Automatic "pipeline" analysis of 3-D MRI data for clinical trials: application to multiple sclerosis

Distinct Characteristics of Suspected Non-Alzheimer Pathophysiology in Relation to Cognitive Status and Cerebrovascular Burden

PURPOSE OF THE REPORT

This study investigated the prevalence and clinical characteristics of suspected non-Alzheimer disease pathophysiology (SNAP) across varying cognitive statuses and cerebral small vessel disease (CSVD) burden.

PATIENTS AND METHODS

We included 1992 participants with cognitive status categorized as cognitively unimpaired, mild cognitive impairment, or dementia. β-amyloid (Aβ, A) positivity was assessed by Aβ PET, and neurodegeneration (N) positivity was determined through hippocampal volume. Participants were further divided by the presence or absence of severe CSVD. The clinical and imaging characteristics of A-N+ (SNAP) group were compared with those of the A-N- and A+N+ groups.

RESULTS

SNAP participants were older and had more vascular risk factors compared with A-N- and A+N+ in the CSVD(-) cohort. SNAP and A+N+ showed similar cortical thinning. At the dementia stage, SNAP had a cognitive trajectory similar to A+N+ in the CSVD(-) cohort. However, SNAP exhibited less cognitive decline than A+N+ in the CSVD(+) cohort.

CONCLUSIONS

SNAP is characterized by distinct clinical and imaging characteristics; however, it does not necessarily indicate a benign prognosis, particularly at the dementia stage. These findings highlight the need to assess SNAP in relation to the cognitive stage and CSVD presence to better understand its progression and guide interventions.

Brain Perfusion, Atrophy, and Dopaminergic Changes in Amyloid Negative Logopenic Primary Progressive Aphasia

Although most cases of logopenic variant primary progressive aphasia (lvPPA) are caused by Alzheimer's disease (AD), Lewy body disease (LBD) has also been reported. We assessed brain perfusion, atrophy, dopamine transporter (DAT) uptake, and language function among patients with lvPPA based on beta-amyloid. Thirty-three patients with lvPPA and 28 healthy controls (HCs) underwent MRI, 18F-florbetaben PET, and early- and late-phase DAT PET. All patients completed a language test. General linear models were applied to investigate the association of brain imaging with the aphasia quotient (AQ) and repetition scores. 20 (60.6%) and 13 (39.4%) of the lvPPA patients were amyloid-positive (lvPPAA+) and -negative (lvPPAA-), respectively. Language function was comparable between groups. Compared to HCs, the lvPPAA+ had lower perfusion across widespread brain regions, the lvPPAA- had lower perfusion in the left supramarginal and angular gyri, and both groups had lower DAT in the left caudate and bilateral substantia nigra. In the lvPPAA-, AQ and repetition scores were positively correlated with perfusion in the left temporal and inferior parietal cortices, with perfusion in the left supramarginal gyrus mediating the effect of left substantia nigra DAT. Although AD is the most common underlying pathology of lvPPA, LBD may contribute to the logopenic phenotype.

Early childhood stress and amygdala structure in children and adolescents with neurodevelopmental disorders

Children and adolescents with neurodevelopmental disorders such as autism spectrum disorder (ASD) and attention deficit hyperactivity disorder (ADHD) may be more susceptible to early life stress compared to their neurotypical peers. This increased susceptibility may be linked to regionally-specific changes in the striatum and amygdala, brain regions sensitive to stress and critical for shaping maladaptive behavioural responses. This study examined early life stress and its impact on striatal and amygdala development in 62 children and adolescents (35 males, mean age = 10.12 years, SD = 3.6) with ASD (n = 14), ADHD (n = 28), or typical development (TD, n = 20) across two cohorts. We assessed stress from various sources, including from the family environment, loss of loved ones, social stress, and illness/injury. We further examined parenting styles as potential moderators of the effects of early life stress. Volumes of the striatum and amygdala were extracted using an automatic segmentation algorithm. Significant group differences in childhood stress exposure were observed (F = 3.29, df = 8, p = 0.002), with autistic children facing more early life stressors (social stress, illness/injury) compared to those with ADHD and neurotypical peers (both, p < 0.002). In autistic children, amygdala volumes were significantly associated with early life stress related to the familial environment, experiences of significant loss, and illness/injury (all, p < 0.03). Positive parenting moderated these effects. These findings suggest that autistic children are more likely to experience early life stress and exhibit region-specific changes in the amygdala, a key brain region implicated in emotional processing and stress responses. This underscores the need for targeted interventions to support autistic children in managing early life stress to potentially mitigate its impact on brain development.

White matter hyperintensity shape is related to long-term progression of cerebrovascular disease in community-dwelling older adults

White matter hyperintensity (WMH) shape is associated with long-term dementia risk in community-dwelling older adults, however, the underlying structural correlates of this association are unknown. We therefore aimed to investigate the association between baseline WMH shape and cerebrovascular disease progression over time in community-dwelling older adults. The association of WMH shape and cerebrovascular disease markers was investigated using linear and logistic regression models in the Age, Gene/Environment Susceptibility-Reykjavik (AGES) study (n = 2297; average time to follow-up: 5.2 years). A more irregular shape of periventricular/confluent WMH at baseline was associated with a larger increase in WMH volume, and with occurrence of new subcortical infarcts, new microbleeds, new enlarged perivascular spaces, and new cerebellar infarcts at the 5.2-year follow-up (all p < 0.05). Furthermore, less elongated and more irregularly shaped deep WMHs were associated with a larger increase in WMH volume, and new cortical infarcts at follow-up (p < 0.05). A less elongated shape of deep WMH was associated with new microbleeds at follow-up (p < 0.05). Our findings show that WMH shape may be indicative of the type of cerebrovascular disease marker progression. This underlines the significance of WMH shape to aid in the assessment of cerebrovascular disease progression.

HEDN: multi-oriented hierarchical extraction and dual-frequency decoupling network for 3D medical image segmentation

Previous 3D encoder-decoder segmentation architectures struggled with fine-grained feature decomposition, resulting in unclear feature hierarchies when fused across layers. Furthermore, the blurred nature of contour boundaries in medical imaging limits the focus on high-frequency contour features. To address these challenges, we propose a Multi-oriented Hierarchical Extraction and Dual-frequency Decoupling Network (HEDN), which consists of three modules: Encoder-Decoder Module (E-DM), Multi-oriented Hierarchical Extraction Module (Multi-HEM), and Dual-frequency Decoupling Module (Dual-DM). The E-DM performs the basic encoding and decoding tasks, while Multi-HEM decomposes and fuses spatial and slice-level features in 3D, enriching the feature hierarchy by weighting them through 3D fusion. Dual-DM separates high-frequency features from the reconstructed network using self-supervision. Finally, the self-supervised high-frequency features separated by Dual-DM are inserted into the process following Multi-HEM, enhancing interactions and complementarities between contour features and hierarchical features, thereby mutually reinforcing both aspects. On the Synapse dataset, HEDN outperforms existing methods, boosting Dice Similarity Score (DSC) by 1.38% and decreasing 95% Hausdorff Distance (HD95) by 1.03 mm. Likewise, on the Automatic Cardiac Diagnosis Challenge (ACDC) dataset, HEDN achieves  0.5% performance gains across all categories.

Factors associated with cognitive reserve according to education level

INTRODUCTION

We investigated distinctive factors associated with cognitive reserve (CR) based on education level.

METHODS

Among 1247 participants who underwent neuropsychological assessment, amyloid positron emission tomography, and brain magnetic resonance imaging, 336 participants with low education (≤6 years) and 697 with high education (≥12 years) were selected. CR was measured as the difference between the predicted and observed value of cognitive function based on cortical thickness. Multiple linear regression was conducted in each group after controlling for age and sex.

RESULTS

In the low-education group, low literacy, long sleep duration(>8 h/day), and diabetes were negatively associated with CR, whereas cognitive and physical activity were positively associated with CR. In the high-education group, cognitive activity was positively related to CR, whereas low literacy, long sleep duration (> 8 h/day), and depression were negatively related to CR.

DISCUSSION

This study provides insights into different strategies for enhancing CR based on educational background.

HIGHLIGHTS

Factors associated with cognitive reserve (CR) varied according to the education level. Diabetes and physical activity were associated with CR in the low-education group. Depression was related to CR in the high-education group. Low literacy, sleep duration, and cognitive activity were associated with CR in both groups. Dementia-prevention strategies should be tailored according to educational level.

Risk Factors for Rapid Cognitive Decline in Amyloid-Negative Individuals Without Cognitive Impairment or With Early-Stage Cognitive Loss in Screening Tests

PURPOSE

Although rapid cognitive decline (RCD) is an important unfavorable prognostic factor, not much is known about it, especially in amyloid-negative individuals. The purpose of this study was to investigate risk factors for RCD in amyloid-negative individuals.

PATIENTS AND METHODS

We retrospectively enrolled 741 individuals who were either cognitively unimpaired or had early-stage cognitive ability loss and who underwent 18 F-florbetaben (FBB) (n = 402) or 18 F-flutemetamol (FMM) (n = 339) PET/CT. Based on visual and semiquantitative (SUV ratio [SUVR]-based) analysis, the following amyloid-negative groups were established: visual-negative FBB (n = 232), visual-negative FMM (n = 161), SUVR-negative FBB (n = 104), and SUVR-negative FMM (n = 101). Univariable and multivariable logistic regression analyses were performed for RCD using 5 SUVRs, 5 cortical thicknesses, and 5 neuropsychological domains and clinico-demographic factors.

