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.

The structural-functional-connectivity coupling of the aging brain.

Aging primarily affects memory and executive functions, a relationship that may be underpinned by the fact that almost all adults over 60 years old develop small vessel disease (SVD). The fact that a wide range of neuropathologies could only explain up to 43% of the variation in age-related cognitive impairment suggests that other factors, such as cognitive reserve, may play a role in the brain's resilience against aging-related cognitive decline. This study aims to examine the relationship between structural-functional-connectivity coupling (SFC), and aging, cognitive abilities and reserve, and SVD-related neuropathologies using a cohort of n = 176 healthy elders from the Harvard Aging Brain Study. The SFC is a recently proposed biomarker that reflects the extent to which anatomical brain connections can predict coordinated neural activity. After controlling for the effect of age, sex, and years of education, global SFC, as well as the intra-network SFC of the dorsolateral somatomotor and dorsal attention networks, and the inter-network SFC between dorsolateral somatomotor and frontoparietal networks decreased with age. The global SFC decreased with total cognitive score. There were significant interaction effects between years of education versus white matter hyperintensities and between years of education versus cerebral microbleeds on inter-network SFC. Enlarged perivascular space in basal ganglia was associated with higher inter-network SFC. Our results suggest that cognitive ability is associated with brain coupling at the global level and cognitive reserve with brain coupling at the inter-functional-brain-cluster level with interaction effect from white matter hyperintensities and cerebral microbleed in a cohort of healthy elderlies.

Deep-Learning-Based MRI Microbleeds Detection for Cerebral Small Vessel Disease on Quantitative Susceptibility Mapping.

BACKGROUND: Cerebral microbleeds (CMB) are indicators of severe cerebral small vessel disease (CSVD) that can be identified through hemosiderin-sensitive sequences in MRI. Specifically, quantitative susceptibility mapping (QSM) and deep learning were applied to detect CMBs in MRI. PURPOSE: To automatically detect CMB on QSM, we proposed a two-stage deep learning pipeline. STUDY TYPE: Retrospective. SUBJECTS: A total number of 1843 CMBs from 393 patients (69 ± 12) with cerebral small vessel disease were included in this study. Seventy-eight subjects (70 ± 13) were used as external testing. FIELD STRENGTH/SEQUENCE: 3 T/QSM. ASSESSMENT: The proposed pipeline consisted of two stages. In stage I, 2.5D fast radial symmetry transform (FRST) algorithm along with a one-layer convolutional network was used to identify CMB candidate regions in QSM images. In stage II, the V-Net was utilized to reduce false positives. The V-Net was trained using CMB and non CMB labels, which allowed for high-level feature extraction and differentiation between CMBs and CMB mimics like vessels. The location of CMB was assessed according to the microbleeds anatomical rating scale (MARS) system. STATISTICAL TESTS: The sensitivity and positive predicative value (PPV) were reported to evaluate the performance of the model. The number of false positive per subject was presented. RESULTS: Our pipeline demonstrated high sensitivities of up to 94.9% at stage I and 93.5% at stage II. The overall sensitivity was 88.9%, and the false positive rate per subject was 2.87. With respect to MARS, sensitivities of above 85% were observed for nine different brain regions. DATA CONCLUSION: We have presented a deep learning pipeline for detecting CMB in the CSVD cohort, along with a semi-automated MARS scoring system using the proposed method. Our results demonstrated the successful application of deep learning for CMB detection on QSM and outperformed previous handcrafted methods. LEVEL OF EVIDENCE: 2 TECHNICAL EFFICACY: Stage 2.

Patterns of care and survival of Chinese glioblastoma patients in the temozolomide era: a Hong Kong population-level analysis over a 14-year period.

