive impairment (MCI), and AD, whereas the FINGER participants are older adults at-risk of dementia, but without substantial cognitive impairment. One-way ANOVA and multiple comparison tests were used to assess the levels of Trx80 between groups. Linear regression models were used to explore associations of Trx80 with cognition, AD CSF biomarkers (Aβ42, t-tau, p-tau and p-tau/t-tau ratio), inflammatory cytokines, and neuroimaging markers.

Results

In the GEDOC cohort, Trx80 was associated to p-tau/t-tau ratio in the MCI group. In the FINGER cohort, serum Trx80 levels correlated with lower hippocampal volume and higher pro-inflammatory cytokine levels. In both GEDOC and FINGER cohorts, ApoE4 carriers had significantly higher serum Trx80 levels compared to non-ApoE4 carriers. However, Trx80 levels in the brain were further decreased in AD patients with ApoE4 genotype.

Conclusion

We report that serum Trx80 levels are associated to AD disease stage as well as to several risk factors for AD such as age and ApoE4 genotype, which suggests that Trx80 could have potential as serum AD biomarker. Increased serum Trx80 and decreased brain Trx80 levels was particularly seen in ApoE4 carriers. Whether this could contribute to the mechanism by which ApoE4 show increased vulnerability to develop AD would need to be further investigated.

Trial registration

ClinicalTrials.govNCT01041989. Registered on 4 January 2010—retrospectively registered

Supplementary Information

The online version contains supplementary material available at 10.1186/s13195-022-00979-9.

White matter microstructure alterations in frontotemporal dementia: Phenotype-associated signatures and single-subject interpretation

BACKGROUND

Frontotemporal dementias (FTD) include a genetically heterogeneous group of conditions with distinctive molecular, radiological and clinical features. The majority of radiology studies in FTD compare FTD subgroups to healthy controls to describe phenotype- or genotype-associated imaging signatures. While the characterization of group-specific imaging traits is academically important, the priority of clinical imaging is the meaningful interpretation of individual datasets.

METHODS

To demonstrate the feasibility of single-subject magnetic resonance imaging (MRI) interpretation, we have evaluated the white matter profile of 60 patients across the clinical spectrum of FTD. A z-score-based approach was implemented, where the diffusivity metrics of individual patients were appraised with reference to demographically matched healthy controls. Fifty white matter tracts were systematically evaluated in each subject with reference to normative data.

RESULTS

The z-score-based approach successfully detected white matter pathology in single subjects, and group-level inferences were analogous to the outputs of standard track-based spatial statistics.

CONCLUSIONS

Our findings suggest that it is possible to meaningfully evaluate the diffusion profile of single FTD patients if large normative datasets are available. In contrast to the visual review of FLAIR and T2-weighted images, computational imaging offers objective, quantitative insights into white matter integrity changes even at single-subject level.

Brain FDG PET for Short- to Medium-Term Prediction of Further Cognitive Decline and Need for Assisted Living in Acutely Hospitalized Geriatric Patients With Newly Detected Clinically Uncertain Cognitive Impairment

PURPOSE

The aim of this study was to evaluate brain FDG PET for short- to medium-term prediction of cognitive decline, need for assisted living, and survival in acutely hospitalized geriatric patients with newly detected clinically uncertain cognitive impairment (CUCI).

MATERIALS AND METHODS

The study included 96 patients (62 females, 81.4 ± 5.4 years) hospitalized due to (sub)acute admission indications with newly detected CUCI (German Clinical Trials Register DRKS00005041). FDG PET was categorized as "neurodegenerative" (DEG+) or "nonneurodegenerative" (DEG-) based on visual inspection by 2 independent readers. In addition, each individual PET was tested voxel-wise against healthy controls (P < 0.001 uncorrected). The resulting total hypometabolic volume (THV) served as reader-independent measure of the spatial extent of neuronal dysfunction/degeneration. FDG PET findings at baseline were tested for association with the change in living situation and change in vital status 12 to 24 months after PET. The association with the annual change of the CDR-SB (Clinical Dementia Rating Sum of Boxes) after PET was tested in a subsample of 72 patients.