RESULTS

In the amyloid-negative groups, a decline in language function was commonly identified as a significant risk factor for RCD ( P = 0.0044 in the visual-negative FBB group, P = 0.0487 in the visual-negative FMM group, P = 0.0031 in the SUVR-negative FBB group, and P = 0.0030 in the SUVR-negative FMM group). In addition, declines in frontal/executive function, frontal SUVR, and parietal SUVR; a longer duration of education; and mild cognitive decline in the amyloid-negative groups were also significant risk factors for RCD.

CONCLUSIONS

Even in amyloid-negative individuals without cognitive impairment or with early-stage cognitive ability loss, those with decreased language and frontal/executive functions on neuropsychological testing are at risk of progression to RCD.

Association between focal amyloid deposition and cognitive impairment in individuals below the amyloid threshold

Purpose

This study aimed to investigate the characteristics of individuals with amyloid levels below the threshold. To achieve this, we differentiated between two groups: those with global amyloid negativity but focal deposition [G(-)F(+)] and those without focal deposition [G(-)F(-)].

Materials and methods

A total of 2,677 participants were diagnosed with cognitive unimpairment (CU) or mild cognitive impairment (MCI). MRI-based regional centiloid (CL) values were used to establish threshold values for each brain region. After applying a cutoff of 20 rdcCL to identify amyloid positivity, participants who were globally amyloid-negative were grouped into three categories: those who showed focal amyloid uptake [G(-)F(+)], individuals without focal amyloid deposition but with relatively high CL(HC) levels comparable to those in the focal uptake group [G(-)F(-) HC)], and those with relatively low CL(LC) levels [G(-)F(-) LC]. We compared the neuropsychological test results and brain structural changes between these groups using ANCOVA.

Results

The G(-)F(+) group demonstrated a lower cortical thickness (P < 0.001) than the G(-)F(-) HC group. In neuropsychological tests, the G(-)F(+) group exhibited lower the Seoul Verbal Learning Test delayed recall (SVLT-DR) and Mini Mental State Examination (MMSE), and showed progressed clinical status in the clinical dementia rating-sum of boxes (CDR-SOB) compared to the G(-)F(-) HC group (P < 0.001). The subsequent sensitivity analyses confirmed the persistence of these findings.

Conclusions

Individuals with focal amyloid deposition [G(-)F(+)] exhibited higher rates of cognitive impairment compared to patients with similar levels of amyloid, underscoring the importance of monitoring the progression of focal uptake, even when it remains below the amyloid threshold.

The genetics of spatiotemporal variation in cortical thickness in youth

Prior studies have shown strong genetic effects on cortical thickness (CT), structural covariance, and neurodevelopmental trajectories in childhood and adolescence. However, the importance of genetic factors on the induction of spatiotemporal variation during neurodevelopment remains poorly understood. Here, we explore the genetics of maturational coupling by examining 308 MRI-derived regional CT measures in a longitudinal sample of 677 twins and family members. We find dynamic inter-regional genetic covariation in youth, with the emergence of regional subnetworks in late childhood and early adolescence. Three critical neurodevelopmental epochs in genetically-mediated maturational coupling were identified, with dramatic network strengthening near eleven years of age. These changes are associated with statistically-significant (empirical p-value <0.0001) increases in network strength as measured by average clustering coefficient and assortativity. We then identify genes from the Allen Human Brain Atlas with similar co-expression patterns to genetically-mediated structural covariation in children. This set was enriched for genes involved in potassium transport and dendrite formation. Genetically-mediated CT-CT covariance was also strongly correlated with expression patterns for genes located in cells of neuronal origin.

Korea-Registries to Overcome Dementia and Accelerate Dementia Research (K-ROAD): A Cohort for Dementia Research and Ethnic-Specific Insights

Background and Purpose

Dementia, particularly Alzheimer's disease, is a significant global health concern, with early diagnosis and treatment development being critical goals. While numerous cohorts have advanced dementia research, there is a lack of comprehensive data on ethnic differences, particularly for the Korean population. The Korea-Registries to Overcome Dementia and Accelerate Dementia Research (K-ROAD) aims to establish a large-scale, hospital-based dementia cohort to address this gap, with a focus on understanding disease progression, developing early diagnostics, and supporting treatment advancements specific to the Korean population.

Methods

K-ROAD comprises multiple prospective cohorts. Participants underwent clinical evaluations, neuroimaging, and biomarker analysis, with data collected on a range of clinical and genomic markers.

Results

As of December 2023, K-ROAD has recruited over 5,800 participants, including individuals across the Alzheimer's clinical syndrome, subcortical vascular cognitive impairment, and frontotemporal dementia spectra. Preliminary findings highlight significant ethnic differences in amyloid positivity, cognitive decline, and biomarker profiles, compared to Western cohorts.

Conclusions

The K-ROAD cohort offers a unique and critical resource for dementia research, providing insights into ethnic-specific disease characteristics and biomarker profiles. These findings will contribute to the development of personalized diagnostic and therapeutic approaches to dementia, enhancing global understanding of the disease.

Clinical application of sparse canonical correlation analysis to detect genetic associations with cortical thickness in Alzheimer's disease

Introduction

Alzheimer's disease (AD) is a progressive neurodegenerative disease characterized by cerebral cortex atrophy. In this study, we used sparse canonical correlation analysis (SCCA) to identify associations between single nucleotide polymorphisms (SNPs) and cortical thickness in the Korean population. We also investigated the role of the SNPs in neurological outcomes, including neurodegeneration and cognitive dysfunction.

Methods

We recruited 1125 Korean participants who underwent neuropsychological testing, brain magnetic resonance imaging, positron emission tomography, and microarray genotyping. We performed group-wise SCCA in Aβ negative (-) and Aβ positive (+) groups. In addition, we performed mediation, expression quantitative trait loci, and pathway analyses to determine the functional role of the SNPs.

Results

We identified SNPs related to cortical thickness using SCCA in Aβ negative and positive groups and identified SNPs that improve the prediction performance of cognitive impairments. Among them, rs9270580 was associated with cortical thickness by mediating Aβ uptake, and three SNPs (rs2271920, rs6859, rs9270580) were associated with the regulation of CHRNA2, NECTIN2, and HLA genes.

Conclusion

Our findings suggest that SNPs potentially contribute to cortical thickness in AD, which in turn leads to worse clinical outcomes. Our findings contribute to the understanding of the genetic architecture underlying cortical atrophy and its relationship with AD.

Deep learning-based quantification of brain atrophy using 2D T1-weighted MRI for Alzheimer's disease classification

Background

Determining brain atrophy is crucial for the diagnosis of neurodegenerative diseases. Despite detailed brain atrophy assessments using three-dimensional (3D) T1-weighted magnetic resonance imaging, their practical utility is limited by cost and time. This study introduces deep learning algorithms for quantifying brain atrophy using a more accessible two-dimensional (2D) T1, aiming to achieve cost-effective differentiation of dementia of the Alzheimer's type (DAT) from cognitively unimpaired (CU), while maintaining or exceeding the performance obtained with T1-3D individuals and to accurately predict AD-specific atrophy similarity and atrophic changes [W-scores and Brain Age Index (BAI)].

Methods

Involving 924 participants (478 CU and 446 DAT), our deep learning models were trained on cerebrospinal fluid (CSF) volumes from 2D T1 images and compared with 3D T1 images. The performance of the models in differentiating DAT from CU was assessed using receiver operating characteristic analysis. Pearson's correlation analyses were used to evaluate the relations between 3D T1 and 2D T1 measurements of cortical thickness and CSF volumes, AD-specific atrophy similarity, W-scores, and BAIs.

Results

Our deep learning models demonstrated strong correlations between 2D and 3D T1-derived CSF volumes, with correlation coefficients r ranging from 0.805 to 0.971. The algorithms based on 2D T1 accurately distinguished DAT from CU with high accuracy (area under the curve values of 0.873), which were comparable to those of algorithms based on 3D T1. Algorithms based on 2D T1 image-derived CSF volumes showed high correlations in AD-specific atrophy similarity (r = 0.915), W-scores for brain atrophy (0.732 ≤ r ≤ 0.976), and BAIs (r = 0.821) compared with those based on 3D T1 images.

Conclusion

Deep learning-based analysis of 2D T1 images is a feasible and accurate alternative for assessing brain atrophy, offering diagnostic precision comparable to that of 3D T1 imaging. This approach offers the advantage of the availability of T1-2D imaging, as well as reduced time and cost, while maintaining diagnostic precision comparable to T1-3D.

Alterations in cortical thickness of frontoparietal regions in patients with social anxiety disorder

Although functional changes of the frontal and (para)limbic area for emotional hyper-reactivity and emotional dysregulation are well documented in social anxiety disorder (SAD), prior studies on structural changes have shown mixed results. This study aimed to identify differences in cortical thickness between SAD and healthy controls (CON). Thirty-five patients with SAD and forty-two matched CON underwent structural magnetic resonance imaging. A vertex-based whole brain and regional analyses were conducted for between-group comparison. The whole-brain analysis revealed increased cortical thickness in the left insula, left superior parietal lobule, left superior temporal gyrus, and left frontopolar cortex in patients with SAD compared to CON, as well as decreased thickness in the left superior/middle frontal gyrus and left fusiform gyrus in patients (after multiple-correction). The results from the ROI analysis did not align with these findings at the statistically significant level after multiple corrections. Changes in cortical thickness were not correlated with social anxiety symptoms. While consistent results were not obtained from different analysis methods, the results from the whole-brain analysis suggest that patients with SAD exhibit distinct neural deficits in areas involved in salience, attention, and socioemotional processing.