Background: The aim of this study is to address the paucity of epidemiological data regarding the characteristics, treatment patterns and survival outcomes of Chinese glioblastoma patients. Methods: This was a population-level study of Hong Kong adult (>18 years) Chinese patients with newly diagnosed histologically confirmed glioblastoma between 2006 and 2019. The age standardized incidence rate (ASIR), patient-, tumor- treatment-related characteristics, overall survival (OS) as well as its predictors were determined. Results: One thousand and ten patients with a median follow-up of 10.0 months were reviewed. The ASIR of glioblastoma was 1.0 per 100 000 population with no significant change during the study period. The mean age was 57 + 14 years. The median OS was 10.6 months (IQR: 5.2-18.4). Independent predictors for survival were: Karnofsky performance score >80 (adjusted OR: 0.8; 95% CI: 0.6-0.9), IDH-1 mutant (aOR: 0.7; 95% CI: 0.5-0.9) or MGMT methylated (aOR: 0.7; 95% CI: 0.5-0.8) glioblastomas, gross total resection (aOR: 0.8; 95% CI: 0.5-0.8) and temozolomide chemoradiotherapy (aOR 0.4; 95% CI: 0.3-0.6). Despite the significant increased administration of temozolomide chemoradiotherapy from 39% (127/326) of patients in 2006-2010 to 63% (227/356) in 2015-2019 (P-value < .001), median OS did not improve (2006-2010: 10.3 months vs 2015-2019: 11.8 months) (OR: 1.1; 95% CI: 0.9-1.3). Conclusions: The incidence of glioblastoma in the Chinese general population is low. We charted the development of neuro-oncological care of glioblastoma patients in Hong Kong during the temozolomide era. Although there was an increased adoption of temozolomide chemoradiotherapy, a corresponding improvement in survival was not observed.

Level of Amyloid-ß (Aß) Binding Leading to Differential Effects on Resting State Functional Connectivity in Major Brain Networks.

INTRODUCTION: Amyloid-ß protein (Aß) is one of the biomarkers for Alzheimer's disease (AD). The recent application of interhemispheric functional connectivity (IFC) in resting-state fMRI has been used as a non-invasive diagnostic tool for early dementia. In this study, we focused on the level of Aß accumulated and its effects on the major functional networks, including default mode network (DMN), central executive network (CEN), salience network (SN), self-referential network (SRN) and sensory motor network (SMN). METHODS: 58 participants (27 Hi Aß (HiAmy) and 31 low Aß (LowAmy)) and 25 healthy controls (HC) were recruited. [18F]flutemetamol PET/CT was performed for diseased groups, and MRI scanning was done for all participants. Voxel-by-voxel correlation analysis was done for both groups in all networks. RESULTS: In HiAmy, IFC was reduced in all networks except SN. A negative correlation in DMN, CEN, SRN and SMN suggests high Aß related to IFC reduction; However, a positive correlation in SN suggests high Aß related to an increase in IFC. In LowAmy, IFC increased in CEN, SMN, SN and SRN. Positive correlation in all major brain networks. CONCLUSION: The level of Aß accumulated demonstrated differential effects on IFC in various brain networks. As the treatment to reduce Aß plaque deposition is available in the market, it may be an option for the HiAmy group to improve their IFC in major brain networks.

Striatal dopamine synthesis capacity and its association with negative symptoms upon resolution of positive symptoms in first-episode schizophrenia and delusional disorder.