RESULTS

The mean time between PET and follow-up did not differ between DEG+ and DEG- patients (1.37 ± 0.27 vs 1.41 ± 0.27 years, P = 0.539). Annual change of CDR-SB was higher in DEG+ compared with DEG- patients (2.78 ± 2.44 vs 0.99 ± 1.81, P = 0.001), and it was positively correlated with THV (age-corrected Spearman ρ = 0.392, P = 0.001). DEG+ patients moved from at home to assisted living significantly earlier than DEG- patients (P = 0.050). Survival was not associated with DEG status or with THV.

CONCLUSIONS

In acutely hospitalized geriatric patients with newly detected CUCI, the brain FDG PET can contribute to the prediction of further cognitive/functional decline and the need for assisted living within 1 to 2 years.

The influence of cerebrovascular disease in dementia with Lewy bodies and Parkinson's disease dementia

INTRODUCTION

Lewy body dementia (LBD), including dementia with Lewy bodies (DLB) and Parkinson's disease dementia (PDD), is a common form of neurodegenerative dementia. The frequency and influence of comorbid cerebrovascular disease is not understood but has potentially important clinical management implications.

METHODS

A systematic literature search was conducted (Medline and Embase) for studies including participants with DLB and/or PDD assessing cerebrovascular lesions (imaging and pathological studies). They included white matter changes, cerebral amyloid angiopathy (CAA), cerebral microbleeds (CMB), macroscopic infarcts, micro-infarcts and intracerebral haemorrhage.

RESULTS

Of 4411 articles, 63 studies were included. Cerebrovascular lesions commonly studied included white matter changes (41 studies) and CMB (18 studies). There was an increased severity of white matter changes on magnetic resonance imaging (visualized as white matter hyperintensities, WMH), but not neuropathology, in LBD compared to PD without dementia and age-matched controls. CMB prevalence in DLB was highly variable but broadly similar to Alzheimer's disease (AD) (0-48%), with a lobar predominance. No relationship was found between large cortical or small subcortical infarcts or intracerebral haemorrhage and presence of LBD.

CONCLUSION

The underlying mechanisms of WMH in LBD require further exploration, as their increased severity in LBD was not supported by neuropathological examination of white matter. CMB in LBD had a similar prevalence as AD. There is a need for larger studies assessing the influence of cerebrovascular lesions on clinical symptoms, disease progression and outcomes.

Local Brain-Age: A U-Net Model

We propose a new framework for estimating neuroimaging-derived "brain-age" at a local level within the brain, using deep learning. The local approach, contrary to existing global methods, provides spatial information on anatomical patterns of brain ageing. We trained a U-Net model using brain MRI scans from n = 3,463 healthy people (aged 18-90 years) to produce individualised 3D maps of brain-predicted age. When testing on n = 692 healthy people, we found a median (across participant) mean absolute error (within participant) of 9.5 years. Performance was more accurate (MAE around 7 years) in the prefrontal cortex and periventricular areas. We also introduce a new voxelwise method to reduce the age-bias when predicting local brain-age "gaps." To validate local brain-age predictions, we tested the model in people with mild cognitive impairment or dementia using data from OASIS3 (n = 267). Different local brain-age patterns were evident between healthy controls and people with mild cognitive impairment or dementia, particularly in subcortical regions such as the accumbens, putamen, pallidum, hippocampus, and amygdala. Comparing groups based on mean local brain-age over regions-of-interest resulted in large effects sizes, with Cohen's d values >1.5, for example when comparing people with stable and progressive mild cognitive impairment. Our local brain-age framework has the potential to provide spatial information leading to a more mechanistic understanding of individual differences in patterns of brain ageing in health and disease.