Deep learning model for individualized trajectory prediction of clinical outcomes in mild cognitive impairment

Objectives

Accurately predicting when patients with mild cognitive impairment (MCI) will progress to dementia is a formidable challenge. This work aims to develop a predictive deep learning model to accurately predict future cognitive decline and magnetic resonance imaging (MRI) marker changes over time at the individual level for patients with MCI.

Methods

We recruited 657 amnestic patients with MCI from the Samsung Medical Center who underwent cognitive tests, brain MRI scans, and amyloid-β (Aβ) positron emission tomography (PET) scans. We devised a novel deep learning architecture by leveraging an attention mechanism in a recurrent neural network. We trained a predictive model by inputting age, gender, education, apolipoprotein E genotype, neuropsychological test scores, and brain MRI and amyloid PET features. Cognitive outcomes and MRI features of an MCI subject were predicted using the proposed network.

Results

The proposed predictive model demonstrated good prediction performance (AUC = 0.814 ± 0.035) in five-fold cross-validation, along with reliable prediction in cognitive decline and MRI markers over time. Faster cognitive decline and brain atrophy in larger regions were forecasted in patients with Aβ (+) than with Aβ (-).

Conclusion

The proposed method provides effective and accurate means for predicting the progression of individuals within a specific period. This model could assist clinicians in identifying subjects at a higher risk of rapid cognitive decline by predicting future cognitive decline and MRI marker changes over time for patients with MCI. Future studies should validate and refine the proposed predictive model further to improve clinical decision-making.

Insulin-like growth factor binding protein-2 in at-risk adults and autopsy-confirmed Alzheimer brains

Insulin, insulin-like growth factors (IGF) and their receptors are highly expressed in the adult hippocampus. Thus, disturbances in the insulin-IGF signalling pathway may account for the selective vulnerability of the hippocampus to nascent Alzheimer's disease (AD) pathology. In the present study, we examined the predominant IGF-binding protein in the CSF, IGFBP2. CSF was collected from 109 asymptomatic members of the parental history-positive PREVENT-AD cohort. CSF levels of IGFBP2, core AD and synaptic biomarkers were measured using proximity extension assay, ELISA and mass spectrometry. Cortical amyloid-beta (Aβ) and tau deposition were examined using 18F-NAV4694 and flortaucipir. Cognitive assessments were performed during up to 8 years of follow-up, using the Repeatable Battery for the Assessment of Neuropsychological Status. T1-weighted structural MRI scans were acquired, and neuroimaging analyses were performed on pre-specified temporal and parietal brain regions. Next, in an independent cohort, we allocated 241 dementia-free ADNI-1 participants into four stages of AD progression based on the biomarkers CSF Aβ42 and total-tau (t-tau). In this analysis, differences in CSF and plasma IGFBP2 levels were examined across the pathological stages. Finally, IGFBP2 mRNA and protein levels were examined in the frontal cortex of 55 autopsy-confirmed AD and 31 control brains from the Quebec Founder Population (QFP) cohort, a unique population isolated from Eastern Canada. CSF IGFBP2 progressively increased over 5 years in asymptomatic PREVENT-AD participants. Baseline CSF IGFBP2 was positively correlated with CSF AD biomarkers and synaptic biomarkers, and negatively correlated with longitudinal changes in delayed memory (P = 0.024) and visuospatial abilities (P = 0.019). CSF IGFBP2 was negatively correlated at a trend-level with entorhinal cortex volume (P = 0.082) and cortical thickness in the piriform (P = 0.039), inferior temporal (P = 0.008), middle temporal (P = 0.014) and precuneus (P = 0.033) regions. In ADNI-1, CSF (P = 0.009) and plasma (P = 0.001) IGFBP2 were significantly elevated in Stage 2 [CSF Aβ(+)/t-tau(+)]. In survival analyses in ADNI-1, elevated plasma IGFBP2 was associated with a greater rate of AD conversion (hazard ratio = 1.62, P = 0.021). In the QFP cohort, IGFBP2 mRNA was reduced (P = 0.049); however, IGFBP2 protein levels did not differ in the frontal cortex of autopsy-confirmed AD brains (P = 0.462). Nascent AD pathology may induce an upregulation in IGFBP2 in asymptomatic individuals. CSF and plasma IGFBP2 may be valuable markers for identifying CSF Aβ(+)/t-tau(+) individuals and those with a greater risk of AD conversion.

Artificial intelligence for volumetric measurement of cerebral white matter hyperintensities on thick-slice fluid-attenuated inversion recovery (FLAIR) magnetic resonance images from multiple centers

We aimed to develop a new artificial intelligence software that can automatically extract and measure the volume of white matter hyperintensities (WMHs) in head magnetic resonance imaging (MRI) using only thick-slice fluid-attenuated inversion recovery (FLAIR) sequences from multiple centers. We enrolled 1092 participants in Japan, comprising the thick-slice Private Dataset. Based on 207 randomly selected participants, neuroradiologists annotated WMHs using predefined guidelines. The annotated images of participants were divided into training (n = 138) and test (n = 69) datasets. The WMH segmentation model comprised a U-Net ensemble and was trained using the Private Dataset. Two other models were trained for validation using either both thin- and thick-slice MRI datasets or the thin-slice dataset alone. The voxel-wise Dice similarity coefficient (DSC) was used as the evaluation metric. The model trained using only thick-slice MRI showed a DSC of 0.820 for the test dataset, which is comparable to the accuracy of human readers. The model trained with the additional thin-slice dataset showed only a slightly improved DSC of 0.822. This automatic WMH segmentation model comprising a U-Net ensemble trained on a thick-slice FLAIR MRI dataset is a promising new method. Despite some limitations, this model may be applicable in clinical practice.

Identification of Distinct Brain MRI Phenotypes and Their Association With Long-Term Dementia Risk in Community-Dwelling Older Adults

BACKGROUND AND OBJECTIVES

Individual brain MRI markers only show at best a modest association with long-term occurrence of dementia. Therefore, it is challenging to accurately identify individuals at increased risk for dementia. We aimed to identify different brain MRI phenotypes by hierarchical clustering analysis based on combined neurovascular and neurodegenerative brain MRI markers and to determine the long-term dementia risk within the brain MRI phenotype subgroups.

METHODS

Hierarchical clustering analysis based on 32 combined neurovascular and neurodegenerative brain MRI markers in community-dwelling individuals of the Age-Gene/Environment Susceptibility Reykjavik Study was applied to identify brain MRI phenotypes. A Cox proportional hazards regression model was used to determine the long-term risk for dementia per subgroup.

RESULTS

We included 3,056 participants and identified 15 subgroups with distinct brain MRI phenotypes. The phenotypes ranged from limited burden, mostly irregular white matter hyperintensity (WMH) shape and cerebral atrophy, mostly irregularly WMHs and microbleeds, mostly cortical infarcts and atrophy, mostly irregularly shaped WMH and cerebral atrophy to multiburden subgroups. Each subgroup showed different long-term risks for dementia (min-max range hazard ratios [HRs] 1.01-6.18; mean time to follow-up 9.9 ± 2.6 years); especially the brain MRI phenotype with mainly WMHs and atrophy showed a large increased risk (HR 6.18, 95% CI 3.37-11.32).

DISCUSSION

Distinct brain MRI phenotypes can be identified in community-dwelling older adults. Our results indicate that distinct brain MRI phenotypes are related to varying long-term risks of developing dementia. Brain MRI phenotypes may in the future assist in an improved understanding of the structural correlates of dementia predisposition.

Behaviour-correlated profiles of cerebellar-cerebral functional connectivity observed in independent neurodevelopmental disorder cohorts

The cerebellum, through its connectivity with the cerebral cortex, plays an integral role in regulating cognitive and affective processes, and its dysregulation can result in neurodevelopmental disorder (NDD)-related behavioural deficits. Identifying cerebellar-cerebral functional connectivity (FC) profiles in children with NDDs can provide insight into common connectivity profiles and their correlation to NDD-related behaviours. 479 participants from the Province of Ontario Neurodevelopmental Disorders (POND) network (typically developing = 93, Autism Spectrum Disorder = 172, Attention Deficit/Hyperactivity Disorder = 161, Obsessive-Compulsive Disorder = 53, mean age = 12.2) underwent resting-state functional magnetic resonance imaging and behaviour testing (Social Communication Questionnaire, Toronto Obsessive-Compulsive Scale, and Child Behaviour Checklist - Attentional Problems Subscale). FC components maximally correlated to behaviour were identified using canonical correlation analysis. Results were then validated by repeating the investigation in 556 participants from an independent NDD cohort provided from a separate consortium (Healthy Brain Network (HBN)). Replication of canonical components was quantified by correlating the feature vectors between the two cohorts. The two cerebellar-cerebral FC components that replicated to the greatest extent were correlated to, respectively, obsessive-compulsive behaviour (behaviour feature vectors, rPOND-HBN = -0.97; FC feature vectors, rPOND-HBN = -0.68) and social communication deficit contrasted against attention deficit behaviour (behaviour feature vectors, rPOND-HBN = -0.99; FC feature vectors, rPOND-HBN = -0.78). The statistically stable (|z| > 1.96) features of the FC feature vectors, measured via bootstrap re-sampling, predominantly comprised of correlations between cerebellar attentional and control network regions and cerebral attentional, default mode, and control network regions. In both cohorts, spectral clustering on FC loading values resulted in subject clusters mixed across diagnostic categories, but no cluster was significantly enriched for any given diagnosis as measured via chi-squared test (p > 0.05). Overall, two behaviour-correlated components of cerebellar-cerebral functional connectivity were observed in two independent cohorts. This suggests the existence of generalizable cerebellar network differences that span across NDD diagnostic boundaries.