RATIONALE: How striatal dopamine synthesis capacity (DSC) contributes to the pathogenesis of negative symptoms in first-episode schizophrenia (SZ) and delusional disorder (DD) has seldom been explored. As negative symptoms during active psychotic episodes can be complicated by secondary influences, such as positive symptoms, longitudinal investigations may help to clarify the relationship between striatal DSC and negative symptoms and differentiate between primary and secondary negative symptoms. OBJECTIVE: A longitudinal study was conducted to examine whether baseline striatal DSC would be related to negative symptoms at 3 months in first-episode SZ and DD patients. METHODS: Twenty-three first-episode age- and gender-matched patients (11 DD and 12 SZ) were consecutively recruited through an early intervention service for psychosis in Hong Kong. Among them, 19 (82.6%) patients (9 DD and 10 SZ) were followed up at 3 months. All patients received an 18F-DOPA PET/MR scan at baseline. RESULTS: Baseline striatal DSC (Kocc;30-60) was inversely associated with negative symptoms at 3 months in first-episode SZ patients (rs = - 0.80, p = 0.010). This association remained in SZ patients even when controlling for baseline negative, positive, and depressive symptoms, as well as cumulative antipsychotic dosage (ß = - 0.69, p = 0.012). Such associations were not observed in first-episode DD patients. Meanwhile, the severity of negative symptoms at 3 months was associated with more positive symptoms in DD patients (rs = 0.74, p = 0.010) and not in SZ patients. CONCLUSIONS: These findings highlight the role of striatal DSC in negative symptoms upon resolution of active psychotic episodes among first-episode SZ patients. Baseline striatal dopamine activity may inform future symptom expression with important treatment implications.

Mapping brain structural differences and neuroreceptor correlates in Parkinson's disease visual hallucinations.

Parkinson's psychosis (PDP) describes a spectrum of symptoms that may arise in Parkinson's disease (PD) including visual hallucinations (VH). Imaging studies investigating the neural correlates of PDP have been inconsistent in their findings, due to differences in study design and limitations of scale. Here we use empirical Bayes harmonisation to pool together structural imaging data from multiple research groups into a large-scale mega-analysis, allowing us to identify cortical regions and networks involved in VH and their relation to receptor binding. Differences of morphometrics analysed show a wider cortical involvement underlying VH than previously recognised, including primary visual cortex and surrounding regions, and the hippocampus, independent of its role in cognitive decline. Structural covariance analyses point to the involvement of the attentional control networks in PD-VH, while associations with receptor density maps suggest neurotransmitter loss may be linked to the cortical changes.

A scoping review of resting-state brain functional alterations in Type 2 diabetes.

Resting-state functional magnetic resonance imaging (rs-fMRI) has been actively used in the last decade to investigate brain functional connectivity alterations in Type 2 Diabetes Mellitus (T2DM) to understand the neuropathophysiology of T2DM in cognitive degeneration. Given the emergence of new analysis techniques, this scoping review aims to map the rs-fMRI analysis techniques that have been applied in the literature and reports the latest rs-fMRI findings that have not been covered in previous reviews. Graph theory, the contemporary rs-fMRI analysis, has been used to demonstrate altered brain topological organisations in people with T2DM, which included altered degree centrality, functional connectivity strength, the small-world architecture and network-based statistics. These alterations were correlated with T2DM patients' cognitive performances. Graph theory also contributes to identify unbiased seeds for seed-based analysis. The expanding rs-fMRI analytical approaches continue to provide new evidence that helps to understand the mechanisms of T2DM-related cognitive degeneration.

Structural network alterations and their association with neurological soft signs in schizophrenia: Evidence from clinical patients and unaffected siblings.

BACKGROUND: Grey matter abnormalities and neurological soft signs (NSS) have been found in schizophrenia patients and their unaffected relatives. Evidence suggested that NSS are associated with grey matter morphometrical alterations in multiple regions in schizophrenia. However, the association between NSS and structural abnormalities at network level remains largely unexplored, especially in the schizophrenia and unaffected siblings. METHOD: We used source-based morphometry (SBM) to examine the association of structural brain network characteristics with NSS in 62 schizophrenia patients, 25 unaffected siblings, and 60 healthy controls. RESULTS: Two components, namely the IC-5 (superior temporal gyrus, inferior frontal gyrus and insula network) and the IC-10 (parahippocampal gyrus, fusiform, thalamus and insula network) showed significant grey matter reductions in schizophrenia patients compared to healthy controls and unaffected siblings. Further association analysis demonstrated separate NSS-related grey matter covarying patterns in schizophrenia, unaffected siblings and healthy controls. Specifically, NSS were negatively associated with IC-1 (hippocampus, caudate and thalamus network) and IC-5 in schizophrenia, but with IC-3 (caudate, superior and middle frontal cortices network) in unaffected siblings and with IC-5 in healthy controls. CONCLUSION: Our results confirmed the key cortical and subcortical network abnormalities and NSS-related grey matter covarying patterns in the schizophrenia and unaffected siblings. Our findings suggest that brain regions implicating genetic liability to schizophrenia are partly separated from brain regions implicating neural abnormalities.