Diagnosis of Alzheimer's disease and behavioural variant frontotemporal dementia with machine learning-aided neuropsychological assessment using feature engineering and genetic algorithms

BACKGROUND

Neuropsychological assessment is considered a valid tool in the diagnosis of neurodegenerative disorders. However, there is an important overlap in cognitive profiles between Alzheimer's disease (AD) and behavioural variant frontotemporal dementia (bvFTD), and the usefulness in diagnosis is uncertain. We aimed to develop machine learning-based models for the diagnosis using cognitive tests.

METHODS

Three hundred and twenty-nine participants (170 AD, 72 bvFTD, 87 healthy control [HC]) were enrolled. Evolutionary algorithms, inspired by the process of natural selection, were applied for both mono-objective and multi-objective classification and feature selection. Classical algorithms (NativeBayes, Support Vector Machines, among others) were also used, and a meta-model strategy.

RESULTS

Accuracies for the diagnosis of AD, bvFTD and the differential diagnosis between them were higher than 84%. Algorithms were able to significantly reduce the number of tests and scores needed. Free and Cued Selective Reminding Test, verbal fluency and Addenbrooke's Cognitive Examination were amongst the most meaningful tests.

CONCLUSIONS

Our study found high levels of accuracy for diagnosis using exclusively neuropsychological tests, which supports the usefulness of cognitive assessment in diagnosis. Machine learning may have a role in improving the interpretation and test selection.

New perspectives on cytoskeletal dysregulation and mitochondrial mislocalization in amyotrophic lateral sclerosis

Amyotrophic lateral sclerosis (ALS) is a progressive neurodegenerative disease characterized by selective, early degeneration of motor neurons in the brain and spinal cord. Motor neurons have long axonal projections, which rely on the integrity of neuronal cytoskeleton and mitochondria to regulate energy requirements for maintaining axonal stability, anterograde and retrograde transport, and signaling between neurons. The formation of protein aggregates which contain cytoskeletal proteins, and mitochondrial dysfunction both have devastating effects on the function of neurons and are shared pathological features across several neurodegenerative conditions, including ALS, Alzheimer's disease, Parkinson's disease, Huntington's disease and Charcot-Marie-Tooth disease. Furthermore, it is becoming increasingly clear that cytoskeletal integrity and mitochondrial function are intricately linked. Therefore, dysregulations of the cytoskeletal network and mitochondrial homeostasis and localization, may be common pathways in the initial steps of neurodegeneration. Here we review and discuss known contributors, including variants in genetic loci and aberrant protein activities, which modify cytoskeletal integrity, axonal transport and mitochondrial localization in ALS and have overlapping features with other neurodegenerative diseases. Additionally, we explore some emerging pathways that may contribute to this disruption in ALS.

Generating diagnostic profiles of cognitive decline and dementia using magnetoencephalography

Accurate identification of the underlying cause(s) of cognitive decline and dementia is challenging due to significant symptomatic overlap between subtypes. This study presents a multi-class classification framework for subjects with subjective cognitive decline, mild cognitive impairment, Alzheimer's disease, dementia with Lewy bodies, fronto-temporal dementia and cognitive decline due to psychiatric illness, trained on source-localized resting-state magnetoencephalography data. Diagnostic profiles, describing probability estimates for each of the 6 diagnoses, were assigned to individual subjects. A balanced accuracy rate of 41% and multi-class area under the curve value of 0.75 were obtained for 6-class classification. Classification primarily depended on posterior relative delta, theta and beta power and amplitude-based functional connectivity in the beta and gamma frequency band. Dementia with Lewy bodies (sensitivity: 100%, precision: 20%) and Alzheimer's disease subjects (sensitivity: 51%, precision: 90%) could be classified most accurately. Fronto-temporal dementia subjects (sensitivity: 11%, precision: 3%) were most frequently misclassified. Magnetoencephalography biomarkers hold promise to increase diagnostic accuracy in a noninvasive manner. Diagnostic profiles could provide an intuitive tool to clinicians and may facilitate implementation of the classifier in the memory clinic.