Associations Between Structural Covariance Network and Antipsychotic Treatment Response in Schizophrenia

BACKGROUND AND HYPOTHESIS

Schizophrenia is associated with widespread cortical thinning and abnormality in the structural covariance network, which may reflect connectome alterations due to treatment effect or disease progression. Notably, patients with treatment-resistant schizophrenia (TRS) have stronger and more widespread cortical thinning, but it remains unclear whether structural covariance is associated with treatment response in schizophrenia.

STUDY DESIGN

We organized a multicenter magnetic resonance imaging study to assess structural covariance in a large population of TRS and non-TRS, who had been resistant and responsive to non-clozapine antipsychotics, respectively. Whole-brain structural covariance for cortical thickness was assessed in 102 patients with TRS, 77 patients with non-TRS, and 79 healthy controls (HC). Network-based statistics were used to examine the difference in structural covariance networks among the 3 groups. Moreover, the relationship between altered individual differentiated structural covariance and clinico-demographics was also explored.

STUDY RESULTS

Patients with non-TRS exhibited greater structural covariance compared with HC, mainly in the fronto-temporal and fronto-occipital regions, while there were no significant differences in structural covariance between TRS and non-TRS or HC. Higher individual differentiated structural covariance was associated with lower general scores of the Positive and Negative Syndrome Scale in the non-TRS group, but not in the TRS group.

CONCLUSIONS

These findings suggest that reconfiguration of brain networks via coordinated cortical thinning is related to treatment response in schizophrenia. Further longitudinal studies are warranted to confirm if greater structural covariance could serve as a marker for treatment response in this disease.

Distinct spatiotemporal patterns of cortical thinning in Alzheimer's disease-type cognitive impairment and subcortical vascular cognitive impairment

Previous studies on Alzheimer's disease-type cognitive impairment (ADCI) and subcortical vascular cognitive impairment (SVCI) has rarely explored spatiotemporal heterogeneity. This study aims to identify distinct spatiotemporal cortical atrophy patterns in ADCI and SVCI. 1,338 participants (713 ADCI, 208 SVCI, and 417 cognitively unimpaired elders) underwent brain magnetic resonance imaging (MRI), amyloid positron emission tomography, and neuropsychological tests. Using MRI, this study measures cortical thickness in five brain regions (medial temporal, inferior temporal, posterior medial parietal, lateral parietal, and frontal areas) and utilizes the Subtype and Stage Inference (SuStaIn) model to predict the most probable subtype and stage for each participant. SuStaIn identifies two distinct cortical thinning patterns in ADCI (medial temporal: 65.8%, diffuse: 34.2%) and SVCI (frontotemporal: 47.1%, parietal: 52.9%) patients. The medial temporal subtype of ADCI shows a faster decline in attention, visuospatial, visual memory, and frontal/executive domains than the diffuse subtype (p-value < 0.01). However, there are no significant differences in longitudinal cognitive outcomes between the two subtypes of SVCI. Our study provides valuable insights into the distinct spatiotemporal patterns of cortical thinning in patients with ADCI and SVCI, suggesting the potential for individualized therapeutic and preventive strategies to improve clinical outcomes.

Association of Glycemic Variability With Imaging Markers of Vascular Burden, β-Amyloid, Brain Atrophy, and Cognitive Impairment

BACKGROUND AND OBJECTIVE

We aimed to investigate the association between glycemic variability (GV) and neuroimaging markers of white matter hyperintensities (WMH), beta-amyloid (Aβ), brain atrophy, and cognitive impairment.

METHODS

This was a retrospective cohort study that included participants without dementia from a memory clinic. They all had Aβ PET, brain MRI, and standardized neuropsychological tests and had fasting glucose (FG) levels tested more than twice during the study period. We defined GV as the intraindividual visit-to-visit variability in FG levels. Multivariable linear regression and logistic regression were used to identify whether GV was associated with the presence of severe WMH and Aβ uptake with DM, mean FG levels, age, sex, hypertension, and presence of APOE4 allele as covariates. Mediation analyses were used to investigate the mediating effect of WMH and Aβ uptake on the relationship between GV and brain atrophy and cognition.

RESULTS

Among the 688 participants, the mean age was 72.2 years, and the proportion of female participants was 51.9%. Increase in GV was predictive of the presence of severe WMH (coefficient [95% CI] 1.032 [1.012-1.054]; p = 0.002) and increased Aβ uptake (1.005 [1.001-1.008]; p = 0.007). Both WMH and increased Aβ uptake partially mediated the relationship between GV and frontal-executive dysfunction (GV → WMH → frontal-executive; direct effect, -0.319 [-0.557 to -0.080]; indirect effect, -0.050 [-0.091 to -0.008]) and memory dysfunction (GV → Aβ → memory; direct effect, -0.182 [-0.338 to -0.026]; indirect effect, -0.067 [-0.119 to -0.015]), respectively. In addition, increased Aβ uptake completely mediated the relationship between GV and hippocampal volume (indirect effect, -1.091 [-2.078 to -0.103]) and partially mediated the relationship between GV and parietal thickness (direct effect, -0.00101 [-0.00185 to -0.00016]; indirect effect, -0.00016 [-0.00032 to -0.000002]).

DISCUSSION

Our findings suggest that increased GV is related to vascular and Alzheimer risk factors and neurodegenerative markers, which in turn leads to subsequent cognitive impairment. Furthermore, GV can be considered a potentially modifiable risk factor for dementia prevention.

Prediction of Amyloid Positivity in Patients with Subcortical Vascular Cognitive Impairment

Background

Amyloid-β (Aβ) commonly coexists and impacts prognosis in subcortical vascular cognitive impairment (SVCI).

Objective

This study aimed to examine the differences in clinical and neuroimaging variables between Aβ-positive and Aβ-negative SVCI and to propose a prediction model for Aβ positivity in clinically diagnosed SVCI patients.

Methods

A total of 130 patients with SVCI were included in model development, and a separate cohort of 70 SVCI patients was used in external validation. The variables for the prediction model were selected by comparing the characteristics of the Aβ-negative and Aβ-positive SVCI groups. The final model was determined using a stepwise method. The model performance was evaluated using the receiver operating characteristic (ROC) curve and a calibration curve. A nomogram was used for visualization.

Results

Among 130 SVCI patients, 70 (53.8%) were Aβ-positive. The Aβ-positive SVCI group was characterized by older age, tendency to be in the dementia stage, a higher prevalence of APOEɛ4, a lower prevalence of lacune, and more severe medial temporal atrophy (MTA). The final prediction model, which excluded MTA grade following the stepwise method for variable selection, demonstrated good accuracy in distinguishing between Aβ-positive and Aβ-negative SVCI, with an area under the curve (AUC) of 0.80. The external validation demonstrated an AUC of 0.71.

Conclusions

The findings suggest that older age, dementia stage, APOEɛ4 carrier, and absence of lacunes may be predictive of Aβ positivity in clinically diagnosed SVCI patients.

Clinical and Pathological Validation of CT-Based Regional Harmonization Methods of Amyloid PET

PURPOSE

The CT-based regional direct comparison Centiloid (dcCL) method was developed to harmonize and quantify regional β-amyloid (Aβ) burden. In the present study, we aimed to investigate correlations between the CT-based regional dcCL scales and Aβ pathological burdens and to validate the clinical utility using thresholds derived from pathological assessment.

PATIENTS AND METHODS

We included a pathological cohort of 63 cases and a clinical cohort of 4062 participants, and obtained modified Consortium to Establish a Registry for Alzheimer's Disease criteria (mCERAD) scores by assessment of neuritic plaque burdens in multiple areas of each cortical region. PET and CT images were processed using the CT-based regional dcCL method to calculate scales in 6 distinct regions.

RESULTS

The CT-based regional dcCL scales were correlated with neuritic plaque burdens represented by mCERAD scores, globally and regionally ( r = 0.56~0.76). In addition, striatum dcCL scales reflected Aβ involvement in the striatum ( P < 0.001). The regional dcCL scales could predict significant Aβ deposition in specific brain regions with high accuracy: area under the receiver operating characteristic curve of 0.81-0.97 with an mCERAD cutoff of 1.5 and area under the receiver operating characteristic curve of 0.88-0.93 with an mCERAD cutoff of 0.5. When applying the dcCL thresholds of 1.5 mCERAD scores, the G(-)R(+) group showed lower performances in memory and global cognitive functions and had less hippocampal volume compared with the G(-)R(-) group ( P < 0.001). However, when applying the dcCL thresholds of 0.5 mCERAD scores, there were no differences in the global cognitive functions between the 2 groups.

CONCLUSIONS

The thresholds of regional dcCL scales derived from pathological assessments might provide clinicians with a better understanding of biomarker-guided diagnosis and distinguishable clinical phenotypes, which are particularly useful when harmonizing different PET ligands with only PET/CT.