A low-cost and shielding-free ultra-low-field brain MRI scanner.

Magnetic resonance imaging is a key diagnostic tool in modern healthcare, yet it can be cost-prohibitive given the high installation, maintenance and operation costs of the machinery. There are approximately seven scanners per million inhabitants and over 90% are concentrated in high-income countries. We describe an ultra-low-field brain MRI scanner that operates using a standard AC power outlet and is low cost to build. Using a permanent 0.055 Tesla Samarium-cobalt magnet and deep learning for cancellation of electromagnetic interference, it requires neither magnetic nor radiofrequency shielding cages. The scanner is compact, mobile, and acoustically quiet during scanning. We implement four standard clinical neuroimaging protocols (T1- and T2-weighted, fluid-attenuated inversion recovery like, and diffusion-weighted imaging) on this system, and demonstrate preliminary feasibility in diagnosing brain tumor and stroke. Such technology has the potential to meet clinical needs at point of care or in low and middle income countries.

Relationship between Amyloid-ß Deposition and the Coupling between Structural and Functional Brain Networks in Patients with Mild Cognitive Impairment and Alzheimer's Disease.

Previous studies have demonstrated that the accumulation of amyloid-ß (Aß) pathologies has distinctive stage-specific effects on the structural and functional brain networks along the Alzheimer's disease (AD) continuum. A more comprehensive account of both types of brain network may provide a better characterization of the stage-specific effects of Aß pathologies. A potential candidate for this joint characterization is the coupling between the structural and functional brain networks (SC-FC coupling). We therefore investigated the effect of Aß accumulation on global SC-FC coupling in patients with mild cognitive impairment (MCI), AD, and healthy controls. Patients with MCI were dichotomized according to their level of Aß pathology seen in 18F-flutemetamol PET-CT scans-namely, Aß-negative and Aß-positive. Our results show that there was no difference in global SC-FC coupling between different cohorts. During the prodromal AD stage, there was a significant negative correlation between the level of Aß pathology and the global SC-FC coupling of MCI patients with positive Aß, but no significant correlation for MCI patients with negative Aß. During the AD dementia stage, the correlation between Aß pathology and global SC-FC coupling in patients with AD was positive. Our results suggest that Aß pathology has distinctive stage-specific effects on global coupling between the structural and functional brain networks along the AD continuum.

Relayed nuclear Overhauser effect weighted (rNOEw) imaging identifies multiple sclerosis.

Multiple sclerosis (MS) is an autoimmune disease of the central nervous system in which the immune system attacks the myelin and axons, consequently leading to demyelination and axonal injury. Magnetic resonance imaging (MRI) plays a pivotal role in the diagnosis of MS, and currently various types of MRI techniques have been used to detect the pathology of MS based on unique mechanisms. In this study, we applied the relayed nuclear Overhauser effect weighted (rNOEw) imaging to study human MS at clinical 3T. Three groups of subjects, including 20 normal control (NC) subjects, 14 neuromyelitis optica spectrum disorders (NMOSD) patients and 21 MS patients, were examined at a clinical 3T MRI scanner. Whole-brain rNOEw images of each subject were obtained by acquiring a control and a labeled image within four minutes. Significantly lower brain rNOEw contrast was detected in MS group compared to NC (P = 0.008) and NMOSD (P = 0.014) groups, while no significant difference was found between NC and NMOSD groups (P = 0.939). The lower rNOEw contrast of MS group compared to NC/NMOSD group was significant in white matter (P = 0.041/0.021), gray matter (P = 0.004/0.020) and brain parenchyma (P = 0.015/0.021). Moreover, MS lesions showed higher number and larger size but lower rNOEw contrast than NMOSD lesions (P = 0.002). Our proposed rNOEw imaging scheme has potential to serve as a new method for assisting MS diagnosis. Importantly, it may be used to identify MS from NMOSD.