Technical and clinical validation of commercial automated volumetric MRI tools for dementia diagnosis-a systematic review

Developments in neuroradiological MRI analysis offer promise in enhancing objectivity and consistency in dementia diagnosis through the use of quantitative volumetric reporting tools (QReports). Translation into clinical settings should follow a structured framework of development, including technical and clinical validation steps. However, published technical and clinical validation of the available commercial/proprietary tools is not always easy to find and pathways for successful integration into the clinical workflow are varied. The quantitative neuroradiology initiative (QNI) framework highlights six necessary steps for the development, validation and integration of quantitative tools in the clinic. In this paper, we reviewed the published evidence regarding regulatory-approved QReports for use in the memory clinic and to what extent this evidence fulfils the steps of the QNI framework. We summarize unbiased technical details of available products in order to increase the transparency of evidence and present the range of reporting tools on the market. Our intention is to assist neuroradiologists in making informed decisions regarding the adoption of these methods in the clinic. For the 17 products identified, 11 companies have published some form of technical validation on their methods, but only 4 have published clinical validation of their QReports in a dementia population. Upon systematically reviewing the published evidence for regulatory-approved QReports in dementia, we concluded that there is a significant evidence gap in the literature regarding clinical validation, workflow integration and in-use evaluation of these tools in dementia MRI diagnosis.

Diagnostic Efficacy of Voxel-Mirrored Homotopic Connectivity in Vascular Dementia as Compared to Alzheimer's Related Neurodegenerative Diseases-A Resting State fMRI Study

Previous studies have demonstrated that functional connectivity (FC) of different brain regions in resting state function MRI were abnormal in patients suffering from mild cognitive impairment (MCI) and Alzheimer’s disease (AD) when comparing to healthy controls (HC) using seed based, independent component analysis (ICA) or small world network techniques. A new technique called voxel-mirrored homotopic connectivity (VMHC) was used in the current study to evaluate the value of interhemispheric functional connectivity (IFC) as a diagnostic tool to differentiate vascular dementia (VD) from other Alzheimer’s related neurodegenerative diseases. Eighty-three participants were recruited from the university hospital memory clinic. A multidisciplinary panel formed by a neuroradiologist and two geriatricians classified the participants into VD (13), AD (16), MCI (29), and HC (25) based on clinical history, Montreal Cognitive Assessment Hong Kong version (HK-MoCA) neuropsychological score, structural MRI, MR perfusion, and 18-F Flutametamol (amyloid) PET-CT findings of individual subjects. We adopted the calculation method used by Kelly et al. (2011) and Zuo et al. (2010) in obtaining VMHC maps. Specific patterns of VMHC maps were obtained for VD, AD, and MCI to HC comparison. VD showed significant reduction in VMHC in frontal orbital gyrus and gyrus rectus. Increased VMHC was observed in default mode network (DMN), executive control network (ECN), and the remaining salient network (SN) regions. AD showed a reduction of IFC in all DMN, ECN, and SN regions; whereas MCI showed VMHC reduction in vSN, and increased VMHC in DMN and ECN. When combining VMHC values of relevant brain regions, the accuracy was improved to 87%, 92%, and 83% for VD, AD, and MCI from HC, respectively, in receiver operating characteristic (ROC) analysis. Through studying the VMHC maps and using VMHC values in relevant brain regions, VMHC can be considered as a reliable diagnostic tool for VD, AD, and MCI from HC.

Synergistic associations of cognitive and motor impairments with functional outcome in covert cerebral small vessel disease

BACKGROUND

Cognitive and motor impairments are the key clinical manifestations of cerebral small vessel disease (SVD), but their combined effects on functional outcome have not been elucidated. This study investigated the interactions and mediating effects of cognitive and motor functions on instrumental activities of daily living (IADL) and quality of life in older individuals with various degrees of white matter hyperintensities (WMH).