Emerging role of vascular burden in AT(N) classification in individuals with Alzheimer's and concomitant cerebrovascular burdens

OBJECTIVES

Alzheimer's disease (AD) is characterised by amyloid-beta accumulation (A), tau aggregation (T) and neurodegeneration (N). Vascular (V) burden has been found concomitantly with AD pathology and has synergistic effects on cognitive decline with AD biomarkers. We determined whether cognitive trajectories of AT(N) categories differed according to vascular (V) burden.

METHODS

We prospectively recruited 205 participants and classified them into groups based on the AT(N) system using neuroimaging markers. Abnormal V markers were identified based on the presence of severe white matter hyperintensities.

RESULTS

In A+ category, compared with the frequency of Alzheimer's pathological change category (A+T-), the frequency of AD category (A+T+) was significantly lower in V+ group (31.8%) than in V- group (64.4%) (p=0.004). Each AT(N) biomarker was predictive of cognitive decline in the V+ group as well as in the V- group (p<0.001). Additionally, the V+ group showed more severe cognitive trajectories than the V- group in the non-Alzheimer's pathological changes (A-T+, A-N+; p=0.002) and Alzheimer's pathological changes (p<0.001) categories.

CONCLUSION

The distribution and longitudinal outcomes of AT(N) system differed according to vascular burdens, suggesting the importance of incorporating a V biomarker into the AT(N) system.

White matter hyperintensity shape is associated with long-term dementia risk

INTRODUCTION

We aimed to investigate the association between white matter hyperintensity (WMH) shape and volume and the long-term dementia risk in community-dwelling older adults.

METHODS

Three thousand seventy-seven participants (mean age: 75.6 ± 5.2 years) of the Age Gene/Environment Susceptibility (AGES)-Reykjavik study underwent baseline 1.5T brain magnetic resonance imaging and were followed up for dementia (mean follow-up: 9.9 ± 2.6 years).

RESULTS

More irregular shape of periventricular/confluent WMH (lower solidity (hazard ratio (95% confidence interval) 1.34 (1.17 to 1.52), p < .001) and convexity 1.38 (1.28 to 1.49), p < .001); higher concavity index 1.43 (1.32 to 1.54), p < .001) and fractal dimension 1.45 (1.32 to 1.58), p < .001)), higher total WMH volume (1.68 (1.54 to 1.87), p < .001), higher periventricular/confluent WMH volume (1.71 (1.55 to 1.89), p < .001), and higher deep WMH volume (1.17 (1.08 to 1.27), p < .001) were associated with an increased long-term dementia risk.

DISCUSSION

WMH shape markers may in the future be useful in determining patient prognosis and may aid in patient selection for future preventive treatments in community-dwelling older adults.

Spatial association between distributed β-amyloid and tau varies with cognition

Several PET studies have explored the relationship between β-amyloid load and tau uptake at the early stages of Alzheimer's disease (AD) progression. Most of these studies have focused on the linear relationship between β-amyloid and tau at the local level and their synergistic effect on different AD biomarkers. We hypothesize that patterns of spatial association between β-amyloid and tau might be uncovered using alternative association metrics that account for linear as well as more complex, possible nonlinear dependencies. In the present study, we propose a new Canonical Distance Correlation Analysis (CDCA) to generate distinctive spatial patterns of the cross-correlation structure between tau, as measured by [18F]flortaucipir PET, and β-amyloid, as measured by [18F]florbetapir PET, from the Alzheimer's Disease Neuroimaging Initiative (ADNI) study. We found that the CDCA-based β-amyloid scores were not only maximally distance-correlated to tau in cognitively normal (CN) controls and mild cognitive impairment (MCI), but also differentiated between low and high levels of β-amyloid uptake. The most distinctive spatial association pattern was characterized by a spread of β-amyloid covering large areas of the cortex and localized tau in the entorhinal cortex. More importantly, this spatial dependency varies according to cognition, which cannot be explained by the uptake differences in β-amyloid or tau between CN and MCI subjects. Hence, the CDCA-based scores might be more accurate than the amyloid or tau SUVR for the enrollment in clinical trials of those individuals on the path of cognitive deterioration.

Predicting cognitive stage transition using p-tau181, Centiloid, and other measures

BACKGROUND

A combination of plasma phospho-tau (p-tau), amyloid beta (Aβ)-positron emission tomography (PET), brain magnetic resonance imaging, cognitive function tests, and other biomarkers might predict future cognitive decline. This study aimed to investigate the efficacy of combining these biomarkers in predicting future cognitive stage transitions within 3 years.

METHODS

Among the participants in the Korean Brain Aging Study for the Early Diagnosis and Prediction of Alzheimer's Disease (KBASE-V) study, 49 mild cognitive impairment (MCI) and 113 cognitively unimpaired (CU) participants with Aβ-PET and brain imaging data were analyzed.

RESULTS

Older age, increased plasma p-tau181, Aβ-PET positivity, and decreased semantic fluency were independently associated with cognitive stage transitions. Combining age, p-tau181, the Centiloid scale, semantic fluency, and hippocampal volume produced high predictive value in predicting future cognitive stage transition (area under the curve = 0.879).

CONCLUSIONS

Plasma p-tau181 and Centiloid scale alone or in combination with other biomarkers, might predict future cognitive stage transition in non-dementia patients.

HIGHLIGHTS

-Plasma p-tau181 and Centiloid scale might predict future cognitive stage transition. -Combining them or adding other biomarkers increased the predictive value. -Factors that independently associated with cognitive stage transition were demonstrated.

BIANCA-MS: An optimized tool for automated multiple sclerosis lesion segmentation

In this work we present BIANCA-MS, a novel tool for brain white matter lesion segmentation in multiple sclerosis (MS), able to generalize across both the wide spectrum of MRI acquisition protocols and the heterogeneity of manually labeled data. BIANCA-MS is based on the original version of BIANCA and implements two innovative elements: a harmonized setting, tested under different MRI protocols, which avoids the need to further tune algorithm parameters to each dataset; and a cleaning step developed to improve consistency in automated and manual segmentations, thus reducing unwanted variability in output segmentations and validation data. BIANCA-MS was tested on three datasets, acquired with different MRI protocols. First, we compared BIANCA-MS to other widely used tools. Second, we tested how BIANCA-MS performs in separate datasets. Finally, we evaluated BIANCA-MS performance on a pooled dataset where all MRI data were merged. We calculated the overlap using the DICE spatial similarity index (SI) as well as the number of false positive/negative clusters (nFPC/nFNC) in comparison to the manual masks processed with the cleaning step. BIANCA-MS clearly outperformed other available tools in both high- and low-resolution images and provided comparable performance across different scanning protocols, sets of modalities and image resolutions. BIANCA-MS performance on the pooled dataset (SI: 0.72 ± 0.25, nFPC: 13 ± 11, nFNC: 4 ± 8) were comparable to those achieved on each individual dataset (median across datasets SI: 0.72 ± 0.28, nFPC: 14 ± 11, nFNC: 4 ± 8). Our findings suggest that BIANCA-MS is a robust and accurate approach for automated MS lesion segmentation.

Sex differences in cognition and structural covariance-based morphometric connectivity: evidence from 28,000+ UK Biobank participants

There is robust evidence for sex differences in domain-specific cognition, where females typically show an advantage for verbal memory, whereas males tend to perform better in spatial memory. Sex differences in brain connectivity are well documented and may provide insight into these differences. In this study, we examined sex differences in cognition and structural covariance, as an index of morphometric connectivity, of a large healthy sample (n = 28,821) from the UK Biobank. Using T1-weighted magnetic resonance imaging scans and regional cortical thickness values, we applied jackknife bias estimation and graph theory to obtain subject-specific measures of structural covariance, hypothesizing that sex-related differences in brain network global efficiency, or overall covariance, would underlie cognitive differences. As predicted, females demonstrated better verbal memory and males showed a spatial memory advantage. Females also demonstrated faster processing speed, with no observed sex difference in executive functioning. Males showed higher global efficiency, as well as higher regional covariance (nodal strengths) in both hemispheres relative to females. Furthermore, higher global efficiency in males mediated sex differences in verbal memory and processing speed. Findings contribute to an improved understanding of how biological sex and differences in cognition are related to morphometric connectivity as derived from graph-theoretic methods.

A Brain Morphometry Study with Across-Site Harmonization Using a ComBat-Generalized Additive Model in Children and Adolescents

Regional anatomical structures of the brain are intimately connected to functions corresponding to specific regions and the temporospatial pattern of genetic expression and their functions from the fetal period to old age. Therefore, quantitative brain morphometry has often been employed in neuroscience investigations, while controlling for the scanner effect of the scanner is a critical issue for ensuring accuracy in brain morphometric studies of rare orphan diseases due to the lack of normal reference values available for multicenter studies. This study aimed to provide across-site normal reference values of global and regional brain volumes for each sex and age group in children and adolescents. We collected magnetic resonance imaging (MRI) examinations of 846 neurotypical participants aged 6.0-17.9 years (339 male and 507 female participants) from 5 institutions comprising healthy volunteers or neurotypical patients without neurological disorders, neuropsychological disorders, or epilepsy. Regional-based analysis using the CIVET 2.1.0. pipeline provided regional brain volumes, and the measurements were across-site combined using ComBat-GAM harmonization. The normal reference values of global and regional brain volumes and lateral indices in our study could be helpful for evaluating the characteristics of the brain morphology of each individual in a clinical setting and investigating the brain morphology of ultra-rare diseases.

Brain structure alterations in girls with central precocious puberty

Purpose

Central precocious puberty (CPP) is puberty that occurs at an unusually early age with several negative psychological outcomes. There is a paucity of data on the morphological characteristics of the brain in CPP. This study aimed to determine the structural differences in the brain of patients with CPP.