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 (HK­MoCA) 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.

Small-World Networks and Their Relationship With Hippocampal Glutamine/Glutamate Concentration in Healthy Adults With Varying Genetic Risk for Alzheimer's Disease.

BACKGROUND: Apolipoprotein E ɛ4 allele (ApoE4) is the most common gene polymorphism related to Alzheimer's disease (AD). Impaired synaptic dysfunction occurs in ApoE4 carriers before any clinical symptoms. It remains unknown whether ApoE4 status affects the hippocampal neuromodulation, which further influences brain network topology. PURPOSE: To study the relationship of regional and global network properties by using graph theory analysis and glutamatergic (Glx) neuromodulation in the ApoE isoforms. STUDY TYPE: Prospective. SUBJECTS: Eighty-four cognitively normal adults (26 ApoE4 and 58 non-ApoE4 carriers). FIELD STRENGTH/SEQUENCE: Gradient-echo echo-planar and point resolved spectroscopy sequence at 3 T. ASSESSMENT: Glx concentration in bilateral hippocampi were processed with jMRUI (4.0), and graph theory metrics (global: γ, λ, small-worldness in whole brain; regional: nodal clustering coefficient (Ci ) and nodal characteristic path length (Li )) in top 20% highly connected hubs of subgroups (low-risk: non-ApoE4; high-risk: APOE4) were calculated and compared. STATISTICAL TESTS: Two-sample t test was used to compare metrics between subgroups. Correlations between regional properties and Glx by Pearson's partial correlation with false discovery rate correction. RESULTS: Significant differences (P < 0.05) in Ci between subgroups were found in hubs of left inferior frontal, bilateral inferior temporal, and bilateral precentral gyri, right parahippocampus, and bilateral precuneus. In addition, there was a significant correlation between Glx in the left hippocampus and Ci in inferior frontal gyrus (r = -0.537, P = 0.024), right inferior temporal (r = -0.478, P = 0.043), right parahippocampus (r = -0.629, P = 0.016), left precentral (r = -0.581, P = 0.022), right precentral (r = -0.651, P = 0.003), left precuneus (r = -0.545, P = 0.024), and right precuneus (r = -0.567, P = 0.022); and Li in left precuneus (r = 0.575, P = 0.032) and right precuneus (r = 0.586, P = 0.032) in the high-risk group, but not in the low-risk group. DATA CONCLUSION: Our results suggested that healthy ApoE4 carriers exhibit poorer local interconnectivity. Moreover, the close relationship between glutamate and small-world network properties in ApoE4 carriers might reflect a compensatory response to the impaired network efficiency. EVIDENCE LEVEL: 2 TECHNICAL EFFICACY: Stage 3.

Relation between rich-club organization versus brain functions and functional recovery after acute ischemic stroke.