METHODS

Participants of the Helsinki Small Vessel Disease Study (n = 152) were assessed according to an extensive clinical, physical, neuropsychological and MRI protocol. Volumes of WMH and gray matter (GM) were obtained with automated segmentation.

RESULTS

Cognitive (global cognition, executive functions, processing speed, memory) and motor functions (gait speed, single-leg stance, timed up-and-go) had strong interrelations with each other, and they were significantly associated with IADL, quality of life as well as WMH and GM volumes. A consistent pattern on significant interactions between cognitive and motor functions was found on informant-evaluated IADL, but not on self-evaluated quality of life. The association of WMH volume with IADL was mediated by global cognition, whereas the association of GM volume with IADL was mediated by global cognition and timed up-and-go performance.

CONCLUSION

The results highlight the complex interplay and synergism between motor and cognitive abilities on functional outcome in SVD. The combined effect of motor and cognitive disturbances on IADL is likely to be greater than their individual effects. Patients with both impairments are at disproportionate risk for poor outcome. WMH and brain atrophy contribute to disability through cognitive and motor impairment.

Automatic quality control of brain T1-weighted magnetic resonance images for a clinical data warehouse

Many studies on machine learning (ML) for computer-aided diagnosis have so far been mostly restricted to high-quality research data. Clinical data warehouses, gathering routine examinations from hospitals, offer great promises for training and validation of ML models in a realistic setting. However, the use of such clinical data warehouses requires quality control (QC) tools. Visual QC by experts is time-consuming and does not scale to large datasets. In this paper, we propose a convolutional neural network (CNN) for the automatic QC of 3D T1-weighted brain MRI for a large heterogeneous clinical data warehouse. To that purpose, we used the data warehouse of the hospitals of the Greater Paris area (Assistance Publique-Hôpitaux de Paris [AP-HP]). Specifically, the objectives were: 1) to identify images which are not proper T1-weighted brain MRIs; 2) to identify acquisitions for which gadolinium was injected; 3) to rate the overall image quality. We used 5000 images for training and validation and a separate set of 500 images for testing. In order to train/validate the CNN, the data were annotated by two trained raters according to a visual QC protocol that we specifically designed for application in the setting of a data warehouse. For objectives 1 and 2, our approach achieved excellent accuracy (balanced accuracy and F1-score >90%), similar to the human raters. For objective 3, the performance was good but substantially lower than that of human raters. Nevertheless, the automatic approach accurately identified (balanced accuracy and F1-score >80%) low quality images, which would typically need to be excluded. Overall, our approach shall be useful for exploiting hospital data warehouses in medical image computing.

Cerebrovascular disease, neurodegeneration, and clinical phenotype in dementia with Lewy bodies

We investigated whether cerebrovascular disease contributes to neurodegeneration and clinical phenotype in dementia with Lewy bodies (DLB). Regional cortical thickness and subcortical gray matter volumes were estimated from structural magnetic resonance imaging (MRI) in 165 DLB patients. Cortical and subcortical infarcts were recorded and white matter hyperintensities (WMHs) were assessed. Subcortical only infarcts were more frequent (13.3%) than cortical only infarcts (3.1%) or both subcortical and cortical infarcts (2.4%). Infarcts, irrespective of type, were associated with WMHs. A higher WMH volume was associated with thinner orbitofrontal, retrosplenial, and posterior cingulate cortices, smaller thalamus and pallidum, and larger caudate volume. A higher WMH volume was associated with the presence of visual hallucinations and lower global cognitive performance, and tended to be associated with the absence of probable rapid eye movement sleep behavior disorder. Presence of infarcts was associated with the absence of parkinsonism. We conclude that cerebrovascular disease is associated with gray matter neurodegeneration in patients with probable DLB, which may have implications for the multifactorial treatment of probable DLB.