Methods

We performed voxel- and surface-based morphometric analyses of 1.5 T T1-weighted brain images scanned from 15 girls with CPP and 13 age-matched non-CPP controls (NC). All patients with CPP were diagnosed by gonadotropin-releasing hormone (GnRH) stimulation test. The magnetic resonance imaging (MRI) data were evaluated using Levene's test for equality of variances and a two-tailed unpaired t-test for equality of means. False discovery rate correction for multiple comparisons was applied using the Benjamini-Hochberg procedure.

Results

Morphometric analyses of the brain scans identified 33 candidate measurements. Subsequently, increased thickness of the right precuneus was identified in the patients with CPP using general linear models and visualizations of cortical thickness with a t-statistical map and a random field theory map.

Conclusion

The brain scans of the patients with CPP showed specific morphological differences to those of the control. The features of brain morphology in CPP identified in this study could contribute to further understanding the association between CPP and detrimental psychological outcomes.

Challenges of implementing computer-aided diagnostic models for neuroimages in a clinical setting

Advances in artificial intelligence have cultivated a strong interest in developing and validating the clinical utilities of computer-aided diagnostic models. Machine learning for diagnostic neuroimaging has often been applied to detect psychological and neurological disorders, typically on small-scale datasets or data collected in a research setting. With the collection and collation of an ever-growing number of public datasets that researchers can freely access, much work has been done in adapting machine learning models to classify these neuroimages by diseases such as Alzheimer's, ADHD, autism, bipolar disorder, and so on. These studies often come with the promise of being implemented clinically, but despite intense interest in this topic in the laboratory, limited progress has been made in clinical implementation. In this review, we analyze challenges specific to the clinical implementation of diagnostic AI models for neuroimaging data, looking at the differences between laboratory and clinical settings, the inherent limitations of diagnostic AI, and the different incentives and skill sets between research institutions, technology companies, and hospitals. These complexities need to be recognized in the translation of diagnostic AI for neuroimaging from the laboratory to the clinic.

Multiple sclerosis lesion segmentation: revisiting weighting mechanisms for federated learning

Background and introduction

Federated learning (FL) has been widely employed for medical image analysis to facilitate multi-client collaborative learning without sharing raw data. Despite great success, FL's applications remain suboptimal in neuroimage analysis tasks such as lesion segmentation in multiple sclerosis (MS), due to variance in lesion characteristics imparted by different scanners and acquisition parameters.

Methods

In this work, we propose the first FL MS lesion segmentation framework via two effective re-weighting mechanisms. Specifically, a learnable weight is assigned to each local node during the aggregation process, based on its segmentation performance. In addition, the segmentation loss function in each client is also re-weighted according to the lesion volume for the data during training.

Results

The proposed method has been validated on two FL MS segmentation scenarios using public and clinical datasets. Specifically, the case-wise and voxel-wise Dice score of the proposed method under the first public dataset is 65.20 and 74.30, respectively. On the second in-house dataset, the case-wise and voxel-wise Dice score is 53.66, and 62.31, respectively.

Discussions and conclusions

The Comparison experiments on two FL MS segmentation scenarios using public and clinical datasets have demonstrated the effectiveness of the proposed method by significantly outperforming other FL methods. Furthermore, the segmentation performance of FL incorporating our proposed aggregation mechanism can achieve comparable performance to that from centralized training with all the raw data.

Classification and prediction of cognitive trajectories of cognitively unimpaired individuals

Objectives

Efforts to prevent Alzheimer's disease (AD) would benefit from identifying cognitively unimpaired (CU) individuals who are liable to progress to cognitive impairment. Therefore, we aimed to develop a model to predict cognitive decline among CU individuals in two independent cohorts.

Methods

A total of 407 CU individuals from the Alzheimer's Disease Neuroimaging Initiative (ADNI) and 285 CU individuals from the Samsung Medical Center (SMC) were recruited in this study. We assessed cognitive outcomes by using neuropsychological composite scores in the ADNI and SMC cohorts. We performed latent growth mixture modeling and developed the predictive model.

Results

Growth mixture modeling identified 13.8 and 13.0% of CU individuals in the ADNI and SMC cohorts, respectively, as the "declining group." In the ADNI cohort, multivariable logistic regression modeling showed that increased amyloid-β (Aβ) uptake (β [SE]: 4.852 [0.862], p < 0.001), low baseline cognitive composite scores (β [SE]: -0.274 [0.070], p < 0.001), and reduced hippocampal volume (β [SE]: -0.952 [0.302], p = 0.002) were predictive of cognitive decline. In the SMC cohort, increased Aβ uptake (β [SE]: 2.007 [0.549], p < 0.001) and low baseline cognitive composite scores (β [SE]: -4.464 [0.758], p < 0.001) predicted cognitive decline. Finally, predictive models of cognitive decline showed good to excellent discrimination and calibration capabilities (C-statistic = 0.85 for the ADNI model and 0.94 for the SMC model).

Conclusion

Our study provides novel insights into the cognitive trajectories of CU individuals. Furthermore, the predictive model can facilitate the classification of CU individuals in future primary prevention trials.

Cortical Thickness and Brain Glucose Metabolism in Healthy Aging

BACKGROUND AND PURPOSE

We aimed to determine the effect of demographic factors on cortical thickness and brain glucose metabolism in healthy aging subjects.

METHODS

The following tests were performed on 71 subjects with normal cognition: neurological examination, 3-tesla magnetic resonance imaging, ¹⁸F-fluorodeoxyglucose positron-emission tomography, and neuropsychological tests. Cortical thickness and brain metabolism were measured using vertex- and voxelwise analyses, respectively. General linear models (GLMs) were used to determine the effects of age, sex, and education on cortical thickness and brain glucose metabolism. The effects of mean lobar cortical thickness and mean lobar metabolism on neuropsychological test scores were evaluated using GLMs after controlling for age, sex, and education. The intracranial volume (ICV) was further included as a predictor or covariate for the cortical thickness analyses.

RESULTS

Age was negatively correlated with the mean cortical thickness in all lobes (frontal and parietal lobes, p=0.001; temporal and occipital lobes, p<0.001) and with the mean temporal metabolism (p=0.005). Education was not associated with cortical thickness or brain metabolism in any lobe. Male subjects had a lower mean parietal metabolism than did female subjects (p<0.001), while their mean cortical thicknesses were comparable. ICV was positively correlated with mean cortical thickness in the frontal (p=0.016), temporal (p=0.009), and occipital (p=0.007) lobes. The mean lobar cortical thickness was not associated with cognition scores, while the mean temporal metabolism was positively correlated with verbal memory test scores.

CONCLUSIONS

Age and sex affect cortical thickness and brain glucose metabolism in different ways. Demographic factors must therefore be considered in analyses of cortical thickness and brain metabolism.

Motor Symptoms in Early- versus Late-Onset Alzheimer's Disease

BACKGROUND

Age at onset was suggested as one possible risk factor for motor dysfunction in Alzheimer's disease (AD).

OBJECTIVE

We investigated the association of motor symptoms with cognition or neurodegeneration in patients with AD, and whether this association differs by the age at onset.

METHODS

We included 113 amyloid positive AD patients and divided them into early-onset AD (EOAD) and late-onset AD (LOAD), who underwent the Unified Parkinson's Disease Rating Scale (UPDRS)-Part III (=UPDRS) scoring, Mini-Mental State Examination (MMSE)/Clinical Deterioration Rating Sum-of-Boxes (CDR-SOB), and magnetic resonance image (MRI). Multiple linear regression was used to evaluate the association of UPDRS and MMSE/CDR-SOB or MRI neurodegeneration measures, and whether the association differs according to the group.

RESULTS

The prevalence of motor symptoms and their severity did not differ between the groups. Lower MMSE (β= -1.1, p < 0.001) and higher CDR-SOB (β= 2.0, p < 0.001) were significantly associated with higher UPDRS. There was no interaction effect between MMSE/CDR-SOB and AD group on UPDRS. Global or all regional cortical thickness and putaminal volume were negatively associated with UPDRS score, but the interaction effect of neurodegeneration and AD group on UPDRS score was significant only in parietal lobe (p for interaction = 0.035), which showed EOAD to have a more pronounced association between parietal thinning and motor symptoms.

CONCLUSION

Our study suggested that the severity of motor deterioration in AD is related to the severity of cognitive impairment itself rather than age at onset, and motor symptoms might occur through multiple mechanisms including cortical and subcortical atrophy.

Classification of Preschoolers with Low-Functioning Autism Spectrum Disorder Using Multimodal MRI Data

Multimodal imaging studies targeting preschoolers and low-functioning autism spectrum disorder (ASD) patients are scarce. We applied machine learning classifiers to parameters from T1-weighted MRI and DTI data of 58 children with ASD (age 3-6 years) and 48 typically developing controls (TDC). Classification performance reached an accuracy, sensitivity, and specificity of 88.8%, 93.0%, and 83.8%, respectively. The most prominent features were the cortical thickness of the right inferior occipital gyrus, mean diffusivity of the middle cerebellar peduncle, and nodal efficiency of the left posterior cingulate gyrus. Machine learning-based analysis of MRI data was useful in distinguishing low-functioning ASD preschoolers from TDCs. Combination of T1 and DTI improved classification accuracy about 10%, and large-scale multi-modal MRI studies are warranted for external validation.