Studies have shown the brain's rich-club organization may underpin brain function and be associated with various brain disorders. In this study, we aimed to investigate the relation between poststroke brain functions and functional recovery versus the rich-club organization of the structural brain network of patients after first-time acute ischemic stroke. A cohort of 16 acute ischemic stroke patients (11 males) was recruited. Structural brain networks were measured using diffusion tensor imaging within 1 week and at 1, 3 and 6 months after stroke. Motor impairment was assessed using the Upper-Extremity Fugl-Meyer motor scale and activities of daily living using the Barthel Index at the same time points as MRI. The rich-club regions that were stable over the course of stroke recovery included the bilateral dorsolateral superior frontal gyri, right supplementary motor area, and left median cingulate and paracingulate gyri. The network properties that correlated with poststroke brain functions were mainly the ratio between communication cost ratio and density ratio of rich-club, feeder and local connections. The recovery of both motor functions and activities of daily living were correlated with higher normalized rich club coefficients and a shorter length of local connections within a week after stroke. The communication cost ratio of feeder connections, the length of rich-club and local connections, and normalized rich club coefficients were found to be potential prognostic indicators of stroke recovery. Our results provide additional support to the notion that different types of network connections play different roles in brain functions as well as functional recovery.

Neurological Soft Signs Are Associated With Altered Cerebellar-Cerebral Functional Connectivity in Schizophrenia.

Cerebellar dysfunction is associated with neurological soft signs (NSS), which is a promising endophenotype for schizophrenia spectrum disorders. However, the relationship between cerebellar-cerebral resting-state functional connectivity (rsFC) and NSS is largely unexplored. Moreover, both NSS and cerebellar-cerebral rsFC have been found to be correlated with negative symptoms of schizophrenia. Here, we investigated the correlations between NSS and cerebellar-cerebral rsFC, explored their relationship with negative symptoms in a main dataset, and validated the significant findings in a replication dataset. Both datasets comprised schizophrenia patients and healthy controls. In schizophrenia patients, we found positive correlations between NSS and rsFC of the cerebellum with the inferior frontal gyrus and the precuneus, and negative correlations between NSS and rsFC of the cerebellum with the inferior temporal gyrus. In healthy controls, NSS scores were positively correlated with rsFC of the cerebellum with the superior frontal gyrus and negatively correlated with rsFC between the cerebellum and the middle occipital gyrus. Cerebellar-prefrontal rsFC was also positively correlated with negative symptoms in schizophrenia patients. These findings were validated in the replication dataset. Our results suggest that the uncoupling of rsFC between the cerebellum and the cerebral cortex may underlie the expression of NSS in schizophrenia. NSS-related cerebellar-prefrontal rsFC may be a potential neural pathway for possible neural modulation to alleviate negative symptoms.

Cerebellar hypoactivation is associated with impaired sensory integration in schizophrenia.

To clarify the involvement of the cerebellum in impaired sensory integration in patients with schizophrenia, 52 first-episode patients with schizophrenia and 52 age- and sex-matched healthy controls underwent a verified sensory integration imaging task to examine the whole-brain dysfunction underlying impaired sensory integration. The familiality of cerebellar activation when integrating sensory stimuli was investigated in 25 siblings of the patients with schizophrenia, while the heritability of cerebellar activation was estimated in 56 monozygotic twins and 56 dizygotic twins. In addition, the functional connectivity between the cerebellum and the remaining regions of the whole brain was explored with psychophysiological interaction analysis. Relative to healthy controls, patients with schizophrenia showed reduced cerebellar activation when performing the sensory integration task in the whole-brain analysis. This reduced cerebellar activation was also found in the siblings of patients with schizophrenia, but to a lesser extent compared with schizophrenia patients. Cerebellar activation during sensory integration was also found to be significantly heritable. Furthermore, dysconnectivity within the cerebellum was found in patients with schizophrenia when integrating auditory and visual stimuli. These findings highlight the role of cerebellar dysfunction in the pathophysiology of schizophrenia symptoms and its potential role as an endophenotype of schizophrenia spectrum disorders. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

Impaired cerebral blood flow in type 2 diabetes mellitus - A comparative study with subjective cognitive decline, vascular dementia and Alzheimer's disease subjects.