Distinct volumetric features of cerebrospinal fluid distribution in idiopathic normal-pressure hydrocephalus and Alzheimer's disease

Objective

The aims of the study were to measure the cerebrospinal fluid (CSF) volumes in the lateral ventricle, high-convexity subarachnoid space, and Sylvian fissure region in patients with idiopathic normal-pressure hydrocephalus (INPH) and Alzheimer’s disease (AD), and to evaluate differences in these volumes between INPH and AD groups and healthy controls.

Methods

Forty-nine INPH patients, 59 AD patients, and 26 healthy controls were imaged with automated three-dimensional volumetric MRI.

Results

INPH patients had larger lateral ventricles and CSF spaces of the Sylvian fissure region and smaller high-convexity subarachnoid spaces than other groups, and AD patients had larger lateral ventricles and CSF spaces of the Sylvian fissure region than the control group. The INPH group showed a negative correlation between lateral ventricle and high-convexity subarachnoid space volumes, while the AD group showed a positive correlation between lateral ventricle volume and volume for CSF spaces of the Sylvian fissure region. The ratio of lateral ventricle to high-convexity subarachnoid space volumes yielded an area under the curve of 0.990, differentiating INPH from AD.

Conclusions

Associations between CSF volumes suggest that there might be different mechanisms between INPH and AD to explain their respective lateral ventricular dilations. The ratio of lateral ventricle to high-convexity subarachnoid space volumes distinguishes INPH from AD with good diagnostic sensitivity and specificity. We propose to refer to this ratio as the VOSS (ventricle over subarachnoid space) index.

The pattern of cortical thickness underlying disruptive behaviors in Alzheimer's disease

Background

Disruptive behaviors, including agitation, disinhibition, irritability, and aberrant motor behaviors, are commonly observed in patients with Alzheimer's disease (AD). However, the neuroanatomical basis of these disruptive behaviors is not fully understood.

Objective

To confirm the differences in cortical thickness and surface area between AD patients and healthy controls and to further investigate the features of cortical thickness and surface area associated with disruptive behaviors in patients with AD.

Methods

One hundred seventy-four participants (125 AD patients and 49 healthy controls) were recruited from memory clinics at the Peking University Institute of Sixth Hospital. Disruptive behaviors, including agitation/aggression, disinhibition, irritability/lability, and aberrant motor activity subdomain scores, were evaluated using the Neuropsychiatry Inventory. Both whole-brain vertex-based and region-of-interest-based cortical thickness and surface area analyses were automatically conducted with the CIVET pipeline based on structural magnetic resonance images. Both group-based statistical comparisons and brain-behavior association analyses were performed using general linear models, with age, sex, and education level as covariables.

Results

Compared with healthy controls, the AD patients exhibited widespread reduced cortical thickness, with the most significant thinning located in the medial and lateral temporal and parietal cortex, and smaller surface areas in the left fusiform and left inferior temporal gyrus. High total scores of disruptive behaviors were significantly associated with cortical thinning in several regions that are involved in sensorimotor processing, language, and expression functions. The total score of disruptive behaviors did not show significant associations with surface areas.

Conclusion

We highlight that disruptive behaviors in patients with AD are selectively associated with cortical thickness abnormalities in sensory, motor, and language regions, which provides insights into neuroanatomical substrates underlying disruptive behaviors. These findings could lead to sensory, motor, and communication interventions for alleviating disruptive behaviors in patients with AD.

Cortical neuroanatomical changes related to specific language impairments in primary progressive aphasia

Objective

Language function test-specific neural substrates in Korean patients with primary progressive aphasia (PPA) might differ from those in other causes of dementia and English-speaking PPA patients. We investigated the correlation between language performance tests and cortical thickness to determine neural substrates in Korean patients with PPA.

Materials and methods

Ninety-six patients with PPA were recruited from the memory clinic. To acquire neural substrates, we performed linear regression using the scores of each language test as a predictor, cortical thickness as an outcome and age, sex, years of education, and intracranial volume as confounders.

Results

Poor performance in each language function test was associated with lower cortical thickness in specific cortical regions: (1) object naming and the bilateral anterior to mid-portion of the lateral temporal and basal temporal regions; (2) semantic generative naming and the bilateral anterior to mid-portion of the lateral temporal and basal temporal regions; (3) phonemic generative naming and the left prefrontal and inferior parietal regions; and (4) comprehension and the left posterior portion of the superior and middle temporal regions. In particular, the neural substrates of the semantic generative naming test in PPA patients, left anterior to mid-portion of the lateral and basal temporal regions, quite differed from those in patients with other causes of dementia.

Conclusion

Our findings provide a better understanding of the different pathomechanisms for language impairments among PPA patients from those with other causes of dementia.

Computer-Aided Detection and Diagnosis of Neurological Disorder

Nowadays, neurological problems are more regular, representing a worry to pregnant ladies, guardians, healthy babies, and kids. Neurological problems emerge in a wide assortment of structures, each with its arrangement of beginnings, inconveniences, and results. The conclusion of neurological illnesses is an evolving concern and predominantly troublesome difficulty for current medication. Current diagnosis advancements (e.g., MRI and EEG) produce immense information (in proportion and aspect) as location, checking, and therapy of nervous system illnesses. As a common rule, investigation of that enormous clinical information is performed physically by specialists to distinguish and figure out the irregularities. It is a genuinely troublesome errand for an individual to collect, make due, investigate, and absorb enormous amounts of information through visible review. As an outcome, the specialist has been requesting electronic conclusion frameworks known as "computer-aided diagnosis" that can consequently identify the nervous system irregularities utilizing the essential clinical information. This framework further develops uniformity of findings, builds treatment outcomes, protects lives, and lessens price and time. As of late, few examinations have improved the computer-aided design frameworks for the executives of enormous clinical information for determination appraisal. This paper investigates the difficulties of tremendous clinical information giving. This article fundamentally evaluated and looked at the exhibition of existing AI and comprehensive learning approaches for identifying nervous system illnesses. A far-reaching piece of this concentration also shows different modalities and illness-determined datasets that identify and record pictures, signs, addresses, and so forth. Restricted related works are additionally summed up on nervous system illnesses, as this space has essentially less work zeroed in on illness and recognition rules. A portion of the standard assessment measurements is likewise introduced in this review for improved outcome examination.

A polynomial regression-based approach to estimate relaxation rate maps suitable for multiparametric segmentation of clinical brain MRI studies in multiple sclerosis

BACKGROUND AND OBJECTIVE

Relaxation parameter maps (RPMs) calculated from spin-echo data have provided a basis for the segmentation of normal brain tissues and white matter lesions in multiple sclerosis (MS) MRI studies. However, Conventional Spin-Echo (CSE) sequences, once the core of clinical MRI studies, have been largely replaced by faster ones, which do not allow the calculation a-posteriori of RPMs from clinical studies. Aim of the study was to develop and validate a method to estimate RPMs (pseudo-RPMs) from routine clinical MRI protocols (including 3D-Gradient Echo T1w, FLAIR and fast-T2w sequences), suitable for fully automatic multiparametric segmentation of normal-appearing and pathological brain tissues in MS.

METHODS

The proposed method processes spatially normalized clinical MRI studies through a multistep pipeline, to collect a set of data points of matched signal intensities (from MRI studies) and relaxation parameters (from a CSE-derived digital template and an MS lesion database), which are then fitted by a multiple and multivariate 4-th degree polynomial regression, providing pseudo-RPMs. The method was applied to a dataset of 59 clinical MRI studies providing pseudo-RPMs that were segmented through a method originally developed for the CSE-derived RPMs. Results of the segmentation in 12 studies were used to iteratively optimize method parameters. Accuracy of segmentation of normal-appearing brain tissues from the pseudo-RPMs was assessed by comparing their age-related changes, as measured in 47 clinical studies, against those measured acquired using CSE sequences in a comparable dataset of 47 patients. Lesion segmentation was validated against manual segmentation carried out by three neuroradiologists.

RESULTS

Age-related changes of normal-appearing brain tissue volumes measured using the pseudo-RPMs substantially overlapped those measured using the RPMs obtained from CSE sequences, and segmentation of MS lesions showed a moderate-high spatial overlap with manual segmentation, comparable to that achieved by the widely used Lesion Segmentation Tool on FLAIR images, with a greater volumetric agreement.

CONCLUSIONS

The proposed approach allows calculation from clinical studies of pseudo-RPMs, which are equivalent to those obtainable from CSE sequences, avoiding the need for the acquisition of additional, dedicated sequences for segmentation purposes.

Automatic segmentation of white matter hyperintensities in routine clinical brain MRI by 2D VB-Net: A large-scale study

White matter hyperintensities (WMH) are imaging manifestations frequently observed in various neurological disorders, yet the clinical application of WMH quantification is limited. In this study, we designed a series of dedicated WMH labeling protocols and proposed a convolutional neural network named 2D VB-Net for the segmentation of WMH and other coexisting intracranial lesions based on a large dataset of 1,045 subjects across various demographics and multiple scanners using 2D thick-slice protocols that are more commonly applied in clinical practice. Using our labeling pipeline, the Dice consistency of the WMH regions manually depicted by two observers was 0.878, which formed a solid basis for the development and evaluation of the automatic segmentation system. The proposed algorithm outperformed other state-of-the-art methods (uResNet, 3D V-Net and Visual Geometry Group network) in the segmentation of WMH and other coexisting intracranial lesions and was well validated on datasets with thick-slice magnetic resonance (MR) images and the 2017 medical image computing and computer assisted intervention WMH Segmentation Challenge dataset (with thin-slice MR images), all showing excellent effectiveness. Furthermore, our method can subclassify WMH to display the WMH distributions and is very lightweight. Additionally, in terms of correlation to visual rating scores, our algorithm showed excellent consistency with the manual delineations and was overall better than those from other competing methods. In conclusion, we developed an automatic WMH quantification framework for multiple application scenarios, exhibiting a promising future in clinical practice.

Altered Surface Area Covariance in the Mentalizing Network in Schizophrenia: Insight Into Theory of Mind Processing

BACKGROUND

Theory of mind (ToM), the cognitive capacity to attribute mental states to self and others, is robustly affected in schizophrenia. The neural substrates of ToM impairment have been largely studied with functional imaging, but little is known about structural abnormalities. We compared structural covariance (between-subjects correlations of brain regional measures) of magnetic resonance imaging-based cortical surface area between patients with schizophrenia and healthy control subjects and between schizophrenia subgroups based on the patients' ToM ability to examine ToM-specific effects on structural covariance in schizophrenia.

METHODS

T1-weighted structural images were acquired on a 3T magnetic resonance imaging scanner, and ToM was assessed with the Hinting Task for 104 patients with schizophrenia and 69 healthy control subjects. The sum of surface area was computed for 12 regions of interest selected and compared between groups to examine structural covariance within the often reported mentalizing network: rostral and caudal middle frontal gyrus, inferior parietal lobule, precuneus, and middle and superior temporal gyrus. High and low ToM groups were defined using a median split on the Hinting Task.

RESULTS

Cortical surface contraction was observed in the schizophrenia group, predominantly in temporoparietal regions. Patients with schizophrenia also exhibited significantly stronger covariance between the right rostral middle frontal gyrus and the right superior temporal gyrus than control subjects (r = 4.015; p < .001). Direct comparisons between high and low ToM subgroups revealed stronger contralateral frontotemporal covariances in the low ToM group.

CONCLUSIONS

Our results provide evidence for structural changes underlying ToM impairments in schizophrenia that need to be confirmed to develop new therapeutic perspectives.

Sex-specific associations in verbal memory brain circuitry in early psychosis

Hippocampal circuitry and related cortical connections are altered in first episode psychosis (FEP) and are associated with verbal memory deficits, as well as positive and negative symptoms. There are robust sex differences in the clinical presentation of psychosis, including poorer verbal memory in male patients. Consideration of sex differences in hippocampal-cortical circuitry and their associations with different behavioral dimensions may be useful for understanding the underlying pathophysiology of verbal memory deficits and related symptomatology in psychosis. Here, we use a data-driven approach to simultaneously capture the complex links between sex, verbal memory, symptoms, and cortical-hippocampal brain metrics in FEP. Structural magnetic resonance imaging and behavioral data were acquired from 100 FEP patients (75 males, 25 females) and 87 controls (55 males, 32 females). Multivariate brain-behavior associations were examined in FEP using partial least squares to map sociodemographic, verbal memory, and clinical data onto brain morphometry. The analysis identified two sex-dependent patterns of verbal memory, symptoms, and brain structure. In male patients, verbal memory deficits and core psychotic symptoms were associated with both increased and decreased frontal and temporal cortical thickness and reductions in CA2/3 hippocampal subfield and fornix volumes. In female patients, fewer negative/depressive symptoms were associated with a more attenuated cortical thickness pattern and more diffuse reductions in hippocampal white matter regions. Taken together, the results contribute towards better understanding the underlying pathophysiology of psychosis by highlighting the unique contribution of specific hippocampal subfields and surrounding white matter and their connections with broader cortical networks in a sex-dependent manner.

Hippocampal Metastasis Rate Based on Non-Small Lung Cancer TNM Stage and Molecular Markers

Hippocampal-avoidance whole-brain radiation therapy (HA-WBRT) is justified because of low hippocampal brain metastases (BM) rate and its prevention of cognitive decline. However, we hypothesize that the risk of developing BM in the hippocampal-avoidance region (HAR) may differ depending on the lung-cancer stage and molecular status. We retrospectively reviewed 123 patients with non-small cell lung cancer (NSCLC) at the initial diagnosis of BM. The number of BMs within the HAR (5 mm expansion) was counted. The cohort was divided into patients with and without BMs in the HAR, and their clinical variables, TNM stage, and epidermal growth factor receptor (EGFR) status were compared. The most influential variable predicting BMs in the HAR was determined using multi-variable logistic regression, classification and regression tree (CART) analyses, and gradient boosting method (GBM). The feasibility of HAR expansion was tested using generalized estimating equation marginal model. Patients with BMs in the HAR were more frequently non-smokers, and more likely to have extra-cranial metastases and EGFR mutations (p<0.05). Multi-variable analysis revealed that extra-cranial metastases were independently associated with the presence of BM in the HAR (odds ratio=8.75, p=0.04). CART analysis and GBM revealed that the existence of extra-cranial metastasis was the most influential variable predicting BM occurrence in the HAR (variable importance: 23% and relative influence: 37.38). The estmated BM incidence of patients without extra-cranial metastases in th extended HAR (7.5-mm and 10-mm expansion) did not differ significantly from that in the conventional HAR. In conclusion, NSCLC patients with extra-cranial metastases were more likely to have BMs in the HAR than those without extra-cranial metastases.

Altered amygdala shape trajectories and emotion recognition in youth at familial high risk of schizophrenia who develop psychosis

Relatives of individuals with schizophrenia have a higher risk of developing the illness compared to the general population. Thus, youth at familial high risk (FHR) offer a unique opportunity to identify neuroimaging-based endophenotypes of psychosis. Previous studies have identified lower amygdalo-hippocampal volume in FHR, as well as lower verbal memory and emotion recognition. However, whether these phenotypes increase the risk of transition to psychosis remains unclear. To determine if individuals who develop psychosis have abnormal neurodevelopmental trajectories of the amygdala and hippocampus, we investigated longitudinal changes of these structures in a unique cohort of 82 youth FHR and 56 healthy controls during a 3-year period. Ten individuals from the FHR group converted to psychosis. Longitudinal changes were compared using linear mixed-effects models. Group differences in verbal memory and emotion recognition performance at baseline were also analyzed. Surface-based morphometry measures revealed variation in amygdalar shape (concave shape of the right dorsomedial region) in those who converted to psychosis. Significantly lower emotion recognition performance at baseline was observed in converters. Percent trial-to-trial transfer on the verbal learning task was also significantly impaired in FHR, independently of the conversion status. Our results identify abnormal shape development trajectories in the dorsomedial amygdala and lower emotion recognition abilities as phenotypes of transition to psychosis. Our findings illustrate potential markers for early identification of psychosis, aiding prevention efforts in youth at risk of schizophrenia.

DARQ: Deep learning of quality control for stereotaxic registration of human brain MRI to the T1w MNI-ICBM 152 template

Linear registration to stereotaxic space is a common first step in many automated image-processing tools for analysis of human brain MRI scans. This step is crucial for the success of the subsequent image-processing steps. Several well-established algorithms are commonly used in the field of neuroimaging for this task, but none have a 100% success rate. Manual assessment of the registration is commonly used as part of quality control. To reduce the burden of this time-consuming step, we propose Deep Automated Registration Qc (DARQ), a fully automatic quality control method based on deep learning that can replace the human rater and accurately perform quality control assessment for stereotaxic registration of T1w brain scans. In a recently published study from our group comparing linear registration methods, we used a database of 9325 MRI scans and 64476 registrations from several publicly available datasets and applied seven linear registration tools to them. In this study, the resulting images that were assessed and labeled by a human rater are used to train a deep neural network to detect cases when registration failed. We further validated the results on an independent dataset of patients with multiple sclerosis, with manual QC labels available (n=1200). In terms of agreement with a manual rater, our automated QC method was able to achieve 89% accuracy and 85% true negative rate (equivalently 15% false positive rate) in detecting scans that should pass quality control in a balanced cross-validation experiments, and 96.1% accuracy and 95.5% true negative rate (or 4.5% FPR) when evaluated in a balanced independent sample, similar to manual QC rater (test-retest accuracy of 93%). The results show that DARQ is robust, fast, accurate, and generalizable in detecting failure in linear stereotaxic registrations and can substantially reduce QC time (by a factor of 20 or more) when processing large datasets.

Quantitative Structural Brain Magnetic Resonance Imaging Analyses: Methodological Overview and Application to Rett Syndrome

Congenital genetic disorders often present with neurological manifestations such as neurodevelopmental disorders, motor developmental retardation, epilepsy, and involuntary movement. Through qualitative morphometric evaluation of neuroimaging studies, remarkable structural abnormalities, such as lissencephaly, polymicrogyria, white matter lesions, and cortical tubers, have been identified in these disorders, while no structural abnormalities were identified in clinical settings in a large population. Recent advances in data analysis programs have led to significant progress in the quantitative analysis of anatomical structural magnetic resonance imaging (MRI) and diffusion-weighted MRI tractography, and these approaches have been used to investigate psychological and congenital genetic disorders. Evaluation of morphometric brain characteristics may contribute to the identification of neuroimaging biomarkers for early diagnosis and response evaluation in patients with congenital genetic diseases. This mini-review focuses on the methodologies and attempts employed to study Rett syndrome using quantitative structural brain MRI analyses, including voxel- and surface-based morphometry and diffusion-weighted MRI tractography. The mini-review aims to deepen our understanding of how neuroimaging studies are used to examine congenital genetic disorders.