The link between non-demented type 2 diabetes mellitus (T2DM) and different types of cognitive impairment is controversial. By controlling for co-morbidities such as cerebral macrovascular and microvascular changes, cerebral atrophy, amyloid burden, hypertension or hyperlipidemia, the current study investigated the cerebral blood flow of T2DM individuals as compared to cognitively impaired subjects recruited from a memory clinic. 15 healthy control (71.8 ± 6.1 years), 18 T2DM (62.5 ± 3.7 years), as well as 8 Subjective Cognitive Decline (69.5 ± 7.5 years), 12 Vascular Dementia (79.3 ± 4.2 years) and 17 Alzheimer's Disease (75.1 ± 8.2 years) underwent multi-parametric MRI brain scanning. Subjects with T2DM and from the memory clinic also had 18-F Flutametamol PET-CT scanning to look for any amyloid burden. Pseudocontinuous Arterial Spin Labeling (PCASL), MR Angiography Head, 3D FLAIR and 3D T1-weighted sequences were used to quantify cerebral blood flow, cerebrovascular changes, white matter hyperintensities and brain atrophy respectively. Vascular risk factors were retrieved from the medical records. The 37 subjects from memory clinic were classified into subjective cognitive decline (SCD), vascular dementia (VD) and Alzheimer's disease (AD) subgroups by a multi-disciplinary panel consisting of a neuroradiologist, and 2 geriatricians. Absolute cortical CBF in our cohort of T2DM, SCD, VD and AD was significantly decreased (p < 0.01) as compared to healthy controls (HC) in both whole brain and eight paired brain regions, after age, normalized grey matter volume and gender adjustment and Bonferroni correction. Subgroup analysis between T2DM, SCD, VD, and AD revealed that CBF of T2DM was not significantly different from AD, VD or SCD. By controlling for co-morbidities, impaired cortical CBF in T2DM was not related to microangiopathy or amyloid deposition, but to the interaction of triple risk factors (such as diabetes mellitus, hypertension, and hyperlipidemia). There was statistically significant negative correlation (p ≤ 0.05) between adjusted CBF and HbA1c in all brain regions of T2DM and HC (with partial correlation ranging from -0.30 to -0.46). Taken together, altered cerebral blood flow in T2DM might be related to disruption of cerebrovascular autoregulation related to vascular risk factors, and such oligemia occurred before clinical manifestation due to altered glycemic control.

Predicting dementia diagnosis from cognitive footprints in electronic health records: a case-control study protocol.

INTRODUCTION: Dementia is a group of disabling disorders that can be devastating for persons living with it and for their families. Data-informed decision-making strategies to identify individuals at high risk of dementia are essential to facilitate large-scale prevention and early intervention. This population-based case-control study aims to develop and validate a clinical algorithm for predicting dementia diagnosis, based on the cognitive footprint in personal and medical history. METHODS AND ANALYSIS: We will use territory-wide electronic health records from the Clinical Data Analysis and Reporting System (CDARS) in Hong Kong between 1 January 2001 and 31 December 2018. All individuals who were at least 65 years old by the end of 2018 will be identified from CDARS. A random sample of control individuals who did not receive any diagnosis of dementia will be matched with those who did receive such a diagnosis by age, gender and index date with 1:1 ratio. Exposure to potential protective/risk factors will be included in both conventional logistic regression and machine-learning models. Established risk factors of interest will include diabetes mellitus, midlife hypertension, midlife obesity, depression, head injuries and low education. Exploratory risk factors will include vascular disease, infectious disease and medication. The prediction accuracy of several state-of-the-art machine-learning algorithms will be compared. ETHICS AND DISSEMINATION: This study was approved by Institutional Review Board of The University of Hong Kong/Hospital Authority Hong Kong West Cluster (UW 18-225). Patients' records are anonymised to protect privacy. Study results will be disseminated through peer-reviewed publications. Codes of the resulted dementia risk prediction algorithm will be made publicly available at the website of the Tools to Inform Policy: Chinese Communities' Action in Response to Dementia project (https://www.tip-card.hku.hk/).